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Alghassab MA, Hatata AY, Sokrana AH, El-Saadawi MM. Optimal location of PMUs for full observability of power system using coronavirus herd immunity optimizer. Heliyon 2024; 10:e31832. [PMID: 38841515 PMCID: PMC11152686 DOI: 10.1016/j.heliyon.2024.e31832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 05/03/2024] [Accepted: 05/22/2024] [Indexed: 06/07/2024] Open
Abstract
Phasor measurement units (PMU) are currently considered as an essential step toward the future smart grid due to their capability in increasing the power system's situation awareness. Due to their high costs and limited resources, optimal placement of PMUs (OPP) is an important challenge to compute the minimum number of PMUs and their optimal distribution in the power systems for achieving full monitoring. The coronavirus herd immunity optimizer (CHIO) is a novel optimization algorithm that emulates the flock immunity strategies for the elimination of the coronavirus pandemic. In this research, the CHIO is adapted for the OPP problem for full fault observability. The proposed algorithm is implemented on power systems considering the zero injection bus impacts. A program is created in MATLAB® environment to implement the proposed algorithm. The algorithm is applied to different test systems including; IEEE 9-bus, 14-bus, 30-bus, 118-bus, 300-bus, New England 39-bus and Polish 2383-bus. The proposed CHIO-based OPP is compared to some exact and metaheuristic-based OPP techniques. Compared to these techniques, the promising results have proved the effectiveness and robustness of the proposed CHIO to solve the OPP problem for full fault observability.
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Affiliation(s)
- Mohammed A. Alghassab
- Department of Electrical Engineering, College of Engineering, Shaqra University, Al-Dawadmi, Riyadh, 11911, Saudi Arabia
| | - Ahmed Y. Hatata
- Department of Electrical Engineering, College of Engineering, Shaqra University, Al-Dawadmi, Riyadh, 11911, Saudi Arabia
- Dept. of Electrical Engineering, Faculty of Engineering, Mansoura University, Egypt
| | - Ahmed H. Sokrana
- Egyptian Electricity Transmission Company “EETC”, Delta Zone, Protection Sector, Egypt
| | - Magdi M. El-Saadawi
- Dept. of Electrical Engineering, Faculty of Engineering, Mansoura University, Egypt
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2
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Yao T, Chen X, Wang H, Gao C, Chen J, Yi D, Wei Z, Yao N, Li Y, Yi D, Wu Y. Deep evolutionary fusion neural network: a new prediction standard for infectious disease incidence rates. BMC Bioinformatics 2024; 25:38. [PMID: 38262917 PMCID: PMC10804580 DOI: 10.1186/s12859-023-05621-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 12/15/2023] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Previously, many methods have been used to predict the incidence trends of infectious diseases. There are numerous methods for predicting the incidence trends of infectious diseases, and they have exhibited varying degrees of success. However, there are a lack of prediction benchmarks that integrate linear and nonlinear methods and effectively use internet data. The aim of this paper is to develop a prediction model of the incidence rate of infectious diseases that integrates multiple methods and multisource data, realizing ground-breaking research. RESULTS The infectious disease dataset is from an official release and includes four national and three regional datasets. The Baidu index platform provides internet data. We choose a single model (seasonal autoregressive integrated moving average (SARIMA), nonlinear autoregressive neural network (NAR), and long short-term memory (LSTM)) and a deep evolutionary fusion neural network (DEFNN). The DEFNN is built using the idea of neural evolution and fusion, and the DEFNN + is built using multisource data. We compare the model accuracy on reference group data and validate the model generalizability on external data. (1) The loss of SA-LSTM in the reference group dataset is 0.4919, which is significantly better than that of other single models. (2) The loss values of SA-LSTM on the national and regional external datasets are 0.9666, 1.2437, 0.2472, 0.7239, 1.4026, and 0.6868. (3) When multisource indices are added to the national dataset, the loss of the DEFNN + increases to 0.4212, 0.8218, 1.0331, and 0.8575. CONCLUSIONS We propose an SA-LSTM optimization model with good accuracy and generalizability based on the concept of multiple methods and multiple data fusion. DEFNN enriches and supplements infectious disease prediction methodologies, can serve as a new benchmark for future infectious disease predictions and provides a reference for the prediction of the incidence rates of various infectious diseases.
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Affiliation(s)
- Tianhua Yao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Xicheng Chen
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Haojia Wang
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Chengcheng Gao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Jia Chen
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Dali Yi
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
- Department of Health Education, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Zeliang Wei
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Ning Yao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Yang Li
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Dong Yi
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
| | - Yazhou Wu
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
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3
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Zhou L, Zhao C, Liu N, Yao X, Cheng Z. Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023; 122:106157. [PMID: 36968247 PMCID: PMC10017389 DOI: 10.1016/j.engappai.2023.106157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 03/08/2023] [Accepted: 03/13/2023] [Indexed: 05/25/2023]
Abstract
Individuals in any country are badly impacted both economically and physically whenever an epidemic of infectious illnesses breaks out. A novel coronavirus strain was responsible for the outbreak of the coronavirus sickness in 2019. Corona Virus Disease 2019 (COVID-19) is the name that the World Health Organization (WHO) officially gave to the pneumonia that was caused by the novel coronavirus on February 11, 2020. The use of models that are informed by machine learning is currently a major focus of study in the field of improved forecasting. By displaying annual trends, forecasting models can be of use in performing impact assessments of potential outcomes. In this paper, proposed forecast models consisting of time series models such as long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), generalized regression unit (GRU), and dense-LSTM have been evaluated for time series prediction of confirmed cases, deaths, and recoveries in 12 major countries that have been affected by COVID-19. Tensorflow1.0 was used for programming. Indices known as mean absolute error (MAE), root means square error (RMSE), Median Absolute Error (MEDAE) and r2 score are utilized in the process of evaluating the performance of models. We presented various ways to time-series forecasting by making use of LSTM models (LSTM, BiLSTM), and we compared these proposed methods to other machine learning models to evaluate the performance of the models. Our study suggests that LSTM based models are among the most advanced models to forecast time series data.
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Affiliation(s)
- Luyu Zhou
- Department of Pharmacy, College of Biology, Hunan University, Changsha, Hunan 410082, China
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao 266021, China
| | - Chun Zhao
- Department of Pharmacy, College of Biology, Hunan University, Changsha, Hunan 410082, China
| | - Ning Liu
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao 266021, China
| | - Xingduo Yao
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao 266021, China
| | - Zewei Cheng
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao 266021, China
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4
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Azizi M, Baghalzadeh Shishehgarkhaneh M, Basiri M, Moehler RC. Squid Game Optimizer (SGO): a novel metaheuristic algorithm. Sci Rep 2023; 13:5373. [PMID: 37005455 PMCID: PMC10066950 DOI: 10.1038/s41598-023-32465-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 03/28/2023] [Indexed: 04/04/2023] Open
Abstract
In this paper, Squid Game Optimizer (SGO) is proposed as a novel metaheuristic algorithm inspired by the primary rules of a traditional Korean game. Squid game is a multiplayer game with two primary objectives: attackers aim to complete their goal while teams try to eliminate each other, and it is usually played on large, open fields with no set guidelines for size and dimensions. The playfield for this game is often shaped like a squid and, according to historical context, appears to be around half the size of a standard basketball court. The mathematical model of this algorithm is developed based on a population of solution candidates with a random initialization process in the first stage. The solution candidates are divided into two groups of offensive and defensive players while the offensive player goes among the defensive players to start a fight which is modeled through a random movement toward the defensive players. By considering the winning states of the players of both sides which is calculated based on the objective function, the position updating process is conducted and the new position vectors are produced. To evaluate the effectiveness of the proposed SGO algorithm, 25 unconstrained mathematical test functions with 100 dimensions are used, alongside six other commonly used metaheuristics for comparison. 100 independent optimization runs are conducted for both SGO and the other algorithms with a pre-determined stopping condition to ensure statistical significance of the results. Statistical metrics such as mean, standard deviation, and mean of required objective function evaluations are calculated. To provide a more comprehensive analysis, four prominent statistical tests including the Kolmogorov-Smirnov, Mann-Whitney, and Kruskal-Wallis tests are used. Meanwhile, the ability of the suggested SGOA is assessed through the cutting-edge real-world problems on the newest CEC like CEC 2020, while the SGO demonstrate outstanding performance in dealing with these complex optimization problems. The overall assessment of the SGO indicates that the proposed algorithm can provide competitive and remarkable outcomes in both benchmark and real-world problems.
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Affiliation(s)
- Mahdi Azizi
- Department of Civil Engineering, University of Tabriz, Tabriz, Iran.
- Department of Civil Engineering, Near East University, Nicosia, Cyprus.
| | | | - Mahla Basiri
- Department of Civil Engineering, University of Tabriz, Tabriz, Iran
- Department of Civil Engineering, Near East University, Nicosia, Cyprus
| | - Robert C Moehler
- Department of Civil Engineering, Monash University, Clayton, VIC, 3800, Australia
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5
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Optimal gene prioritization and disease prediction using knowledge based ontology structure. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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6
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Mashayekhi MR, Mosayyebi S. A new hybrid Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithm for the design of castellated beams. ASIAN JOURNAL OF CIVIL ENGINEERING 2023. [PMCID: PMC10061414 DOI: 10.1007/s42107-023-00630-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
In this paper, a hybrid meta-heuristic algorithm based on Harris Hawk Optimization (HHO) and Particle Swarm Optimization (PSO) is developed and used to design castellated beams. The total cost, including material and construction costs, is taken as the objective function, and the code requirements and geometry limitations have been considered in constraint functions. The proposed method is used to obtain the optimal solution of three examples. The solution is then compared with the results of five known methods. It is observed that the proposed algorithm achieves better results with a higher convergence speed as compared to the considered methods.
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Affiliation(s)
- M. R. Mashayekhi
- Department of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - S. Mosayyebi
- Department of Civil Engineering, Sharif University of Technology, Tehran, Iran
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7
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A novel metaheuristic algorithm inspired by COVID-19 for real-parameter optimization. Neural Comput Appl 2023; 35:10147-10196. [PMID: 37155551 PMCID: PMC9996600 DOI: 10.1007/s00521-023-08229-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 01/06/2023] [Indexed: 03/11/2023]
Abstract
In this modern world, we are encountered with numerous complex and emerging problems. The metaheuristic optimization science plays a key role in many fields from medicine to engineering, design, etc. Metaheuristic algorithms inspired by nature are among the most effective and fastest optimization methods utilized to optimize different objective functions to minimize or maximize one or more specific objectives. The use of metaheuristic algorithms and their modified versions is expanding every day. However, due to the abundance and complexity of various problems in the real world, it is always necessary to select the most proper metaheuristic method; hence, there is a strong need to create new algorithms to achieve our desired goal. In this paper, a new and powerful metaheuristic algorithm, called the coronavirus metamorphosis optimization algorithm (CMOA), is proposed based on metabolism and transformation under various conditions. The proposed CMOA algorithm has been tested and implemented on the comprehensive and complex CEC2014 benchmark functions, which are functions based on real-world problems. The results of the experiments in a comparative study under the same conditions show that the CMOA is superior to the newly-developed metaheuristic algorithms including AIDO, ITGO, RFOA, SCA, CSA, CS, SOS, GWO, WOA, MFO, PSO, Jaya, CMA-ES, GSA, RW-GWO, mTLBO, MG-SCA, TOGPEAe, m-SCA, EEO and OB-L-EO, indicating the effectiveness and robustness of the CMOA algorithm as a powerful algorithm. As it was observed from the results, the CMOA provides more suitable and optimized solutions than its competitors for the problems studied. The CMOA preserves the diversity of the population and prevents trapping in local optima. The CMOA is also applied to three engineering problems including optimal design of a welded beam, a three-bar truss and a pressure vessel, showing its high potential in solving such practical problems and effectiveness in finding global optima. According to the obtained results, the CMOA is superior to its counterparts in terms of providing a more acceptable solution. Several statistical indicators are also tested using the CMOA, which demonstrates its efficiency compared to the rest of the methods. This is also highlighted that the CMOA is a stable and reliable method when employed for expert systems.
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8
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Liu Y, Sun Y, Xue B, Zhang M, Yen GG, Tan KC. A Survey on Evolutionary Neural Architecture Search. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:550-570. [PMID: 34357870 DOI: 10.1109/tnnls.2021.3100554] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Deep neural networks (DNNs) have achieved great success in many applications. The architectures of DNNs play a crucial role in their performance, which is usually manually designed with rich expertise. However, such a design process is labor-intensive because of the trial-and-error process and also not easy to realize due to the rare expertise in practice. Neural architecture search (NAS) is a type of technology that can design the architectures automatically. Among different methods to realize NAS, the evolutionary computation (EC) methods have recently gained much attention and success. Unfortunately, there has not yet been a comprehensive summary of the EC-based NAS algorithms. This article reviews over 200 articles of most recent EC-based NAS methods in light of the core components, to systematically discuss their design principles and justifications on the design. Furthermore, current challenges and issues are also discussed to identify future research in this emerging field.
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9
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Shaikh AK, Nazir A, Khan I, Shah AS. Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series. Sci Rep 2022; 12:22562. [PMID: 36581655 PMCID: PMC9799709 DOI: 10.1038/s41598-022-26499-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 12/15/2022] [Indexed: 12/31/2022] Open
Abstract
Smart grids and smart homes are getting people's attention in the modern era of smart cities. The advancements of smart technologies and smart grids have created challenges related to energy efficiency and production according to the future demand of clients. Machine learning, specifically neural network-based methods, remained successful in energy consumption prediction, but still, there are gaps due to uncertainty in the data and limitations of the algorithms. Research published in the literature has used small datasets and profiles of primarily single users; therefore, models have difficulties when applied to large datasets with profiles of different customers. Thus, a smart grid environment requires a model that handles consumption data from thousands of customers. The proposed model enhances the newly introduced method of Neural Basis Expansion Analysis for interpretable Time Series (N-BEATS) with a big dataset of energy consumption of 169 customers. Further, to validate the results of the proposed model, a performance comparison has been carried out with the Long Short Term Memory (LSTM), Blocked LSTM, Gated Recurrent Units (GRU), Blocked GRU and Temporal Convolutional Network (TCN). The proposed interpretable model improves the prediction accuracy on the big dataset containing energy consumption profiles of multiple customers. Incorporating covariates into the model improved accuracy by learning past and future energy consumption patterns. Based on a large dataset, the proposed model performed better for daily, weekly, and monthly energy consumption predictions. The forecasting accuracy of the N-BEATS interpretable model for 1-day-ahead energy consumption with "day as covariates" remained better than the 1, 2, 3, and 4-week scenarios.
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Affiliation(s)
- Abdul Khalique Shaikh
- grid.412846.d0000 0001 0726 9430Department of Information Systems, Sultan Qaboos University, Muscat, Oman
| | - Amril Nazir
- grid.444464.20000 0001 0650 0848Department of Information Systems and Technology Management, Zayed University, P.O. Box 144534 Abu Dhabi, United Arab Emirates
| | - Imran Khan
- grid.412846.d0000 0001 0726 9430Department of Computer Science, Sultan Qaboos University, Muscat, Oman
| | - Abdul Salam Shah
- grid.440439.e0000 0004 0444 6368Department of Computer Engineering, University of Kuala Lumpur (UniKl-MIIT), Kuala Lumpur, Malaysia
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10
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Intelligent IoT-BOTNET Attack Detection Model with optimized Hybrid Classification Model. Comput Secur 2022. [DOI: 10.1016/j.cose.2022.103064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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11
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Oyelade ON, Ezugwu AE. Immunity-based Ebola optimization search algorithm for minimization of feature extraction with reduction in digital mammography using CNN models. Sci Rep 2022; 12:17916. [PMID: 36289321 PMCID: PMC9606367 DOI: 10.1038/s41598-022-22933-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 10/20/2022] [Indexed: 01/20/2023] Open
Abstract
Feature classification in digital medical images like mammography presents an optimization problem which researchers often neglect. The use of a convolutional neural network (CNN) in feature extraction and classification has been widely reported in the literature to have achieved outstanding performance and acceptance in the disease detection procedure. However, little emphasis is placed on ensuring that only discriminant features extracted by the convolutional operations are passed on to the classifier, to avoid bottlenecking the classification operation. Unfortunately, since this has been left unaddressed, a subtle performance impairment has resulted from this omission. Therefore, this study is devoted to addressing these drawbacks using a metaheuristic algorithm to optimize the number of features extracted by the CNN, so that suggestive features are applied for the classification process. To achieve this, a new variant of the Ebola-based optimization algorithm is proposed, based on the population immunity concept and the use of a chaos mapping initialization strategy. The resulting algorithm, called the immunity-based Ebola optimization search algorithm (IEOSA), is applied to the optimization problem addressed in the study. The optimized features represent the output from the IEOSA, which receives the noisy and unfiltered detected features from the convolutional process as input. An exhaustive evaluation of the IEOSA was carried out using classical and IEEE CEC benchmarked functions. A comparative analysis of the performance of IEOSA is presented, with some recent optimization algorithms. The experimental result showed that IEOSA performed well on all the tested benchmark functions. Furthermore, IEOSA was then applied to solve the feature enhancement and selection problem in CNN for better prediction of breast cancer in digital mammography. The classification accuracy returned by the IEOSA method showed that the new approach improved the classification process on detected features when using CNN models.
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Affiliation(s)
- Olaide N Oyelade
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa
| | - Absalom E Ezugwu
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa.
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12
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Chen L, Song N, Ma Y. Harris hawks optimization based on global cross-variation and tent mapping. THE JOURNAL OF SUPERCOMPUTING 2022; 79:5576-5614. [PMID: 36310649 PMCID: PMC9595096 DOI: 10.1007/s11227-022-04869-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 10/03/2022] [Indexed: 06/16/2023]
Abstract
Harris hawks optimization (HHO) is a new meta-heuristic algorithm that builds a model by imitating the predation process of Harris hawks. In order to solve the problems of poor convergence speed caused by uniform choice position update formula in the exploration stage of basic HHO and falling into local optimization caused by insufficient population richness in the later stage of the algorithm, a Harris hawks optimization based on global cross-variation and tent mapping (CRTHHO) is proposed in this paper. Firstly, the tent mapping is introduced in the exploration stage to optimize random parameter q to speed up the convergence in the early stage. Secondly, the crossover mutation operator is introduced to cross and mutate the global optimal position in each iteration process. The greedy strategy is used to select, which prevents the algorithm from falling into local optimal because of skipping the optimal solution and improves the convergence accuracy of the algorithm. In order to investigate the performance of CRTHHO, experiments are carried out on ten benchmark functions and the CEC2017 test set. Experimental results show that the CRTHHO algorithm performs better than the HHO algorithm and is competitive with five advanced meta-heuristic algorithms.
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Affiliation(s)
- Lei Chen
- School of Information Engineering, Tianjin University of Commerce, Beichen District, Tianjin, 300134 China
| | - Na Song
- School of Science, Tianjin University of Commerce, Beichen District, Tianjin, 300134 China
| | - Yunpeng Ma
- School of Information Engineering, Tianjin University of Commerce, Beichen District, Tianjin, 300134 China
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13
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Classification of Marine Mammals Using the Trained Multilayer Perceptron Neural Network with the Whale Algorithm Developed with the Fuzzy System. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3216400. [PMID: 36304739 PMCID: PMC9596276 DOI: 10.1155/2022/3216400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 09/11/2022] [Accepted: 09/27/2022] [Indexed: 11/17/2022]
Abstract
The existence of various sounds from different natural and unnatural sources in the deep sea has caused the classification and identification of marine mammals intending to identify different endangered species to become one of the topics of interest for researchers and activist fields. In this paper, first, an experimental data set was created using a designed scenario. The whale optimization algorithm (WOA) is then used to train the multilayer perceptron neural network (MLP-NN). However, due to the large size of the data, the algorithm has not determined a clear boundary between the exploration and extraction phases. Next, to support this shortcoming, the fuzzy inference is used as a new approach to developing and upgrading WOA called FWOA. Fuzzy inference by setting FWOA control parameters can well define the boundary between the two phases of exploration and extraction. To measure the performance of the designed categorizer, in addition to using it to categorize benchmark datasets, five benchmarking algorithms CVOA, WOA, ChOA, BWO, and PGO were also used for MLPNN training. The measured criteria are concurrency speed, ability to avoid local optimization, and the classification rate. The simulation results on the obtained data set showed that, respectively, the classification rate in MLPFWOA, MLP-CVOA, MLP-WOA, MLP-ChOA, MLP-BWO, and MLP-PGO classifiers is equal to 94.98, 92.80, 91.34, 90.24, 89.04, and 88.10. As a result, MLP-FWOA performed better than other algorithms.
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14
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Khalid AM, Hosny KM, Mirjalili S. COVIDOA: a novel evolutionary optimization algorithm based on coronavirus disease replication lifecycle. Neural Comput Appl 2022; 34:22465-22492. [PMID: 36043205 PMCID: PMC9411047 DOI: 10.1007/s00521-022-07639-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 07/18/2022] [Indexed: 11/13/2022]
Abstract
This paper presents a novel bio-inspired optimization algorithm called Coronavirus Optimization Algorithm (COVIDOA). COVIDOA is an evolutionary search strategy that mimics the mechanism of coronavirus when hijacking human cells. COVIDOA is inspired by the frameshifting technique used by the coronavirus for replication. The proposed algorithm is tested using 20 standard benchmark optimization functions with different parameter values. Besides, we utilized five IEEE Congress of Evolutionary Computation (CEC) benchmark test functions (CECC06, 2019 Competition) and five CEC 2011 real-world problems to prove the proposed algorithm's efficiency. The proposed algorithm is compared to eight of the most popular and recent metaheuristic algorithms from the state-of-the-art in terms of best cost, average cost (AVG), corresponding standard deviation (STD), and convergence speed. The results demonstrate that COVIDOA is superior to most existing metaheuristics.
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15
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Rizk-Allah RM, Zineldin MI, Mousa AAA, Abdel-Khalek S, Mohamed MS, Snášel V. On a Novel Hybrid Manta Ray Foraging Optimizer and Its Application on Parameters Estimation of Lithium-Ion Battery. INT J COMPUT INT SYS 2022. [PMCID: PMC9364860 DOI: 10.1007/s44196-022-00114-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
In this paper, we propose a hybrid meta-heuristic algorithm called MRFO-PSO that hybridizes the Manta ray foraging optimization (MRFO) and particle swarm optimization (PSO) with the aim to balance the exploration and exploitation abilities. In the MRFO-PSO, the concept of velocity of the PSO is incorporated to guide the searching process of the MRFO, where the velocity is updated by the first best and the second-best solutions. By this integration, the balancing issue between the exploration phase and exploitation ability has been further improved. To illustrate the robustness and effectiveness of the MRFO-PSO, it is tested on 23 benchmark equations and it is applied to estimate the parameters of Tremblay's model with three different commercial lithium-ion batteries including the Samsung Cylindrical ICR18650-22 lithium-ion rechargeable battery, Tenergy 30209 prismatic cell, Ultralife UBBL03 (type LI-7) rechargeable battery. The study contribution exclusively utilizes hybrid machine learning-based tuning for Tremblay's model parameters to overcome the disadvantages of human-based tuning. In addition, the comparisons of the MRFO-PSO with six recent meta-heuristic methods are performed in terms of some statistical metrics and Wilcoxon’s test-based non-parametric test. As a result, the conducted performance measures have confirmed the competitive results as well as the superiority of the proposed MRFO-PSO.
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Affiliation(s)
- Rizk M. Rizk-Allah
- Basic Engineering Science Department, Faculty of Engineering, Menoufia University, Shebin El-Kom, 32511 Egypt
| | | | - Abd Allah A. Mousa
- Department of Mathematics, College of Science, Taif University, Taif, 21944 Saudi Arabia
| | - S. Abdel-Khalek
- Department of Mathematics, College of Science, Taif University, Taif, 21944 Saudi Arabia
| | - Mohamed S. Mohamed
- Department of Mathematics, College of Science, Taif University, Taif, 21944 Saudi Arabia
| | - Václav Snášel
- Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava, Poruba, 70800 Ostrava, Czech Republic
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16
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Melgar-García L, Gutiérrez-Avilés D, Godinho MT, Espada R, Brito IS, Martínez-Álvarez F, Troncoso A, Rubio-Escudero C. A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.06.101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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17
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Balasubramanian E, Elangovan E, Tamilarasan P, Kanagachidambaresan GR, Chutia D. Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 14:1-21. [PMID: 35789596 PMCID: PMC9244350 DOI: 10.1007/s12652-022-04098-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 06/06/2022] [Indexed: 06/15/2023]
Abstract
Autonomous mission capabilities with optimal path are stringent requirements for Unmanned Aerial Vehicle (UAV) navigation in diverse applications. The proposed research framework is to identify an energy-efficient optimal path to achieve the designated missions for the navigation of UAVs in various constrained and denser obstacle prone regions. Hence, the present work is aimed to develop an optimal energy-efficient path planning algorithm through combining well known modified ant colony optimization algorithm (MACO) and a variant of A*, namely the memory-efficient A* algorithm (MEA*) for avoiding the obstacles in three dimensional (3D) environment and arrive at an optimal path with minimal energy consumption. The novelty of the proposed method relies on integrating the above two efficient algorithms to optimize the UAV path planning task. The basic design of this study is, that by utilizing an improved version of the pheromone strategy in MACO, the local trap and premature convergence are minimized, and also an optimal path is found by means of reward and penalty mechanism. The sole notion of integrating the MEA* algorithm arises from the fact that it is essential to overcome the stringent memory requirement of conventional A* algorithm and to resolve the issue of tracking only the edges of the grids. Combining the competencies of MACO and MEA*, a hybrid algorithm is proposed to avoid obstacles and find an efficient path. Simulation studies are performed by varying the number of obstacles in a 3D domain. The real-time flight trials are conducted experimentally using a UAV by implementing the attained optimal path. A comparison of the total energy consumption of UAV with theoretical analysis is accomplished. The significant finding of this study is that, the MACO-MEA* algorithm achieved 21% less energy consumption and 55% shorter execution time than the MACO-A*. moreover, the path traversed in both simulation and experimental methods is 99% coherent with each other. it confirms that the developed hybrid MACO-MEA* energy-efficient algorithm is a viable solution for UAV navigation in 3D obstacles prone regions.
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Affiliation(s)
- E. Balasubramanian
- Department of Mechanical Engineering, Head-Centre for Autonomous System Research, Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology, Avadi, Chennai, Tamilnadu 600062 India
| | - E. Elangovan
- Department of Electronics and Communication Engineering, Centre for Autonomous System Research, Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology, Avadi, Chennai, Tamilnadu 600062 India
| | - P. Tamilarasan
- Department of Aeronautical Engineering, Centre for Autonomous System Research, Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology, Avadi, Chennai, Tamilnadu 600062 India
| | - G. R. Kanagachidambaresan
- Department of Computer Science and Engineering and Associate Editor, Wireless Networks, Springer, Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology, Avadi, Chennai, Tamilnadu 600062 India
| | - Dibyajyoti Chutia
- Department of Space, North Eastern Space Applications Centre, ISRO, Umiam, Meghalaya 793103 India
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18
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Galetsi P, Katsaliaki K, Kumar S. The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19. Soc Sci Med 2022; 301:114973. [PMID: 35452893 PMCID: PMC9001170 DOI: 10.1016/j.socscimed.2022.114973] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 02/21/2022] [Accepted: 04/08/2022] [Indexed: 12/23/2022]
Abstract
With Covid-19 impacting communities in different ways, research has increasingly turned to big data analytics (BDA) and artificial intelligence (AI) tools to track and monitor the virus's spread and its effect on humanity and the global economy. The purpose of this study is to conduct an in-depth literature review to identify how BDA and AI were involved in the management of Covid-19 (while considering diversity, equity, and inclusion (DEI)). The rigorous search resulted in a portfolio of 607 articles, retrieved from the Web of Science database, where content analysis has been conducted. This study identifies the BDA and AI applications developed to deal with the initial Covid-19 outbreak and the containment of the pandemic, along with their benefits for the social good. Moreover, this study reveals the DEI challenges related to these applications, ways to mitigate the concerns, and how to develop viable techniques to deal with similar crises in the future. The article pool recognized the high presence of machine learning (ML) and the role of mobile technology, social media and telemedicine in the use of BDA and AI during Covid-19. This study offers a collective insight into many of the key issues and underlying complexities affecting public health and society from Covid-19, and the solutions offered from information systems and technological perspectives.
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Affiliation(s)
- Panagiota Galetsi
- School of Humanities, Social Sciences and Economics, International Hellenic University, 14th Km Thessaloniki-N.Moudania, Thessaloniki, 57001, Greece
| | - Korina Katsaliaki
- School of Humanities, Social Sciences and Economics, International Hellenic University, 14th Km Thessaloniki-N.Moudania, Thessaloniki, 57001, Greece
| | - Sameer Kumar
- Opus College of Business, University of St. Thomas Minneapolis Campus 1000 LaSalle Ave, Schulze Hall 333, Minneapolis, MN, 55403, USA.
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19
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Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4673665. [PMID: 35535189 PMCID: PMC9078797 DOI: 10.1155/2022/4673665] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 03/13/2022] [Accepted: 04/07/2022] [Indexed: 01/11/2023]
Abstract
One of the most popular population-based metaheuristic algorithms is Harris hawks optimization (HHO), which imitates the hunting mechanisms of Harris hawks in nature. Although HHO can obtain optimal solutions for specific problems, it stagnates in local optima solutions. In this paper, an improved Harris hawks optimization named ERHHO is proposed for solving global optimization problems. Firstly, we introduce tent chaotic map in the initialization stage to improve the diversity of the initialization population. Secondly, an exploration factor is proposed to optimize parameters for improving the ability of exploration. Finally, a random walk strategy is proposed to enhance the exploitation capability of HHO further and help search agent jump out the local optimal. Results from systematic experiments conducted on 23 benchmark functions and the CEC2017 test functions demonstrated that the proposed method can provide a more reliable solution than other well-known algorithms.
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20
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Antony Sophia N, Wiselin Jiji G. Classification of Acute Pathology for Vocal Cord Using Advanced Multi-Resolution Algorithm. INT J PATTERN RECOGN 2022. [DOI: 10.1142/s0218001422580046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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21
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Sharma V, Tripathi AK. A systematic review of meta-heuristic algorithms in IoT based application. ARRAY 2022. [DOI: 10.1016/j.array.2022.100164] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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22
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Saffari A, Khishe M, Zahiri SH. Fuzzy-ChOA: an improved chimp optimization algorithm for marine mammal classification using artificial neural network. ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING 2022; 111:403-417. [PMID: 35291314 PMCID: PMC8912427 DOI: 10.1007/s10470-022-02014-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 01/19/2022] [Accepted: 02/16/2022] [Indexed: 06/14/2023]
Abstract
Chimp optimization algorithm (ChOA) is a robust nature-inspired technique, which was recently proposed for addressing real-world challenging engineering problems. Due to the novelty of the ChOA, there is room for its improvement. Recognition and classification of marine mammals using artificial neural networks (ANNs) are high-dimensional challenging problems. In order to address this problem, this paper proposed the using of ChOA as ANN's trainer. However, evolving ANNs using metaheuristic algorithms suffers from high complexity and processing time. In order to address this shortcoming, this paper proposes the fuzzy logic to adjust the ChOA's control parameters (Fuzzy-ChOA) for tuning the relationship between exploration and exploitation phases. In this regard, we collect underwater marine mammals sounds and then produce an experimental dataset. After pre-processing and feature extraction, the ANN is used as a classifier. Besides, for having a fair comparison, we used a benchmark audio database of marine mammals. The comparison algorithms include ChOA, coronavirus optimization algorithm, harris hawks optimization, black widow optimization algorithm, Kalman filter benchmark algorithms, and also comparative benchmarks include convergence speed, local optimal avoidance ability, classification rate, and receiver operating characteristics (ROC). The simulation results show that the proposed fuzzy model can tune the boundary between the exploration and extraction phases. The convergence curve and ROC confirm that the convergence rate and performance of the designed recognizer are better than benchmark algorithms.
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Affiliation(s)
- Abbas Saffari
- Department of Electrical Engineering, University of Birjand, Birjand, Iran
| | - Mohammad Khishe
- Department of Marine Electronics and Communication Engineering, Imam Khomeini Marine Science University of Nowshahr, Nowshahr, Iran
| | - Seyed-Hamid Zahiri
- Department of Electrical Engineering, University of Birjand, Birjand, Iran
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23
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Rai R, Das A, Dhal KG. Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review. EVOLVING SYSTEMS 2022; 13:889-945. [PMID: 37520044 PMCID: PMC8859498 DOI: 10.1007/s12530-022-09425-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 01/15/2022] [Indexed: 12/14/2022]
Abstract
Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019-2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.
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Affiliation(s)
- Rebika Rai
- Department of Computer Applications, Sikkim University, Sikkim, India
| | - Arunita Das
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal India
| | - Krishna Gopal Dhal
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal India
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24
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Torres JF, Martínez-Álvarez F, Troncoso A. A deep LSTM network for the Spanish electricity consumption forecasting. Neural Comput Appl 2022; 34:10533-10545. [PMID: 35153386 PMCID: PMC8817773 DOI: 10.1007/s00521-021-06773-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 11/21/2021] [Indexed: 11/24/2022]
Abstract
Nowadays, electricity is a basic commodity necessary for the well-being of any modern society. Due to the growth in electricity consumption in recent years, mainly in large cities, electricity forecasting is key to the management of an efficient, sustainable and safe smart grid for the consumer. In this work, a deep neural network is proposed to address the electricity consumption forecasting in the short-term, namely, a long short-term memory (LSTM) network due to its ability to deal with sequential data such as time-series data. First, the optimal values for certain hyper-parameters have been obtained by a random search and a metaheuristic, called coronavirus optimization algorithm (CVOA), based on the propagation of the SARS-Cov-2 virus. Then, the optimal LSTM has been applied to predict the electricity demand with 4-h forecast horizon. Results using Spanish electricity data during nine years and half measured with 10-min frequency are presented and discussed. Finally, the performance of the proposed LSTM using random search and the LSTM using CVOA is compared, on the one hand, with that of recently published deep neural networks (such as a deep feed-forward neural network optimized with a grid search) and temporal fusion transformers optimized with a sampling algorithm, and, on the other hand, with traditional machine learning techniques, such as a linear regression, decision trees and tree-based ensemble techniques (gradient-boosted trees and random forest), achieving the smallest prediction error below 1.5%.
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25
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A Modified Coronavirus Herd Immunity Optimizer for the Power Scheduling Problem. MATHEMATICS 2022. [DOI: 10.3390/math10030315] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The Coronavirus herd immunity optimizer (CHIO) is a new human-based optimization algorithm that imitates the herd immunity strategy to eliminate of the COVID-19 disease. In this paper, the coronavirus herd immunity optimizer (CHIO) is modified to tackle a discrete power scheduling problem in a smart home (PSPSH). PSPSH is a combinatorial optimization problem with NP-hard features. It is a highly constrained discrete scheduling problem concerned with assigning the operation time for smart home appliances based on a dynamic pricing scheme(s) and several other constraints. The primary objective when solving PSPSH is to maintain the stability of the power system by reducing the ratio between average and highest power demand (peak-to-average ratio (PAR)) and reducing electricity bill (EB) with considering the comfort level of users (UC). This paper modifies and adapts the CHIO algorithm to deal with such discrete optimization problems, particularly PSPSH. The adaptation and modification include embedding PSPSH problem-specific operators to CHIO operations to meet the discrete search space requirements. PSPSH is modeled as a multi-objective problem considering all objectives, including PAR, EB, and UC. The proposed method is examined using a dataset that contains 36 home appliances and seven consumption scenarios. The main CHIO parameters are tuned to find their best values. These best values are used to evaluate the proposed method by comparing its results with comparative five metaheuristic algorithms. The proposed method shows encouraging results and almost obtains the best results in all consumption scenarios.
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26
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Operation of the Egyptian Power Grid with Maximum Penetration Level of Renewable Energies Using Corona Virus Optimization Algorithm. SMART CITIES 2022. [DOI: 10.3390/smartcities5010003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Countries around the world are looking forward to fully sustainable energy by the middle of the century to meet Paris climate agreement goals. This paper presents a novel algorithm to optimally operate the Egyptian grid with maximum renewable power generation, minimum voltage deviation and minimum power losses. The optimal operation is performed using Corona Virus Algorithm (CVO). The proposed CVO is compared to the Teaching and Learning-Based Optimization (TLBO) algorithm in terms of voltage deviation, power losses and share of renewable energies. The real demand, solar irradiance and wind speed in typical winter and summer days are considered. The 2020 Egyptian grid model is developed, simulated, and optimized using DIgSILENT software application. The results have proved the effectiveness of the proposed CVO, compared to the TLBO, to operate the grid with the highest share possible of renewables. The paper is a step forward to achieve Egyptian government targets to reach 20% and 42% penetration level of renewable energies by 2022 and 2035, respectively.
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Khalilpourazari S, Hashemi Doulabi H. Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec. ANNALS OF OPERATIONS RESEARCH 2022; 312:1261-1305. [PMID: 33424076 PMCID: PMC7779111 DOI: 10.1007/s10479-020-03871-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/07/2020] [Indexed: 05/12/2023]
Abstract
World Health Organization (WHO) stated COVID-19 as a pandemic in March 2020. Since then, 26,795,847 cases have been reported worldwide, and 878,963 lost their lives due to the illness by September 3, 2020. Prediction of the COVID-19 pandemic will enable policymakers to optimize the use of healthcare system capacity and resource allocation to minimize the fatality rate. In this research, we design a novel hybrid reinforcement learning-based algorithm capable of solving complex optimization problems. We apply our algorithm to several well-known benchmarks and show that the proposed methodology provides quality solutions for most complex benchmarks. Besides, we show the dominance of the offered method over state-of-the-art methods through several measures. Moreover, to demonstrate the suggested method's efficiency in optimizing real-world problems, we implement our approach to the most recent data from Quebec, Canada, to predict the COVID-19 outbreak. Our algorithm, combined with the most recent mathematical model for COVID-19 pandemic prediction, accurately reflected the future trend of the pandemic with a mean square error of 6.29E-06. Furthermore, we generate several scenarios for deepening our insight into pandemic growth. We determine essential factors and deliver various managerial insights to help policymakers making decisions regarding future social measures.
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Affiliation(s)
- Soheyl Khalilpourazari
- Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, Canada
- Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Montreal, Canada
| | - Hossein Hashemi Doulabi
- Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, Canada
- Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Montreal, Canada
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28
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Nama S, Saha AK, Sharma S. Performance up-gradation of Symbiotic Organisms Search by Backtracking Search Algorithm. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 13:5505-5546. [PMID: 33868507 PMCID: PMC8036246 DOI: 10.1007/s12652-021-03183-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 03/25/2021] [Indexed: 05/08/2023]
Abstract
Symbiotic Organisms Search (SOS) algorithm is characterized based on the framework of relationships among the ecosystem species. Nevertheless, it is suffering from wasteful discovery, little productivity, and slack convergence rate. These deficiencies cause stagnation at the local optimum, which is hazardous in deciding the genuine optima of the optimization problem. Backtracking Search Algorithm (BSA) is likewise another streamlining method for comprehending the non-direct complex optimization problem. Consequently, in the current paper, an endeavor has been made toward the expulsion of the downsides from the traditional SOS by proposing a novel ensemble technique called e-SOSBSA to overhaul the degree of intensification and diversification. In e-SOSBSA, firstly, the mutation operator of BSA with the self-adaptive mutation rate is incorporated to produce a mutant of population and leap out from the local optima. Secondly, the crossover operator of BSA with the adaptive component of mixrate is incorporated to leverage the entire active search regions visited previously. The suggested e-SOSBSA has been tested with 20 classical benchmark functions, IEEE CEC2014, CEC2015, CEC2017, and the latest CEC 2020 test functions. Statistical analyses, convergence analysis, and diversity analysis are performed to show the stronger search capabilities of the proposed e-SOSBSA in contrast with the component algorithms and several state-of-the-art algorithms. Moreover, the proposed e-SOSBSA is applied to find the optimum value of the seven problems of engineering optimization. The numerical investigations and examinations show that the proposed e-SOSBSA can be profoundly viable in tackling real-world engineering optimization problems.
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Affiliation(s)
- Sukanta Nama
- Department of Applied Mathematics, Maharaja Bir Bikram University, Agartala, Tripura India
- Department of Mathematics, National Institute of Technology Agartala, Agartala, Tripura 799046 India
| | - Apu Kumar Saha
- Department of Mathematics, National Institute of Technology Agartala, Agartala, Tripura 799046 India
| | - Sushmita Sharma
- Department of Mathematics, National Institute of Technology Agartala, Agartala, Tripura 799046 India
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29
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Application of bio-inspired optimization algorithms in food processing. Curr Res Food Sci 2022; 5:432-450. [PMID: 35243356 PMCID: PMC8866069 DOI: 10.1016/j.crfs.2022.02.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 02/08/2022] [Accepted: 02/11/2022] [Indexed: 12/23/2022] Open
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30
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Transmission Network Expansion Planning Considering Optimal Allocation of Series Capacitive Compensation and Active Power Losses. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app12010388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper presents a modeling and solution approach to the static and multistage transmission network expansion planning problem considering series capacitive compensation and active power losses. The transmission network expansion planning is formulated as a mixed integer nonlinear programming problem and solved through a highly efficient genetic algorithm. Furthermore, the Villasana Garver’s constructive heuristic algorithm is implemented to render the configurations of the genetic algorithm feasible. The installation of series capacitive compensation devices is carried out with the aim of modifying the reactance of the original circuit. The linearization of active power losses is done through piecewise linear functions. The proposed model was implemented in C++ language programming. To show the applicability and effectiveness of the proposed methodology several tests are performed on the 6-bus Garver system, the IEEE 24-bus test system, and the South Brazilian 46-bus test system, presenting costs reductions in their multi-stage expansion planning of 7.4%, 4.65% and 1.74%, respectively.
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31
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Deep Ensemble of Slime Mold Algorithm and Arithmetic Optimization Algorithm for Global Optimization. Processes (Basel) 2021. [DOI: 10.3390/pr9101774] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
In this paper, a new hybrid algorithm based on two meta-heuristic algorithms is presented to improve the optimization capability of original algorithms. This hybrid algorithm is realized by the deep ensemble of two new proposed meta-heuristic methods, i.e., slime mold algorithm (SMA) and arithmetic optimization algorithm (AOA), called DESMAOA. To be specific, a preliminary hybrid method was applied to obtain the improved SMA, called SMAOA. Then, two strategies that were extracted from the SMA and AOA, respectively, were embedded into SMAOA to boost the optimizing speed and accuracy of the solution. The optimization performance of the proposed DESMAOA was analyzed by using 23 classical benchmark functions. Firstly, the impacts of different components are discussed. Then, the exploitation and exploration capabilities, convergence behaviors, and performances are evaluated in detail. Cases at different dimensions also were investigated. Compared with the SMA, AOA, and another five well-known optimization algorithms, the results showed that the proposed method can outperform other optimization algorithms with high superiority. Finally, three classical engineering design problems were employed to illustrate the capability of the proposed algorithm for solving the practical problems. The results also indicate that the DESMAOA has very promising performance when solving these problems.
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32
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Jordan E, Shin DE, Leekha S, Azarm S. Optimization in the Context of COVID-19 Prediction and Control: A Literature Review. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:130072-130093. [PMID: 35781925 PMCID: PMC8768956 DOI: 10.1109/access.2021.3113812] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 09/10/2021] [Indexed: 05/08/2023]
Abstract
This paper presents an overview of some key results from a body of optimization studies that are specifically related to COVID-19, as reported in the literature during 2020-2021. As shown in this paper, optimization studies in the context of COVID-19 have been used for many aspects of the pandemic. From these studies, it is observed that since COVID-19 is a multifaceted problem, it cannot be studied from a single perspective or framework, and neither can the related optimization models. Four new and different frameworks are proposed that capture the essence of analyzing COVID-19 (or any pandemic for that matter) and the relevant optimization models. These are: (i) microscale vs. macroscale perspective; (ii) early stages vs. later stages perspective; (iii) aspects with direct vs. indirect relationship to COVID-19; and (iv) compartmentalized perspective. To limit the scope of the review, only optimization studies related to the prediction and control of COVID-19 are considered (public health focused), and which utilize formal optimization techniques or machine learning approaches. In this context and to the best of our knowledge, this survey paper is the first in the literature with a focus on the prediction and control related optimization studies. These studies include optimization of screening testing strategies, prediction, prevention and control, resource management, vaccination prioritization, and decision support tools. Upon reviewing the literature, this paper identifies current gaps and major challenges that hinder the closure of these gaps and provides some insights into future research directions.
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Affiliation(s)
- Elizabeth Jordan
- Department of Mechanical EngineeringUniversity of MarylandCollege ParkMD20742USA
| | - Delia E. Shin
- Department of Mechanical EngineeringUniversity of MarylandCollege ParkMD20742USA
| | - Surbhi Leekha
- Department of Epidemiology and Public HealthUniversity of Maryland School of MedicineBaltimoreMD21201USA
| | - Shapour Azarm
- Department of Mechanical EngineeringUniversity of MarylandCollege ParkMD20742USA
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Khalilpourazari S, Hashemi Doulabi H, Özyüksel Çiftçioğlu A, Weber GW. Gradient-based grey wolf optimizer with Gaussian walk: Application in modelling and prediction of the COVID-19 pandemic. EXPERT SYSTEMS WITH APPLICATIONS 2021; 177:114920. [PMID: 33814731 PMCID: PMC7997148 DOI: 10.1016/j.eswa.2021.114920] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 01/07/2021] [Accepted: 03/15/2021] [Indexed: 05/06/2023]
Abstract
This research proposes a new type of Grey Wolf optimizer named Gradient-based Grey Wolf Optimizer (GGWO). Using gradient information, we accelerated the convergence of the algorithm that enables us to solve well-known complex benchmark functions optimally for the first time in this field. We also used the Gaussian walk and Lévy flight to improve the exploration and exploitation capabilities of the GGWO to avoid trapping in local optima. We apply the suggested method to several benchmark functions to show its efficiency. The outcomes reveal that our algorithm performs superior to most existing algorithms in the literature in most benchmarks. Moreover, we apply our algorithm for predicting the COVID-19 pandemic in the US. Since the prediction of the epidemic is a complicated task due to its stochastic nature, presenting efficient methods to solve the problem is vital. Since the healthcare system has a limited capacity, it is essential to predict the pandemic's future trend to avoid overload. Our results predict that the US will have almost 16 million cases by the end of November. The upcoming peak in the number of infected, ICU admitted cases would be mid-to-end November. In the end, we proposed several managerial insights that will help the policymakers have a clearer vision about the growth of COVID-19 and avoid equipment shortages in healthcare systems.
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Affiliation(s)
- Soheyl Khalilpourazari
- Department of Mechanical, Industrial & Aerospace Engineering, Concordia University, Montreal, Canada
- Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Montreal, Canada
| | - Hossein Hashemi Doulabi
- Department of Mechanical, Industrial & Aerospace Engineering, Concordia University, Montreal, Canada
- Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Montreal, Canada
| | | | - Gerhard-Wilhelm Weber
- Faculty of Engineering Management, Poznan University of Technology, ul. Jacka Rychlewskiego 2, Strzelecka, 60-965, Poznan, Poland
- Institute of Applied Mathematics, Middle East Technical University, 06800 Ankara, Turkey
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Piotrowski AP, Piotrowska AE. Differential evolution and particle swarm optimization against COVID-19. Artif Intell Rev 2021; 55:2149-2219. [PMID: 34426713 PMCID: PMC8374127 DOI: 10.1007/s10462-021-10052-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/17/2021] [Indexed: 11/29/2022]
Abstract
COVID-19 disease, which highly affected global life in 2020, led to a rapid scientific response. Versatile optimization methods found their application in scientific studies related to COVID-19 pandemic. Differential Evolution (DE) and Particle Swarm Optimization (PSO) are two metaheuristics that for over two decades have been widely researched and used in various fields of science. In this paper a survey of DE and PSO applications for problems related with COVID-19 pandemic that were rapidly published in 2020 is presented from two different points of view: 1. practitioners seeking the appropriate method to solve particular problem, 2. experts in metaheuristics that are interested in methodological details, inter comparisons between different methods, and the ways for improvement. The effectiveness and popularity of DE and PSO is analyzed in the context of other metaheuristics used against COVID-19. It is found that in COVID-19 related studies: 1. DE and PSO are most frequently used for calibration of epidemiological models and image-based classification of patients or symptoms, but applications are versatile, even interconnecting the pandemic and humanities; 2. reporting on DE or PSO methodological details is often scarce, and the choices made are not necessarily appropriate for the particular algorithm or problem; 3. mainly the basic variants of DE and PSO that were proposed in the late XX century are applied, and research performed in recent two decades is rather ignored; 4. the number of citations and the availability of codes in various programming languages seems to be the main factors for choosing metaheuristics that are finally used.
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Affiliation(s)
- Adam P. Piotrowski
- Institute of Geophysics, Polish Academy of Sciences, Ks. Janusza 64, 01-452 Warsaw, Poland
| | - Agnieszka E. Piotrowska
- Faculty of Polish Studies, University of Warsaw, Krakowskie Przedmiescie 26/28, 00-927 Warsaw, Poland
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Torre-Bastida AI, Díaz-de-Arcaya J, Osaba E, Muhammad K, Camacho D, Del Ser J. Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions. Neural Comput Appl 2021:1-31. [PMID: 34366573 PMCID: PMC8329000 DOI: 10.1007/s00521-021-06332-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Accepted: 07/12/2021] [Indexed: 02/07/2023]
Abstract
This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.
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Affiliation(s)
| | - Josu Díaz-de-Arcaya
- TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
| | - Eneko Osaba
- TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
| | - Khan Muhammad
- Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Software, Sejong University, Seoul, 143-747 Republic of Korea
| | - David Camacho
- Universidad Politécnica de Madrid, 28031 Madrid, Spain
| | - Javier Del Ser
- University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
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36
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Big Data Research in Fighting COVID-19: Contributions and Techniques. BIG DATA AND COGNITIVE COMPUTING 2021. [DOI: 10.3390/bdcc5030030] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The COVID-19 pandemic has induced many problems in various sectors of human life. After more than one year of the pandemic, many studies have been conducted to discover various technological innovations and applications to combat the virus that has claimed many lives. The use of Big Data technology to mitigate the threats of the pandemic has been accelerated. Therefore, this survey aims to explore Big Data technology research in fighting the pandemic. Furthermore, the relevance of Big Data technology was analyzed while technological contributions to five main areas were highlighted. These include healthcare, social life, government policy, business and management, and the environment. The analytical techniques of machine learning, deep learning, statistics, and mathematics were discussed to solve issues regarding the pandemic. The data sources used in previous studies were also presented and they consist of government officials, institutional service, IoT generated, online media, and open data. Therefore, this study presents the role of Big Data technologies in enhancing the research relative to COVID-19 and provides insights into the current state of knowledge within the domain and references for further development or starting new studies are provided.
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37
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A new approach of deep neural computing for spatial prediction of wildfire danger at tropical climate areas. ECOL INFORM 2021. [DOI: 10.1016/j.ecoinf.2021.101300] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Emami H. Stock exchange trading optimization algorithm: a human-inspired method for global optimization. THE JOURNAL OF SUPERCOMPUTING 2021; 78:2125-2174. [PMID: 34188358 PMCID: PMC8227409 DOI: 10.1007/s11227-021-03943-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/08/2021] [Indexed: 05/27/2023]
Abstract
In this paper, a human-inspired optimization algorithm called stock exchange trading optimization (SETO) for solving numerical and engineering problems is introduced. The inspiration source of this optimizer is the behavior of traders and stock price changes in the stock market. Traders use various fundamental and technical analysis methods to gain maximum profit. SETO mathematically models the technical trading strategy of traders to perform optimization. It contains three main actuators including rising, falling, and exchange. These operators navigate the search agents toward the global optimum. The proposed algorithm is compared with seven popular meta-heuristic optimizers on forty single-objective unconstraint numerical functions and four engineering design problems. The statistical results obtained on test problems show that SETO is capable of providing competitive and promising performances compared with counterpart algorithms in solving optimization problems of different dimensions, especially 1000-dimension problems. Out of 40 numerical functions, the SETO algorithm has achieved the global optimum on 36 functions, and out of 4 engineering problems, it has obtained the best results on 3 problems.
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Optimal Coronavirus Optimization Algorithm Based PID Controller for High Performance Brushless DC Motor. ALGORITHMS 2021. [DOI: 10.3390/a14070193] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper presents an efficient coronavirus optimization algorithm (CVOA) to find the optimal values of the PID controller to track a preselected reference speed of a brushless DC (BLDC) motor under several types of disturbances. This work simulates how the coronavirus (COVID-19) spreads and infects healthy people. The initial values of PID controller parameters consider the zero patient, who infects new patients (other values of PID controller parameters). The model aims to simulate as accurately as possible the coronavirus activity. The CVOA has two major advantages compared to other similar strategies. First, the CVOA parameters are already adjusted according to disease statistics to prevent designers from initializing them with arbitrary values. Second, the approach has the ability to finish after several iterations where the infected population initially grows at an exponential rate. The proposed CVOA was investigated with well-known optimization techniques such as the genetic algorithm (GA) and Harmony Search (HS) optimization. A multi-objective function was used to allow the designer to select the desired rise time, the desired settling time, the desired overshoot, and the desired steady-state error. Several tests were performed to investigate the obtained proper values of PID controller parameters. In the first test, the BLDC motor was exposed to sudden load at a steady speed. In the second test, the continuous sinusoidal load was applied to the rotor of the BLDC motor. In the third test, different operating points of reference speed were selected to the rotor of the BLDC motor. The results proved that the CVOA-based PID controller has the best performance among the techniques. In the first test, the CVOA-based PID controller has a minimum rise time (0.0042 s), minimum settling time (0.0079 s), and acceptable overshoot (0.0511%). In the second test, the CVOA-based PID controller has the minimum deviation about the reference speed (±4 RPM). In the third test, the CVOA-based PID controller can accurately track the reference speed among other techniques.
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40
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Pegalajar M, Ruiz L, Cuéllar M, Rueda R. Analysis and enhanced prediction of the Spanish Electricity Network through Big Data and Machine Learning techniques. Int J Approx Reason 2021. [DOI: 10.1016/j.ijar.2021.03.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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41
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Bio-Inspired Algorithms and Its Applications for Optimization in Fuzzy Clustering. ALGORITHMS 2021. [DOI: 10.3390/a14040122] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
In recent years, new metaheuristic algorithms have been developed taking as reference the inspiration on biological and natural phenomena. This nature-inspired approach for algorithm development has been widely used by many researchers in solving optimization problems. These algorithms have been compared with the traditional ones and have demonstrated to be superior in many complex problems. This paper attempts to describe the algorithms based on nature, which are used in optimizing fuzzy clustering in real-world applications. We briefly describe the optimization methods, the most cited ones, nature-inspired algorithms that have been published in recent years, authors, networks and relationship of the works, etc. We believe the paper can serve as a basis for analysis of the new area of nature and bio-inspired optimization of fuzzy clustering.
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Barzegar V, Laflamme S, Hu C, Dodson J. Multi-Time Resolution Ensemble LSTMs for Enhanced Feature Extraction in High-Rate Time Series. SENSORS (BASEL, SWITZERLAND) 2021; 21:1954. [PMID: 33802233 PMCID: PMC8001144 DOI: 10.3390/s21061954] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/03/2021] [Accepted: 03/04/2021] [Indexed: 11/17/2022]
Abstract
Systems experiencing high-rate dynamic events, termed high-rate systems, typically undergo accelerations of amplitudes higher than 100 g-force in less than 10 ms. Examples include adaptive airbag deployment systems, hypersonic vehicles, and active blast mitigation systems. Given their critical functions, accurate and fast modeling tools are necessary for ensuring the target performance. However, the unique characteristics of these systems, which consist of (1) large uncertainties in the external loads, (2) high levels of non-stationarities and heavy disturbances, and (3) unmodeled dynamics generated from changes in system configurations, in combination with the fast-changing environments, limit the applicability of physical modeling tools. In this paper, a deep learning algorithm is used to model high-rate systems and predict their response measurements. It consists of an ensemble of short-sequence long short-term memory (LSTM) cells which are concurrently trained. To empower multi-step ahead predictions, a multi-rate sampler is designed to individually select the input space of each LSTM cell based on local dynamics extracted using the embedding theorem. The proposed algorithm is validated on experimental data obtained from a high-rate system. Results showed that the use of the multi-rate sampler yields better feature extraction from non-stationary time series compared with a more heuristic method, resulting in significant improvement in step ahead prediction accuracy and horizon. The lean and efficient architecture of the algorithm results in an average computing time of 25 μμs, which is below the maximum prediction horizon, therefore demonstrating the algorithm's promise in real-time high-rate applications.
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Affiliation(s)
- Vahid Barzegar
- Department of Civil, Construction, and Environmental Engineering, Iowa State University, 813 Bissell Road, Ames, IA 50011, USA;
| | - Simon Laflamme
- Department of Civil, Construction, and Environmental Engineering, Iowa State University, 813 Bissell Road, Ames, IA 50011, USA;
- Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA;
| | - Chao Hu
- Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA;
- Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA
| | - Jacob Dodson
- Air Force Research Laboratory, Munitions Directorate, Fuzes Branch, Eglin Air Force Base, FL 32542, USA;
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43
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Torres JF, Hadjout D, Sebaa A, Martínez-Álvarez F, Troncoso A. Deep Learning for Time Series Forecasting: A Survey. BIG DATA 2021; 9:3-21. [PMID: 33275484 DOI: 10.1089/big.2020.0159] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Time series forecasting has become a very intensive field of research, which is even increasing in recent years. Deep neural networks have proved to be powerful and are achieving high accuracy in many application fields. For these reasons, they are one of the most widely used methods of machine learning to solve problems dealing with big data nowadays. In this work, the time series forecasting problem is initially formulated along with its mathematical fundamentals. Then, the most common deep learning architectures that are currently being successfully applied to predict time series are described, highlighting their advantages and limitations. Particular attention is given to feed forward networks, recurrent neural networks (including Elman, long-short term memory, gated recurrent units, and bidirectional networks), and convolutional neural networks. Practical aspects, such as the setting of values for hyper-parameters and the choice of the most suitable frameworks, for the successful application of deep learning to time series are also provided and discussed. Several fruitful research fields in which the architectures analyzed have obtained a good performance are reviewed. As a result, research gaps have been identified in the literature for several domains of application, thus expecting to inspire new and better forms of knowledge.
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Affiliation(s)
- José F Torres
- Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain
| | - Dalil Hadjout
- Department of Commerce, SADEG Company (Sonelgaz Group), Bejaia, Algeria
| | - Abderrazak Sebaa
- LIMED Laboratory, Faculty of Exact Sciences, University of Bejaia, Bejaia, Algeria
- Higher School of Sciences and Technologies of Computing and Digital, Bejaia, Algeria
| | | | - Alicia Troncoso
- Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain
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44
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Saleem MH, Potgieter J, Arif KM. Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers. PLANTS 2020; 9:plants9101319. [PMID: 33036220 PMCID: PMC7599959 DOI: 10.3390/plants9101319] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 10/03/2020] [Accepted: 10/04/2020] [Indexed: 11/19/2022]
Abstract
Recently, plant disease classification has been done by various state-of-the-art deep learning (DL) architectures on the publicly available/author generated datasets. This research proposed the deep learning-based comparative evaluation for the classification of plant disease in two steps. Firstly, the best convolutional neural network (CNN) was obtained by conducting a comparative analysis among well-known CNN architectures along with modified and cascaded/hybrid versions of some of the DL models proposed in the recent researches. Secondly, the performance of the best-obtained model was attempted to improve by training through various deep learning optimizers. The comparison between various CNNs was based on performance metrics such as validation accuracy/loss, F1-score, and the required number of epochs. All the selected DL architectures were trained in the PlantVillage dataset which contains 26 different diseases belonging to 14 respective plant species. Keras with TensorFlow backend was used to train deep learning architectures. It is concluded that the Xception architecture trained with the Adam optimizer attained the highest validation accuracy and F1-score of 99.81% and 0.9978 respectively which is comparatively better than the previous approaches and it proves the novelty of the work. Therefore, the method proposed in this research can be applied to other agricultural applications for transparent detection and classification purposes.
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Affiliation(s)
- Muhammad Hammad Saleem
- Department of Mechanical and Electrical Engineering, School of Food and Advanced Technology, Massey University, Auckland 0632, New Zealand;
| | - Johan Potgieter
- Massey Agritech Partnership Research Centre, School of Food and Advanced Technology, Massey University, Palmerston North 4442, New Zealand;
| | - Khalid Mahmood Arif
- Department of Mechanical and Electrical Engineering, School of Food and Advanced Technology, Massey University, Auckland 0632, New Zealand;
- Correspondence:
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45
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Swapnarekha H, Behera HS, Nayak J, Naik B. Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review. CHAOS, SOLITONS, AND FRACTALS 2020; 138:109947. [PMID: 32836916 PMCID: PMC7256553 DOI: 10.1016/j.chaos.2020.109947] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 05/26/2020] [Indexed: 05/09/2023]
Abstract
The World Health Organization (WHO) declared novel coronavirus 2019 (COVID-19), an infectious epidemic caused by SARS-CoV-2, as Pandemic in March 2020. It has affected more than 40 million people in 216 countries. Almost in all the affected countries, the number of infected and deceased patients has been enhancing at a distressing rate. As the early prediction can reduce the spread of the virus, it is highly desirable to have intelligent prediction and diagnosis tools. The inculcation of efficient forecasting and prediction models may assist the government in implementing better design strategies to prevent the spread of virus. In this paper, a state-of-the-art analysis of the ongoing machine learning (ML) and deep learning (DL) methods in the diagnosis and prediction of COVID-19 has been done. Moreover, a comparative analysis on the impact of machine learning and other competitive approaches like mathematical and statistical models on COVID-19 problem has been conducted. In this study, some factors such as type of methods(machine learning, deep learning, statistical & mathematical) and the impact of COVID research on the nature of data used for the forecasting and prediction of pandemic using computing approaches has been presented. Finally some important research directions for further research on COVID-19 are highlighted which may facilitate the researchers and technocrats to develop competent intelligent models for the prediction and forecasting of COVID-19 real time data.
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Affiliation(s)
- H Swapnarekha
- Department of Information Technology, Veer Surendra Sai University of Technology (VSSUT), Burla, Sambalpur-768018, Odisha, India
| | - Himansu Sekhar Behera
- Department of Information Technology, Veer Surendra Sai University of Technology (VSSUT), Burla, Sambalpur-768018, Odisha, India
| | - Janmenjoy Nayak
- Department of Computer Science and Engineering, Aditya Institute of Technology and Management (AITAM), Tekkali, Andhra Pradesh 532201, India
| | - Bighnaraj Naik
- Department of Computer Application, Veer Surendra Sai University of Technology (VSSUT), Burla, Sambalpur-768018, Odisha, India
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46
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A Survey of Machine Learning Models in Renewable Energy Predictions. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10175975] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The use of renewable energy to reduce the effects of climate change and global warming has become an increasing trend. In order to improve the prediction ability of renewable energy, various prediction techniques have been developed. The aims of this review are illustrated as follows. First, this survey attempts to provide a review and analysis of machine-learning models in renewable-energy predictions. Secondly, this study depicts procedures, including data pre-processing techniques, parameter selection algorithms, and prediction performance measurements, used in machine-learning models for renewable-energy predictions. Thirdly, the analysis of sources of renewable energy, values of the mean absolute percentage error, and values of the coefficient of determination were conducted. Finally, some possible potential opportunities for future work were provided at end of this survey.
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47
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Hybridizing Deep Learning and Neuroevolution: Application to the Spanish Short-Term Electric Energy Consumption Forecasting. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10165487] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The electric energy production would be much more efficient if accurate estimations of the future demand were available, since these would allow allocating only the resources needed for the production of the right amount of energy required. With this motivation in mind, we propose a strategy, based on neuroevolution, that can be used to this aim. Our proposal uses a genetic algorithm in order to find a sub-optimal set of hyper-parameters for configuring a deep neural network, which can then be used for obtaining the forecasting. Such a strategy is justified by the observation that the performances achieved by deep neural networks are strongly dependent on the right setting of the hyper-parameters, and genetic algorithms have shown excellent search capabilities in huge search spaces. Moreover, we base our proposal on a distributed computing platform, which allows its use on a large time-series. In order to assess the performances of our approach, we have applied it to a large dataset, related to the electric energy consumption registered in Spain over almost 10 years. Experimental results confirm the validity of our proposal since it outperforms all other forecasting techniques to which it has been compared.
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