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Lu J, Yang R, Hu G, Hussien AG. Ameliorated Snake Optimizer-Based Approximate Merging of Disk Wang-Ball Curves. Biomimetics (Basel) 2024; 9:134. [PMID: 38534819 DOI: 10.3390/biomimetics9030134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 02/17/2024] [Accepted: 02/20/2024] [Indexed: 03/28/2024] Open
Abstract
A method for the approximate merging of disk Wang-Ball (DWB) curves based on the modified snake optimizer (BEESO) is proposed in this paper to address the problem of difficulties in the merging of DWB curves. By extending the approximate merging problem for traditional curves to disk curves and viewing it as an optimization problem, an approximate merging model is established to minimize the merging error through an error formulation. Considering the complexity of the model built, a BEESO with better convergence accuracy and convergence speed is introduced, which combines the snake optimizer (SO) and three strategies including bi-directional search, evolutionary population dynamics, and elite opposition-based learning. The merging results and merging errors of numerical examples demonstrate that BEESO is effective in solving approximate merging models, and it provides a new method for the compression and transfer of product shape data in Computer-Aided Geometric Design.
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Affiliation(s)
- Jing Lu
- College of Mathematics and Computer Application, Shangluo University, Shangluo 726000, China
| | - Rui Yang
- Department of Applied Mathematics, Xi'an University of Technology, Xi'an 710054, China
| | - Gang Hu
- Department of Applied Mathematics, Xi'an University of Technology, Xi'an 710054, China
| | - Abdelazim G Hussien
- Department of Computer and Information Science, Linköping University, 58183 Linköping, Sweden
- Faculty of Science, Fayoum University, Faiyum 63514, Egypt
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2
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Houssein EH, Abdalkarim N, Hussain K, Mohamed E. Accurate multilevel thresholding image segmentation via oppositional Snake Optimization algorithm: Real cases with liver disease. Comput Biol Med 2024; 169:107922. [PMID: 38184861 DOI: 10.1016/j.compbiomed.2024.107922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 12/19/2023] [Accepted: 01/01/2024] [Indexed: 01/09/2024]
Abstract
Liver-related diseases significantly contribute to global mortality rates. Accurate segmentation of liver disease from CT scans is essential for early diagnosis and treatment selection, particularly in computer-aided diagnosis (CAD) systems. To address challenges posed by inconsistent liver presence and unclear boundaries, an enhanced Snake Optimization (SO) algorithm is proposed that integrates with opposition-based learning (OBL) called (SO-OBL), proving effective in global optimization and multilevel image segmentation. Experiments using CEC'2022 test functions compare SO-OBL with eleven recent and state-of-the-art metaheuristic algorithms, demonstrating its superior performance. Additionally, an advanced liver disease segmentation model based on SO-OBL incorporates an optimized multilevel thresholding technique, leveraging Otsu's function. Notable segmentation metric results, including FSIM = 0.947, SSIM = 0.941, PSNR = 24.876, MSE = 236.88, and execution time = 0.281, underscore the model's efficiency and potential for accurate diagnosis in CAD systems.
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Affiliation(s)
- Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Nada Abdalkarim
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Kashif Hussain
- Department of Science and Engineering, Solent University, Southampton, United Kingdom.
| | - Ebtsam Mohamed
- Faculty of Computers and Information, Minia University, Minia, Egypt.
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3
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Wahab Sait AR. A Lightweight Diabetic Retinopathy Detection Model Using a Deep-Learning Technique. Diagnostics (Basel) 2023; 13:3120. [PMID: 37835861 PMCID: PMC10572365 DOI: 10.3390/diagnostics13193120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/01/2023] [Accepted: 10/02/2023] [Indexed: 10/15/2023] Open
Abstract
Diabetic retinopathy (DR) is a severe complication of diabetes. It affects a large portion of the population of the Kingdom of Saudi Arabia. Existing systems assist clinicians in treating DR patients. However, these systems entail significantly high computational costs. In addition, dataset imbalances may lead existing DR detection systems to produce false positive outcomes. Therefore, the author intended to develop a lightweight deep-learning (DL)-based DR-severity grading system that could be used with limited computational resources. The proposed model followed an image pre-processing approach to overcome the noise and artifacts found in fundus images. A feature extraction process using the You Only Look Once (Yolo) V7 technique was suggested. It was used to provide feature sets. The author employed a tailored quantum marine predator algorithm (QMPA) for selecting appropriate features. A hyperparameter-optimized MobileNet V3 model was utilized for predicting severity levels using images. The author generalized the proposed model using the APTOS and EyePacs datasets. The APTOS dataset contained 5590 fundus images, whereas the EyePacs dataset included 35,100 images. The outcome of the comparative analysis revealed that the proposed model achieved an accuracy of 98.0 and 98.4 and an F1 Score of 93.7 and 93.1 in the APTOS and EyePacs datasets, respectively. In terms of computational complexity, the proposed DR model required fewer parameters, fewer floating-point operations (FLOPs), a lower learning rate, and less training time to learn the key patterns of the fundus images. The lightweight nature of the proposed model can allow healthcare centers to serve patients in remote locations. The proposed model can be implemented as a mobile application to support clinicians in treating DR patients. In the future, the author will focus on improving the proposed model's efficiency to detect DR from low-quality fundus images.
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Affiliation(s)
- Abdul Rahaman Wahab Sait
- Department of Documents and Archive, Center of Documents and Administrative Communication, King Faisal University, P.O. Box 400, Hofuf 31982, Al-Ahsa, Saudi Arabia
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4
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Fu Q, Li Q, Li X. An improved multi-objective marine predator algorithm for gene selection in classification of cancer microarray data. Comput Biol Med 2023; 160:107020. [PMID: 37196457 DOI: 10.1016/j.compbiomed.2023.107020] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 04/09/2023] [Accepted: 05/05/2023] [Indexed: 05/19/2023]
Abstract
Gene selection (GS) is an important branch of interest within the field of feature selection, which is widely used in cancer classification. It provides essential insights into the pathogenesis of cancer and enables a deeper understanding of cancer data. In cancer classification, GS is essentially a multi-objective optimization problem, which aims to simultaneously optimize the two objectives of classification accuracy and the size of the gene subset. The marine predator algorithm (MPA) has been successfully employed in practical applications, however, its random initialization can lead to blindness, which may adversely affect the convergence of the algorithm. Furthermore, the elite individuals in guiding evolution are randomly chosen from the Pareto solutions, which may degrade the good exploration performance of the population. To overcome these limitations, a multi-objective improved MPA with continuous mapping initialization and leader selection strategies is proposed. In this work, a new continuous mapping initialization with ReliefF overwhelms the defects with less information in late evolution. Moreover, an improved elite selection mechanism with Gaussian distribution guides the population to evolve towards a better Pareto front. Finally, an efficient mutation method is adopted to prevent evolutionary stagnation. To evaluate its effectiveness, the proposed algorithm was compared with 9 famous algorithms. The experimental results on 16 datasets demonstrate that the proposed algorithm can significantly reduce the data dimension and obtain the highest classification accuracy on most of high-dimension cancer microarray datasets.
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Affiliation(s)
- Qiyong Fu
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
| | - Qi Li
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
| | - Xiaobo Li
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China.
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5
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Al-Betar MA, Awadallah MA, Makhadmeh SN, Alyasseri ZAA, Al-Naymat G, Mirjalili S. Marine Predators Algorithm: A Review. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:3405-3435. [PMID: 37260911 PMCID: PMC10115392 DOI: 10.1007/s11831-023-09912-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 03/05/2023] [Indexed: 06/02/2023]
Abstract
Marine Predators Algorithm (MPA) is a recent nature-inspired optimizer stemmed from widespread foraging mechanisms based on Lévy and Brownian movements in ocean predators. Due to its superb features, such as derivative-free, parameter-less, easy-to-use, flexible, and simplicity, MPA is quickly evolved for a wide range of optimization problems in a short period. Therefore, its impressive characteristics inspire this review to analyze and discuss the primary MPA research studies established. In this review paper, the growth of the MPA is analyzed based on 102 research papers to show its powerful performance. The MPA inspirations and its theoretical concepts are also illustrated, focusing on its convergence behaviour. Thereafter, the MPA versions suggested improving the MPA behaviour on connecting the search space shape of real-world optimization problems are analyzed. A plethora and diverse optimization applications have been addressed, relying on MPA as the main solver, which is also described and organized. In addition, a critical discussion about the convergence behaviour and the main limitation of MPA is given. The review is end-up highlighting the main findings of this survey and suggests some possible MPA-related improvements and extensions that can be carried out in the future.
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Affiliation(s)
- Mohammed Azmi Al-Betar
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
- Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Al-Huson, Irbid, Jordan
| | - Mohammed A. Awadallah
- Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab Emirates
| | - Sharif Naser Makhadmeh
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
| | - Zaid Abdi Alkareem Alyasseri
- Information Technology Research and Development Center (ITRDC), University of Kufa, An Najaf, 54001 Iraq
- College of Engineering, University of Warith Al-Anbiyaa, Karbalä’, Iraq
| | - Ghazi Al-Naymat
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
| | - Seyedali Mirjalili
- Center for Artificial Intelligence Research and Optimization, Torrens University, Adelaide, Australia
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Torse DA, Khanai R, Pai K, Iyer S, Mavinkattimath S, Kallimani R, Shahpur S. Optimal feature selection for COVID-19 detection with CT images enabled by metaheuristic optimization and artificial intelligence. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-31. [PMID: 37362744 PMCID: PMC10025793 DOI: 10.1007/s11042-023-15031-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 08/09/2022] [Accepted: 02/27/2023] [Indexed: 06/28/2023]
Abstract
There is a broad range of novel Coronaviruses (CoV) such as the common cold, cough, and severe lung infections. The mutation of this virus, which originally started as COVID-19 in Wuhan, China, has continued the rapid spread globally. As the mutated form of this virus spreads across the world, testing and screening procedures of patients have become tedious for healthcare departments in largely populated countries such as India. To diagnose COVID-19 pneumonia by radiological methods, high-resolution computed tomography (CT) of the chest has been considered the most precise method of examination. The use of modern artificial intelligence (AI) techniques on chest high-resolution computed tomography (HRCT) images can help to detect the disease, especially in remote areas with a lack of specialized physicians. This article presents a novel metaheuristic algorithm for automatic COVID-19 detection using a least square support vector machine (LSSVM) classifier for three classes namely normal, COVID, and pneumonia. The proposed model results in a classification accuracy of 87.2% and an F1-score of 86.3% for multiclass classifications from simulations. The analysis of information transfer rate (ITR) revealed that the modified quantum-based marine predators algorithm (Mq-MPA) feature selection algorithm reduces the classification time of LSSVM by 23% when compared to the deep learning models.
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Affiliation(s)
- Dattaprasad A. Torse
- Department of ECE, KLE Dr. M.S. Sheshgiri College of Engineering and Technology, Udyambag, Belagavi, KA 590008 India
| | - Rajashri Khanai
- Department of CSE, KLE Dr. M.S. Sheshgiri College of Engineering and Technology, Udyambag, Belagavi, KA 590008 India
| | - Krishna Pai
- Department of ECE, KLE Dr. M.S. Sheshgiri College of Engineering and Technology, Udyambag, Belagavi, KA 590008 India
| | - Sridhar Iyer
- Department of ECE, KLE Dr. M.S. Sheshgiri College of Engineering and Technology, Udyambag, Belagavi, KA 590008 India
| | - Swati Mavinkattimath
- Department of ECE, KLE Dr. M.S. Sheshgiri College of Engineering and Technology, Udyambag, Belagavi, KA 590008 India
| | - Rakhee Kallimani
- Department of EEE, KLE Dr. M.S. Sheshgiri College of Engineering and Technology, Udyambag, Belagavi, KA 590008 India
| | - Salma Shahpur
- Department of ECE, Jain College of Engineering, Belagavi, KA 590008 India
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7
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Rai R, Dhal KG, Das A, Ray S. An Inclusive Survey on Marine Predators Algorithm: Variants and Applications. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:3133-3172. [PMID: 36855410 PMCID: PMC9951854 DOI: 10.1007/s11831-023-09897-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 02/08/2023] [Indexed: 05/13/2023]
Abstract
Marine Predators Algorithm (MPA) is the existing population-based meta-heuristic algorithms that falls under the category of Nature-Inspired Optimization Algorithm (NIOA) enthused by the foraging actions of the marine predators that principally pursues Levy or Brownian approach as its foraging strategy. Furthermore, it employs the optimal encounter rate stratagem involving both the predator as well as prey. Since its introduction by Faramarzi in the year 2020, MPA has gained enormous popularity and has been employed in numerous application areas ranging from Mathematical and Engineering Optimization problems to Fog Computing to Image Processing to Photovoltaic System to Wind-Solar Generation System for resolving continuous optimization problems. Such huge interest from the research fraternity or the massive recognition of MPA is due to several factors such as its simplicity, ease of application, realistic execution time, superior convergence acceleration rate, soaring effectiveness, its ability to unravel continuous, multi-objective and binary problems when compared with other renowned optimization algorithms existing in the literature. This paper offers a detailed summary of the Marine Predators Algorithm (MPA) and its variants. Furthermore, the applications of MPA in a number of spheres such as Image processing, classification, electrical power system, Photovoltaic models, structural damage detection, distribution networks, engineering applications, Task Scheduling, optimization problems etc., are illustrated. To conclude, the paper highlights and thereby advocates few of the potential future research directions for MPA.
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Affiliation(s)
- Rebika Rai
- Department of Computer Applications, Sikkim University, Sikkim, India
| | - Krishna Gopal Dhal
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal India
| | - Arunita Das
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal India
| | - Swarnajit Ray
- Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal India
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8
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Zhang S, Wang S, Dong R, Zhang K, Zhang X. A Multi-strategy Improved Outpost and Differential Evolution Mutation Marine Predators Algorithm for Global Optimization. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023; 48:1-24. [PMID: 36845881 PMCID: PMC9937532 DOI: 10.1007/s13369-023-07683-2] [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: 08/04/2022] [Accepted: 01/29/2023] [Indexed: 02/20/2023]
Abstract
Marine Predators Algorithm (MPA) is a recent efficient metaheuristic algorithm that is enlightened by the biological behavior of ocean predators and prey. This algorithm simulates the Levy and Brownian movements of prevalent foraging strategy and has been applied to many complex optimization problems. However, the algorithm has defects such as a low diversity of the solutions, ease into the local optimal solutions, and decreasing convergence speed in dealing with complex problems. A modified version of this algorithm called ODMPA is proposed based on the tent map, the outpost mechanism, and the differential evolution mutation with simulated annealing (DE-SA) mechanism. The tent map and DE-SA mechanism are added to enhance the exploration capability of MPA by increasing the diversity of the search agents, and the outpost mechanism is mainly used to improve the convergence speed of MPA. To validate the outstanding performance of the ODMPA, a series of global optimization problems are selected as the test sets, including the standard IEEE CEC2014 benchmark functions, which are the authoritative test set, three well-known engineering problems, and photovoltaic model parameters tasks. Compared with some famous algorithms, the results reveal that ODMPA has achieved better performance than its counterparts in CEC2014 benchmark functions. And in solving real-world optimization problems, ODMPA could get higher accuracy than other metaheuristic algorithms. These practical results demonstrate that the mechanisms introduced positively affect the original MPA, and the proposed ODMPA can be a widely effective tool in tackling many optimization problems.
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Affiliation(s)
- Shuhan Zhang
- College of Computer Science and Technology, Jilin University, Changchun, 130012 China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012 China
| | - Shengsheng Wang
- College of Computer Science and Technology, Jilin University, Changchun, 130012 China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012 China
| | - Ruyi Dong
- College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, 132022 China
| | - Kai Zhang
- College of Computer Science and Technology, Jilin University, Changchun, 130012 China
| | - Xiaohui Zhang
- 2012 Laboratories, Huawei Technology Co., Ltd., Beijing, 100095 China
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9
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Ma Y, Chang C, Lin Z, Zhang X, Song J, Chen L. Modified Marine Predators Algorithm hybridized with teaching-learning mechanism for solving optimization problems. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:93-127. [PMID: 36650759 DOI: 10.3934/mbe.2023006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Marine Predators Algorithm (MPA) is a newly nature-inspired meta-heuristic algorithm, which is proposed based on the Lévy flight and Brownian motion of ocean predators. Since the MPA was proposed, it has been successfully applied in many fields. However, it includes several shortcomings, such as falling into local optimum easily and precocious convergence. To balance the exploitation and exploration ability of MPA, a modified marine predators algorithm hybridized with teaching-learning mechanism is proposed in this paper, namely MTLMPA. Compared with MPA, the proposed MTLMPA has two highlights. Firstly, a kind of teaching mechanism is introduced in the first phase of MPA to improve the global searching ability. Secondly, a novel learning mechanism is introduced in the third phase of MPA to enhance the chance encounter rate between predator and prey and to avoid premature convergence. MTLMPA is verified by 23 benchmark numerical testing functions and 29 CEC-2017 testing functions. Experimental results reveal that the MTLMPA is more competitive compared with several state-of-the-art heuristic optimization algorithms.
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Affiliation(s)
- Yunpeng Ma
- School of Information Engineering, Tianjin University of Commerce, Beichen, Tianjin 300134
| | - Chang Chang
- College of Science, Tianjin University of Commerce, Beichen, Tianjin 300134
| | - Zehua Lin
- College of Science, Tianjin University of Commerce, Beichen, Tianjin 300134
| | - Xinxin Zhang
- School of Information Engineering, Tianjin University of Commerce, Beichen, Tianjin 300134
| | - Jiancai Song
- School of Information Engineering, Tianjin University of Commerce, Beichen, Tianjin 300134
| | - Lei Chen
- School of Information Engineering, Tianjin University of Commerce, Beichen, Tianjin 300134
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Qin X, Zhang S, Dong X, Zhan Y, Wang R, Xu D. China's carbon dioxide emission forecast based on improved marine predator algorithm and multi-kernel support vector regression. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:5730-5748. [PMID: 35982382 PMCID: PMC9387893 DOI: 10.1007/s11356-022-22302-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 07/26/2022] [Indexed: 05/27/2023]
Abstract
Global warming has constituted a major global problem. Carbon dioxide emissions from the burning of fossil fuels are the main cause of global warming. Therefore, carbon dioxide emission forecasting has attracted widespread attention. Aiming at the problem of carbon dioxide emissions forecasting, this paper proposes a new hybrid forecasting model of carbon dioxide emissions, which combines the marine predator algorithm (MPA) and multi-kernel support vector regression. For further strengthening the prediction accuracy, a novel variant of MPA is proposed, called EGMPA, which introduces the elite opposition-based learning strategy and the golden sine algorithm into MPA. Algorithm test results show that EGMPA can effectively improve the convergence speed and optimization accuracy. The carbon dioxide emission data of China from 1965 to 2020 are taken as the research objects. Root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the performance of the proposed model. The proposed multi-kernel support vector regression model is used to forecast China's carbon dioxide emissions during the "14th Five-Year Plan" period. The results show that the proposed model has RMSE of 37.43 Mt, MAE of 30.63 Mt, and MAPE of 0.32%, which significantly improves the prediction accuracy and can accurately and effectively predict China's carbon dioxide emissions. During the "14th Five-Year Plan" period, China's carbon dioxide emissions will continue to show an increasing trend, but the growth rate will slow down significantly.
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Affiliation(s)
- Xiwen Qin
- School of Mathematics and Statistics, Changchun University of Technology, No. 2055 Yan'an Street, Chaoyang District, Changchun, 130012, China.
- Graduate School, Changchun University of Technology, No. 2055 Yan'an Street, Chaoyang District, Changchun, 130012, China.
| | - Siqi Zhang
- School of Mathematics and Statistics, Changchun University of Technology, No. 2055 Yan'an Street, Chaoyang District, Changchun, 130012, China
| | - Xiaogang Dong
- School of Mathematics and Statistics, Changchun University of Technology, No. 2055 Yan'an Street, Chaoyang District, Changchun, 130012, China
| | - Yichang Zhan
- School of Mathematics and Statistics, Changchun University of Technology, No. 2055 Yan'an Street, Chaoyang District, Changchun, 130012, China
| | - Rui Wang
- School of Mathematics and Statistics, Changchun University of Technology, No. 2055 Yan'an Street, Chaoyang District, Changchun, 130012, China
| | - Dingxin Xu
- School of Mathematics and Statistics, Changchun University of Technology, No. 2055 Yan'an Street, Chaoyang District, Changchun, 130012, China
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11
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Elaziz MA, Ewees AA, Al-qaness MAA, Alshathri S, Ibrahim RA. Feature Selection for High Dimensional Datasets Based on Quantum-Based Dwarf Mongoose Optimization. MATHEMATICS 2022; 10:4565. [DOI: 10.3390/math10234565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Feature selection (FS) methods play essential roles in different machine learning applications. Several FS methods have been developed; however, those FS methods that depend on metaheuristic (MH) algorithms showed impressive performance in various domains. Thus, in this paper, based on the recent advances in MH algorithms, we introduce a new FS technique to modify the performance of the Dwarf Mongoose Optimization (DMO) Algorithm using quantum-based optimization (QBO). The main idea is to utilize QBO as a local search of the traditional DMO to avoid its search limitations. So, the developed method, named DMOAQ, benefits from the advantages of the DMO and QBO. It is tested with well-known benchmark and high-dimensional datasets, with comprehensive comparisons to several optimization methods, including the original DMO. The evaluation outcomes verify that the DMOAQ has significantly enhanced the search capability of the traditional DMO and outperformed other compared methods in the evaluation experiments.
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Xiao Y, Guo Y, Cui H, Wang Y, Li J, Zhang Y. IHAOAVOA: An improved hybrid aquila optimizer and African vultures optimization algorithm for global optimization problems. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:10963-11017. [PMID: 36124577 DOI: 10.3934/mbe.2022512] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) are two newly developed meta-heuristic algorithms that simulate several intelligent hunting behaviors of Aquila and African vulture in nature, respectively. AO has powerful global exploration capability, whereas its local exploitation phase is not stable enough. On the other hand, AVOA possesses promising exploitation capability but insufficient exploration mechanisms. Based on the characteristics of both algorithms, in this paper, we propose an improved hybrid AO and AVOA optimizer called IHAOAVOA to overcome the deficiencies in the single algorithm and provide higher-quality solutions for solving global optimization problems. First, the exploration phase of AO and the exploitation phase of AVOA are combined to retain the valuable search competence of each. Then, a new composite opposition-based learning (COBL) is designed to increase the population diversity and help the hybrid algorithm escape from the local optima. In addition, to more effectively guide the search process and balance the exploration and exploitation, the fitness-distance balance (FDB) selection strategy is introduced to modify the core position update formula. The performance of the proposed IHAOAVOA is comprehensively investigated and analyzed by comparing against the basic AO, AVOA, and six state-of-the-art algorithms on 23 classical benchmark functions and the IEEE CEC2019 test suite. Experimental results demonstrate that IHAOAVOA achieves superior solution accuracy, convergence speed, and local optima avoidance than other comparison methods on most test functions. Furthermore, the practicality of IHAOAVOA is highlighted by solving five engineering design problems. Our findings reveal that the proposed technique is also highly competitive and promising when addressing real-world optimization tasks. The source code of the IHAOAVOA is publicly available at https://doi.org/10.24433/CO.2373662.v1.
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Affiliation(s)
- Yaning Xiao
- College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
| | - Yanling Guo
- College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
| | - Hao Cui
- College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
| | - Yangwei Wang
- College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
| | - Jian Li
- College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
| | - Yapeng Zhang
- College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
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Houssein EH, Helmy BED, Oliva D, Jangir P, Premkumar M, Elngar AA, Shaban H. An efficient multi-thresholding based COVID-19 CT images segmentation approach using an improved equilibrium optimizer. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103401] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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