1
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Singh S, Singh H, Mittal N, Singh S, Askar SS, Alshamrani AM, Abouhawwash M. An efficient multi-level thresholding method for breast thermograms analysis based on an improved BWO algorithm. BMC Med Imaging 2024; 24:191. [PMID: 39080591 PMCID: PMC11290159 DOI: 10.1186/s12880-024-01361-x] [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: 03/20/2024] [Accepted: 07/09/2024] [Indexed: 08/02/2024] Open
Abstract
Breast cancer is a prevalent disease and the second leading cause of death in women globally. Various imaging techniques, including mammography, ultrasonography, X-ray, and magnetic resonance, are employed for detection. Thermography shows significant promise for early breast disease detection, offering advantages such as being non-ionizing, non-invasive, cost-effective, and providing real-time results. Medical image segmentation is crucial in image analysis, and this study introduces a thermographic image segmentation algorithm using the improved Black Widow Optimization Algorithm (IBWOA). While the standard BWOA is effective for complex optimization problems, it has issues with stagnation and balancing exploration and exploitation. The proposed method enhances exploration with Levy flights and improves exploitation with quasi-opposition-based learning. Comparing IBWOA with other algorithms like Harris Hawks Optimization (HHO), Linear Success-History based Adaptive Differential Evolution (LSHADE), and the whale optimization algorithm (WOA), sine cosine algorithm (SCA), and black widow optimization (BWO) using otsu and Kapur's entropy method. Results show IBWOA delivers superior performance in both qualitative and quantitative analyses including visual inspection and metrics such as fitness value, threshold values, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index (FSIM). Experimental results demonstrate the outperformance of the proposed IBWOA, validating its effectiveness and superiority.
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Affiliation(s)
- Simrandeep Singh
- Department of Electronics & Communication Engineering, UCRD, Chandigarh University, Gharuan, Punjab, India
| | - Harbinder Singh
- VISILAB, Universidad de Castilla-La Mancha, Ciudad Real, 13071, Spain
| | - Nitin Mittal
- Skill Faculty of Engineering and Technology, Shri Vishwakarma Skill University, Palwal, 121102, India.
| | - Supreet Singh
- School of Computer Science, UPES, Dehradun, Uttarakhand, India
| | - S S Askar
- Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia
| | - Ahmad M Alshamrani
- Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia
| | - Mohamed Abouhawwash
- Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
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2
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Salgotra R, Lamba AK, Talwar D, Gulati D, Gandomi AH. A hybrid swarm intelligence algorithm for region-based image fusion. Sci Rep 2024; 14:13723. [PMID: 38877014 PMCID: PMC11178836 DOI: 10.1038/s41598-024-63746-w] [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: 10/17/2023] [Accepted: 05/31/2024] [Indexed: 06/16/2024] Open
Abstract
This paper proposes a novel multi-hybrid algorithm named DHPN, using the best-known properties of dwarf mongoose algorithm (DMA), honey badger algorithm (HBA), prairie dog optimizer (PDO), cuckoo search (CS), grey wolf optimizer (GWO) and naked mole rat algorithm (NMRA). It follows an iterative division for extensive exploration and incorporates major parametric enhancements for improved exploitation operation. To counter the local optima problems, a stagnation phase using CS and GWO is added. Six new inertia weight operators have been analyzed to adapt algorithmic parameters, and the best combination of these parameters has been found. An analysis of the suitability of DHPN towards population variations and higher dimensions has been performed. For performance evaluation, the CEC 2005 and CEC 2019 benchmark data sets have been used. A comparison has been performed with differential evolution with active archive (JADE), self-adaptive DE (SaDE), success history based DE (SHADE), LSHADE-SPACMA, extended GWO (GWO-E), jDE100, and others. The DHPN algorithm is also used to solve the image fusion problem for four fusion quality metrics, namely, edge-based similarity index ( Q A B / F ), sum of correlation difference (SCD), structural similarity index measure (SSIM), and artifact measure ( N A B / F ). The averageQ A B / F = 0.765508 , S C D = 1.63185 , S S I M = 0.726317 , andN A B / F = 0.006617 shows the best combination of results obtained by DHPN with respect to the existing algorithms such as DCH, CBF, GTF, JSR and others. Experimental and statistical Wilcoxon's and Friedman's tests show that the proposed DHPN algorithm performs significantly better in comparison to the other algorithms under test.
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Affiliation(s)
- Rohit Salgotra
- Faculty of Physics and Applied Computer Science, AGH University of Science & Technology, Kraków, Poland
- MEU Research Unit, Middle East University, Amman, Jordan
- Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, 2007, Australia
| | - Amanjot Kaur Lamba
- Department of Electronics and Communication Engineering, Punjab Engineering College (Deemed-to-be University), Chandigarh, India
| | - Dhruv Talwar
- Department of Electronics and Communication Engineering, Punjab Engineering College (Deemed-to-be University), Chandigarh, India
| | - Dhairya Gulati
- Department of Electronics and Communication Engineering, Punjab Engineering College (Deemed-to-be University), Chandigarh, India
| | - Amir H Gandomi
- Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, 2007, Australia.
- University Research and Innovation Center (EKIK), Óbuda University, Budapest, 1034, Hungary.
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3
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Zhang K, He Y, Wang Y, Sun C. Improved Multi-Strategy Sand Cat Swarm Optimization for Solving Global Optimization. Biomimetics (Basel) 2024; 9:280. [PMID: 38786490 PMCID: PMC11118958 DOI: 10.3390/biomimetics9050280] [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: 04/01/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/25/2024] Open
Abstract
The sand cat swarm optimization algorithm (SCSO) is a novel metaheuristic algorithm that has been proposed in recent years. The algorithm optimizes the search ability of individuals by mimicking the hunting behavior of sand cat groups in nature, thereby achieving robust optimization performance. It is characterized by few control parameters and simple operation. However, due to the lack of population diversity, SCSO is less efficient in solving complex problems and is prone to fall into local optimization. To address these shortcomings and refine the algorithm's efficacy, an improved multi-strategy sand cat optimization algorithm (IMSCSO) is proposed in this paper. In IMSCSO, a roulette fitness-distance balancing strategy is used to select codes to replace random agents in the exploration phase and enhance the convergence performance of the algorithm. To bolster population diversity, a novel population perturbation strategy is introduced, aiming to facilitate the algorithm's escape from local optima. Finally, a best-worst perturbation strategy is developed. The approach not only maintains diversity throughout the optimization process but also enhances the algorithm's exploitation capabilities. To evaluate the performance of the proposed IMSCSO, we conducted experiments in the CEC 2017 test suite and compared IMSCSO with seven other algorithms. The results show that the IMSCSO proposed in this paper has better optimization performance.
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Affiliation(s)
- Kuan Zhang
- College of Information Science and Technology, Northeastern University, Shenyang 110000, China; (K.Z.); (Y.H.)
- School of Aerospace, Harbin Institute of Technology, Harbin 150001, China
| | - Yirui He
- College of Information Science and Technology, Northeastern University, Shenyang 110000, China; (K.Z.); (Y.H.)
| | - Yuhang Wang
- School of Software, Henan University, Kaifeng 475001, China;
| | - Changjian Sun
- College of Electronic Science and Engineering, Jilin University, Changchun 130000, China
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4
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Salgotra R, Gandomi AH. A novel multi-hybrid differential evolution algorithm for optimization of frame structures. Sci Rep 2024; 14:4877. [PMID: 38418500 PMCID: PMC10902366 DOI: 10.1038/s41598-024-54384-3] [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: 09/22/2023] [Accepted: 02/12/2024] [Indexed: 03/01/2024] Open
Abstract
Differential evolution (DE) is a robust optimizer designed for solving complex domain research problems in the computational intelligence community. In the present work, a multi-hybrid DE (MHDE) is proposed for improving the overall working capability of the algorithm without compromising the solution quality. Adaptive parameters, enhanced mutation, enhanced crossover, reducing population, iterative division and Gaussian random sampling are some of the major characteristics of the proposed MHDE algorithm. Firstly, an iterative division for improved exploration and exploitation is used, then an adaptive proportional population size reduction mechanism is followed for reducing the computational complexity. It also incorporated Weibull distribution and Gaussian random sampling to mitigate premature convergence. The proposed framework is validated by using IEEE CEC benchmark suites (CEC 2005, CEC 2014 and CEC 2017). The algorithm is applied to four engineering design problems and for the weight minimization of three frame design problems. Experimental results are analysed and compared with recent hybrid algorithms such as laplacian biogeography based optimization, adaptive differential evolution with archive (JADE), success history based DE, self adaptive DE, LSHADE, MVMO, fractional-order calculus-based flower pollination algorithm, sine cosine crow search algorithm and others. Statistically, the Friedman and Wilcoxon rank sum tests prove that the proposed algorithm fares better than others.
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Affiliation(s)
- Rohit Salgotra
- Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Kraków, Poland.
- MEU Research Unit, Middle East University, Amman, Jordan.
| | - Amir H Gandomi
- Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, 2007, Australia.
- University Research and Innovation Center (EKIK), Óbuda University, 1034, Budapest, Hungary.
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5
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Singh S, Singh U, Mittal N, Gared F. A self-adaptive attraction and repulsion-based naked mole-rat algorithm for energy-efficient mobile wireless sensor networks. Sci Rep 2024; 14:1040. [PMID: 38200166 PMCID: PMC10781696 DOI: 10.1038/s41598-024-51218-0] [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: 07/27/2023] [Accepted: 01/02/2024] [Indexed: 01/12/2024] Open
Abstract
Naked mole-rat algorithm (NMRA) is a swarm intelligence-based algorithm that draws inspiration from the mating behaviour of mole rats (workers and breeders). This approach, which is based on the ability of breeders to reproduce with the queen, has been utilized to tackle optimization problems. The algorithm, however, suffers from local optima stagnation problem and a slower rate of convergence in order to provide gobal optimal solution. This study suggests attraction and repulsion strategy based NMRA (ARNMRA) along with self-adaptive properties to avoid trapping of solution in local optima. This strategy is utilized to create new breeder rat solutions and mating factor [Formula: see text] is made self-adaptive using simulated annealing (sa) based mutation operator. ARNMRA is evaluated on CEC 2005 numerical benchmark problems and found to be superior to other algorithms, including well-known ones like selective operation based GWO (SOGWO), opposition based laplacian equilibrium optimizer (OB-L-EO), improved whale optimization algorithm (IWOA), success-history based adaptive DE (SHADE) and original NMRA. Further, according to experimental results, the performance of ARNMRA is likewise superior to the NMRA for the CEC 2019 and CEC 2020 numerical problems. Convergence profiles and statistical tests (rank-sum test and Friedman test) are employed further to validate the experimental results. Moreover, this article extends the application of ARNMRA to address the data gathering aspect in mobile wireless sensor networks (MWSNs) with the goal of prolonging network lifetime and enhancing energy efficiency. In this MWSN-based protocol, a sensor node is elected as a cluster head based on factors like mobility, residual energy, and connection time. The protocol aims to maximize the system lifetime by efficiently collecting data from all sensors and transmitting it to the base station. The study emphasizes the significance of considering dynamic node densities and speed when designing effective data-gathering protocols for MWSNs.
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Affiliation(s)
- Supreet Singh
- Department of ECE, Thapar Institute of Engineering and Technology, Patiala, India
- Department of CSE, Faculty of Engineering and Technology, SGT University, Gurugram, India
| | - Urvinder Singh
- Department of ECE, Thapar Institute of Engineering and Technology, Patiala, India
| | - Nitin Mittal
- Skill Faculty of Engineering and Technology, Shri Viswakarma Skill University, Dudhola, Haryana, India
| | - Fikreselam Gared
- Faculty of Electrical and Computer Engineering, Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, Ethiopia.
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6
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Singh H, Mittal N, Gupta A, Singh P, Gared F. End fire linear antenna array synthesis using differential evolution inspired Adaptive Naked Mole Rat Algorithm. Sci Rep 2023; 13:12308. [PMID: 37516755 PMCID: PMC10387104 DOI: 10.1038/s41598-023-39509-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 07/26/2023] [Indexed: 07/31/2023] Open
Abstract
Linear antenna arrays (LAAs) play a critical role in smart system communication applications such as the Internet of Things (IoT), mobile communication and beamforming. However, minimizing secondary lobes while maintaining a low beamwidth remains challenging. This study presents an enhanced synthesis methodology for LAAs using the Adaptive Naked Mole Rat Algorithm (ANMRA). ANMRA, inspired by mole-rat mating habits, improves exploration and exploitation capabilities for directive LAA applications. The performance of ANMRA is assessed using the CEC 2019 benchmark test functions, a widely adopted standard for statistical evaluation in optimization algorithms. The proposed methodology results are also benchmarked against state-of-the-art algorithms, including the Salp Swarm Algorithm (SSA), Cuckoo Search (CS), Artificial Hummingbird Algorithm (AHOA), Chimp Optimization Algorithm (ChOA), and Naked Mole Rat Algorithm (NMRA). The results demonstrate that ANMRA achieves superior performance among the benchmarked algorithms by successfully minimizing secondary lobes and obtaining a narrow beamwidth. The ANMRA controlled design achieves the lowest Side Lobe Level (SLL) of - 37.08 dB and the smallest beamwidth of 74.68°. The statistical assessment using the benchmark test functions further confirms the effectiveness of ANMRA. By optimizing antenna element magnitude and placement control, ANMRA enables precise primary lobe placement, grating lobe elimination, and high directivity in LAAs. This research contributes to advancing smart system communication technologies, particularly in the context of IoT and beamforming applications, by providing an enhanced synthesis methodology for LAAs that offers improved performance in terms of secondary lobe reduction and beamwidth optimization.
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Affiliation(s)
- Harbinder Singh
- Department of Electronics and Communication Engineering, UCRD, Chandigarh University, Punjab, India
| | - Nitin Mittal
- Skill Faculty of Engineering and Technology, Shri Vishwakarma Skill University, Dudhola, Haryana, India
| | - Amit Gupta
- Department of Electronics and Communication Engineering, I.K Gujral Punjab Technical University, Punjab, India
| | - Pratap Singh
- Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India
| | - Fikreselam Gared
- Faculty of Electrical and Computer Engineering, Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, Ethiopia.
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7
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Rajwar K, Deep K, Das S. An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges. Artif Intell Rev 2023; 56:1-71. [PMID: 37362893 PMCID: PMC10103682 DOI: 10.1007/s10462-023-10470-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2023]
Abstract
As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called 'novel' if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on 'novel ideas', so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.
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Affiliation(s)
- Kanchan Rajwar
- Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667 India
| | - Kusum Deep
- Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667 India
| | - Swagatam Das
- Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal 700108 India
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8
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Elbeltagi A, Seifi A, Ehteram M, Zerouali B, Vishwakarma DK, Pandey K. GLUE analysis of meteorological-based crop coefficient predictions to derive the explicit equation. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08466-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
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9
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Zamli KZ, Din F, Alhadawi HS. Exploring a Q-learning-based chaotic naked mole rat algorithm for S-box construction and optimization. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08243-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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10
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Mohamadi S, Sheikh Khozani Z, Ehteram M, Ahmed AN, El-Shafie A. Rainfall prediction using multiple inclusive models and large climate indices. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:85312-85349. [PMID: 35790639 DOI: 10.1007/s11356-022-21727-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 06/25/2022] [Indexed: 06/15/2023]
Abstract
Rainfall prediction is vital for the management of available water resources. Accordingly, this study used large lagged climate indices to predict rainfall in Iran's Sefidrood basin. A radial basis function neural network (RBFNN) and a multilayer perceptron (MLP) network were used to predict monthly rainfall. The models were trained using the naked mole rat (NMR) algorithm, firefly algorithm (FFA), genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. Large lagged climate indices, as well as three hybrid models, i.e., inclusive multiple model (IMM)-MLP, IMM-RBFNN, and the simple average method (SAM), were then employed to predict rainfall. This paper aims to predict rainfall using large climate indices, ensemble models, and optimized artificial neural network models. Also, the paper considers the uncertainty resources in the modeling process. The inputs were selected using a new input selection method, namely a hybrid gamma test (GT). The GT was integrated with the NMR algorithm to create a new test for determining the best input scenario. Therefore, the main innovations of this study were the introduction of the new ensemble and the new hybrid GT, as well as the new MLP and RBFNN models. The introduced ensemble models of the current study are not only useful for rainfall prediction but also can be used to predict other metrological parameters. The uncertainty of the model parameters and input data were also analysed. It was found that the IMM-MLP model reduced the root mean square error (RMSE) of the IMM-RBFNN, SAM, MLP-NMR, RBFNN-NMR, MLP-FFA, RBFNN-FFA, MLP-PSO, RBFNN-PSO, MLP-GA, and RBFNN-GA, MLP, and RBFNN models by 12%, 25%, 31%, 55%, 60%, 62%, 66%, 69%, 70%, 71%, 72%, and 72%, respectively. The IMMs, such as the IMM-MLP, IMM-RBFNN, and SAM, outperformed standalone models. The uncertainty bound of the multiple inclusive models was narrower than that of the standalone MLP and RBFNN models. The MLP-NMR model decreased the RMSE of the RBFNN-NMR, RBFNN-FFA, RBFNN-PSO, and RBFNN models by 15%, 26%, 37%, 42%, and 45%, respectively. The proposed ensemble models were robust tools for combining standalone models to predict hydrological variables.
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Affiliation(s)
- Sedigheh Mohamadi
- Department of Ecology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran
| | - Zohreh Sheikh Khozani
- Institute of Structural Mechanics, Bauhaus Universität Weimar, 99423, Weimar, Germany
| | - Mohammad Ehteram
- Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran.
| | - Ali Najah Ahmed
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Selangor, Malaysia
| | - Ahmed El-Shafie
- Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), 50603, Malaysia, Kuala Lumpur
- National Water and Energy Center, United Arab Emirates University, P.O. Box. 15551, Al Ain, United Arab Emirates
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11
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Wu H, Zhang X, Song L, Zhang Y, Gu L, Zhao X. Wild Geese Migration Optimization Algorithm: A New Meta-Heuristic Algorithm for Solving Inverse Kinematics of Robot. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5191758. [PMID: 36337271 PMCID: PMC9630037 DOI: 10.1155/2022/5191758] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 07/21/2022] [Accepted: 08/18/2022] [Indexed: 08/26/2023]
Abstract
This paper proposes a new meta-heuristic algorithm, named wild geese migration optimization (GMO) algorithm. It is inspired by the social behavior of wild geese swarming in nature. They maintain a special formation for long-distance migration in small groups for survival and reproduction. The mathematical model is established based on these social behaviors to solve optimization problems. Meanwhile, the performance of the GMO algorithm is tested on the stable benchmark function of CEC2017, and its potential for dealing with practical problems is studied in five engineering design problems and the inverse kinematics solution of robot. The test results show that the GMO algorithm has excellent computational performance compared to other algorithms. The practical application results show that the GMO algorithm has strong applicability, more accurate optimization results, and more competitiveness in challenging problems with unknown search space, compared with well-known algorithms in the literature. The proposal of GMO algorithm enriches the team of swarm intelligence optimization algorithms and also provides a new solution for solving engineering design problems and inverse kinematics of robots.
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Affiliation(s)
- Honggang Wu
- School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
| | - Xinming Zhang
- School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
- School of Mechatronic Engineering and Automation, Foshan University, Foshan 528225, China
| | - Linsen Song
- School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
| | - Yufei Zhang
- School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
| | - Lidong Gu
- School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
| | - Xiaonan Zhao
- School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
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12
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He J, Peng Z, Cui D, Qiu J, Li Q, Zhang H. Enhanced sooty tern optimization algorithm using multiple search guidance strategies and multiple position update modes for solving optimization problems. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03635-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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13
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Improved Sparrow Algorithm Based on Game Predatory Mechanism and Suicide Mechanism. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4925416. [PMID: 35615547 PMCID: PMC9126709 DOI: 10.1155/2022/4925416] [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: 01/18/2022] [Revised: 03/21/2022] [Accepted: 04/22/2022] [Indexed: 11/17/2022]
Abstract
In order to overcome the defect that sparrow search algorithm converges very fast but is easy to fall into the trap of local optimization, based on the original mechanism of sparrow algorithm, this paper proposes game predatory mechanism and suicide mechanism, which makes sparrow algorithm more in line with its biological characteristics and enhances the ability of the algorithm to get rid of the attraction of local optimization while retaining the advantages of fast convergence speed. By initializing the population with the good point set strategy, the quality of the initial population is guaranteed and the diversity of the population is enhanced. In view of the current situation that the diversity index evaluation does not consider the invalid search caused by individuals beyond the boundary in the search process, an index to measure the invalid search beyond the boundary in the search process is proposed, and the measurement of diversity index is further improved to make it more accurate. The improved algorithm is tested on six basic functions and CEC2017 test function to verify its effectiveness. Finally, the improved algorithm is applied to the three-dimensional path planning of UAV with threat area. The results show that the improved algorithm has stronger optimization performance, has strong competitiveness compared with other algorithms, and can quickly plan the effective and stable path of UAV, which improves an effective method for the application in this field and other fields.
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14
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Bi J, Zhang Y. An improved Henry gas solubility optimization for optimization tasks. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02670-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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15
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Large scale salp-based grey wolf optimization for feature selection and global optimization. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-06921-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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16
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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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Sarkar T, Salauddin M, Mukherjee A, Shariati MA, Rebezov M, Tretyak L, Pateiro M, Lorenzo JM. 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] [Abstract] [Key Words] [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
Abstract
Bio-inspired optimization techniques (BOT) are part of intelligent computing techniques. There are several BOTs available and many new BOTs are evolving in this era of industrial revolution 4.0. Genetic algorithm, particle swarm optimization, artificial bee colony, and grey wolf optimization are the techniques explored by researchers in the field of food processing technology. Although, there are other potential methods that may efficiently solve the optimum related problem in food industries. In this review, the mathematical background of the techniques, their application and the potential microbial-based optimization methods with higher precision has been surveyed for a complete and comprehensive understanding of BOTs along with their mechanism of functioning. These techniques can simulate the process efficiently and able to find the near-to-optimal value expeditiously.
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Affiliation(s)
- Tanmay Sarkar
- Department of Food Processing Technology, Malda Polytechnic, West Bengal State Council of Technical Education, Malda, 732102, West Bengal, India
| | - Molla Salauddin
- Department of Food Processing Technology, Mir Madan Mohanlal Govt. Polytechnic, West Bengal State Council of Technical Education, Nadia 741156, West Bengal, India
| | - Alok Mukherjee
- Government College of Engineering and Ceramic Technology, Kolkata, India
| | - Mohammad Ali Shariati
- Department of Scientific Research, K.G. Razumovsky Moscow State University of Technologies and Management (The First Cossack University), 109004, Moscow, Russian Federation
| | - Maksim Rebezov
- Department of Scientific Research, K.G. Razumovsky Moscow State University of Technologies and Management (The First Cossack University), 109004, Moscow, Russian Federation
- Biophotonics Center, Prokhorov General Physics Institute of the Russian Academy of Science, 119991, Moscow, Russian Federation
- Department of Scientific Research, V. M. Gorbatov Federal Research Center for Food Systems, 109316, Moscow, Russian Federation
| | - Lyudmila Tretyak
- Department of Metrology, Standardization and Certification, Orenburg State University, 460018, Orenburg, Russian Federation
| | - Mirian Pateiro
- Centro Tecnológico de La Carne de Galicia, Rúa Galicia Nº 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900, Ourense, Spain
| | - José M. Lorenzo
- Centro Tecnológico de La Carne de Galicia, Rúa Galicia Nº 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900, Ourense, Spain
- Universidade de Vigo, Área de Tecnoloxía dos Alimentos, Facultade de Ciencias, 32004 Ourense, Spain
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An Efficient Adaptive Salp Swarm Algorithm Using Type II Fuzzy Entropy for Multilevel Thresholding Image Segmentation. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:2794326. [PMID: 35132329 PMCID: PMC8817858 DOI: 10.1155/2022/2794326] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 12/09/2021] [Accepted: 12/16/2021] [Indexed: 11/20/2022]
Abstract
Salp swarm algorithm (SSA) is an innovative contribution to smart swarm algorithms and has shown its utility in a wide range of research domains. While it is an efficient algorithm, it is noted that SSA suffers from several issues, including weak exploitation, convergence, and unstable exploitation and exploration. To overcome these, an improved SSA called as adaptive salp swarm algorithm (ASSA) was proposed. Thresholding is among the most effective image segmentation methods in which the objective function is described in relation of threshold values and their position in the histogram. Only if one threshold is assumed, a segmented image of two groups is obtained. But on other side, several groups in the output image are generated with multilevel thresholds. The methods proposed by authors previously were traditional measures to identify objective functions. However, the basic challenge with thresholding methods is defining the threshold numbers that the individual must choose. In this paper, ASSA, along with type II fuzzy entropy, is proposed. The technique presented is examined in context with multilevel image thresholding, specifically with ASSA. For this reason, the proposed method is tested using various images simultaneously with histograms. For evaluating the performance efficiency of the proposed method, the results are compared, and robustness is tested with the efficiency of the proposed method to multilevel segmentation of image; numerous images are utilized arbitrarily from datasets.
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Singh S, Mittal N, Singh H. A feature level image fusion for IR and visible image using mNMRA based segmentation. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-06900-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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20
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A Modified Slime Mould Algorithm for Global Optimization. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:2298215. [PMID: 34912443 PMCID: PMC8668367 DOI: 10.1155/2021/2298215] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 08/27/2021] [Accepted: 10/12/2021] [Indexed: 12/02/2022]
Abstract
Slime mould algorithm (SMA) is a population-based metaheuristic algorithm inspired by the phenomenon of slime mould oscillation. The SMA is competitive compared to other algorithms but still suffers from the disadvantages of unbalanced exploitation and exploration and is easy to fall into local optima. To address these shortcomings, an improved variant of SMA named MSMA is proposed in this paper. Firstly, a chaotic opposition-based learning strategy is used to enhance population diversity. Secondly, two adaptive parameter control strategies are proposed to balance exploitation and exploration. Finally, a spiral search strategy is used to help SMA get rid of local optimum. The superiority of MSMA is verified in 13 multidimensional test functions and 10 fixed-dimensional test functions. In addition, two engineering optimization problems are used to verify the potential of MSMA to solve real-world optimization problems. The simulation results show that the proposed MSMA outperforms other comparative algorithms in terms of convergence accuracy, convergence speed, and stability.
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21
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Monga P, Sharma M, Sharma SK. A comprehensive meta-analysis of emerging swarm intelligent computing techniques and their research trend. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2021. [DOI: 10.1016/j.jksuci.2021.11.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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22
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A New Set of Mutation Operators for Dragonfly Algorithm. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-021-05639-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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23
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Singh S, Mittal N, Singh H. A multilevel thresholding algorithm using HDAFA for image segmentation. Soft comput 2021. [DOI: 10.1007/s00500-021-05956-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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24
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Optimization of FFF Process Parameters by Naked Mole-Rat Algorithms with Enhanced Exploration and Exploitation Capabilities. Polymers (Basel) 2021; 13:polym13111702. [PMID: 34070964 PMCID: PMC8196965 DOI: 10.3390/polym13111702] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 05/19/2021] [Accepted: 05/21/2021] [Indexed: 11/30/2022] Open
Abstract
Fused filament fabrication (FFF) has numerous process parameters that influence the mechanical strength of parts. Hence, many optimization studies are performed using conventional tools and algorithms. Although studies have also been performed using advanced algorithms, limited research has been reported in which variants of the naked mole-rat algorithm (NMRA) are implemented for solving the optimization issues of manufacturing processes. This study was performed to scrutinize optimum parameters and their levels to attain maximum impact strength, flexural strength and tensile strength based on five different FFF process parameters. The algorithm yielded better results than other studies and successfully achieved a maximum response, which may be helpful to enhance the mechanical strength of FFF parts. The study opens a plethora of research prospects for implementing NMRA in manufacturing. Moreover, the findings may help identify critical parametric levels for the fabrication of customized products at the commercial level and help to attain the objectives of Industry 4.0.
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25
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Zhu A, Gu Z, Hu C, Niu J, Xu C, Li Z. Political optimizer with interpolation strategy for global optimization. PLoS One 2021; 16:e0251204. [PMID: 33956841 PMCID: PMC8101970 DOI: 10.1371/journal.pone.0251204] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 04/21/2021] [Indexed: 11/18/2022] Open
Abstract
Political optimizer (PO) is a relatively state-of-the-art meta-heuristic optimization technique for global optimization problems, as well as real-world engineering optimization, which mimics the multi-staged process of politics in human society. However, due to a greedy strategy during the election phase, and an inappropriate balance of global exploration and local exploitation during the party switching stage, it suffers from stagnation in local optima with a low convergence accuracy. To overcome such drawbacks, a sequence of novel PO variants were proposed by integrating PO with Quadratic Interpolation, Advance Quadratic Interpolation, Cubic Interpolation, Lagrange Interpolation, Newton Interpolation, and Refraction Learning (RL). The main contributions of this work are listed as follows. (1) The interpolation strategy was adopted to help the current global optima jump out of local optima. (2) Specifically, RL was integrated into PO to improve the diversity of the population. (3) To improve the ability of balancing exploration and exploitation during the party switching stage, a logistic model was proposed to maintain a good balance. To the best of our knowledge, PO combined with the interpolation strategy and RL was proposed here for the first time. The performance of the best PO variant was evaluated by 19 widely used benchmark functions and 30 test functions from the IEEE CEC 2014. Experimental results revealed the superior performance of the proposed algorithm in terms of exploration capacity.
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Affiliation(s)
- Aijun Zhu
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, P.R. China
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, United States of America
- School of Mechano-Electronic Engineering, Xidian University, Xi’an, P.R. China
- Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin, P.R. China
| | - Zhanqi Gu
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, P.R. China
| | - Cong Hu
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, P.R. China
| | - Junhao Niu
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, P.R. China
| | - Chuanpei Xu
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, P.R. China
| | - Zhi Li
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, P.R. China
- School of Mechano-Electronic Engineering, Xidian University, Xi’an, P.R. China
- Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin, P.R. China
- Guilin University of Aerospace Technology, Guilin, P.R. China
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26
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Salgotra R, Singh U, Singh S, Mittal N. A hybridized multi-algorithm strategy for engineering optimization problems. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.106790] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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27
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Ezugwu AE, Shukla AK, Nath R, Akinyelu AA, Agushaka JO, Chiroma H, Muhuri PK. Metaheuristics: a comprehensive overview and classification along with bibliometric analysis. Artif Intell Rev 2021. [DOI: 10.1007/s10462-020-09952-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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28
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An Improved Adaptive Genetic Algorithm for Two-Dimensional Rectangular Packing Problem. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11010413] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper proposes the hybrid adaptive genetic algorithm (HAGA) as an improved method for solving the NP-hard two-dimensional rectangular packing problem to maximize the filling rate of a rectangular sheet. The packing sequence and rotation state are encoded in a two-stage approach, and the initial population is constructed from random generation by a combination of sorting rules. After using the sort-based method as an improved selection operator for the hybrid adaptive genetic algorithm, the crossover probability and mutation probability are adjusted adaptively according to the joint action of individual fitness from the local perspective and the global perspective of population evolution. The approach not only can obtain differential performance for individuals but also deals with the impact of dynamic changes on population evolution to quickly find a further improved solution. The heuristic placement algorithm decodes the rectangular packing sequence and addresses the two-dimensional rectangular packing problem through continuous iterative optimization. The computational results of a wide range of benchmark instances from zero-waste to non-zero-waste problems show that the HAGA outperforms those of two adaptive genetic algorithms from the related literature. Compared with some recent algorithms, this algorithm, which can be increased by up to 1.6604% for the average filling rate, has great significance for improving the quality of work in fields such as packing and cutting.
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29
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Abstract
COVID-19 has emerged as a global pandemic over the past four months and has impacted more than 180 countries of the world. With a global increase rate of 3% to 5% daily cases, the virus seems to be a never ending process and WHO reports that the virus may stay here forever. So it becomes necessary to analyze the possible impact of the virus globally and present predictions on how it will behave in future. In this chapter, time series forecasting of COVID-19 with respect to Australia has been analyzed, and prediction models have been derived by using genetic programming. Two prediction models have been proposed, one each for confirmed cases and death cases. The results are validated and importance of prediction variables are presented and discussed. From the numerical results, it can be said that the proposed gene expression programming models are highly reliable and can be considered as standard for time series prediction for COVID-19 in Australia.
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30
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Salgotra R, Gandomi M, Gandomi AH. Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries. CHAOS, SOLITONS, AND FRACTALS 2020; 140:110118. [PMID: 32834632 PMCID: PMC7367045 DOI: 10.1016/j.chaos.2020.110118] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Accepted: 07/10/2020] [Indexed: 05/03/2023]
Abstract
COVID-19 or SARS-Cov-2, affecting 6 million people and more than 300,000 deaths, the global pandemic has engulfed more than 90% countries of the world. The virus started from a single organism and is escalating at a rate of 3% to 5% daily and seems to be a never ending process. Understanding the basic dynamics and presenting new predictions models for evaluating the potential effect of the virus is highly crucial. In present work, an evolutionary data analytics method called as Genetic programming (GP) is used to mathematically model the potential effect of coronavirus in 15 most affected countries of the world. Two datasets namely confirmed cases (CC) and death cases (DC) were taken into consideration to estimate, how transmission varied in these countries between January 2020 and May 2020. Further, a percentage rise in the number of daily cases is also shown till 8 June 2020 and it is expected that Brazil will have the maximum rise in CC and USA have the most DC. Also, prediction of number of new CC and DC cases for every one million people in each of these countries is presented. The proposed model predicted that the transmission of COVID-19 in China is declining since late March 2020; in Singapore, France, Italy, Germany and Spain the curve has stagnated; in case of Canada, South Africa, Iran and Turkey the number of cases are rising slowly; whereas for USA, UK, Brazil, Russia and Mexico the rate of increase is very high and control measures need to be taken to stop the chains of transmission. Apart from that, the proposed prediction models are simple mathematical equations and future predictions can be drawn from these general equations. From the experimental results and statistical validation, it can be said that the proposed models use simple linkage functions and provide highly reliable results for time series prediction of COVID-19 in these countries.
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Affiliation(s)
- Rohit Salgotra
- Dept. of ECE, Thapar Institute of Engineering & Technology, Patiala, India
| | - Mostafa Gandomi
- School of Civil Engineering, University of Tehran, Tehran, Iran
| | - Amir H Gandomi
- Faculty of Engineering & Information Technology, University of Technology Sydney, NSW 2007, Australia
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31
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Braude S, Holtze S, Begall S, Brenmoehl J, Burda H, Dammann P, Marmol D, Gorshkova E, Henning Y, Hoeflich A, Höhn A, Jung T, Hamo D, Sahm A, Shebzukhov Y, Šumbera R, Miwa S, Vyssokikh MY, Zglinicki T, Averina O, Hildebrandt TB. Surprisingly long survival of premature conclusions about naked mole‐rat biology. Biol Rev Camb Philos Soc 2020; 96:376-393. [DOI: 10.1111/brv.12660] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 10/06/2020] [Accepted: 10/13/2020] [Indexed: 12/11/2022]
Affiliation(s)
- Stan Braude
- Biology Department Washington University, One Brookings Drive St. Louis MO 63130 U.S.A
| | - Susanne Holtze
- Department of Reproduction Management Leibniz‐Institute for Zoo and Wildlife Research Berlin 10315 Germany
| | - Sabine Begall
- Department of General Zoology, Faculty of Biology University of Duisburg‐Essen, Universitätsstr Essen 45147 Germany
| | - Julia Brenmoehl
- Institute for Genome Biology Leibniz‐Institute for Farm Animal Biology Dummerstorf 18196 Germany
| | - Hynek Burda
- Department of Game Management and Wildlife Biology, Faculty of Forestry and Wood Sciences Czech University of Life Sciences Praha 16500 Czech Republic
| | - Philip Dammann
- Department of General Zoology, Faculty of Biology University of Duisburg‐Essen, Universitätsstr Essen 45147 Germany
- University Hospital Essen Hufelandstr Essen 45141 Germany
| | - Delphine Marmol
- Molecular Physiology Research Unit (URPhyM), NARILIS University of Namur Namur 5000 Belgium
| | - Ekaterina Gorshkova
- Engelhardt Institute of Molecular Biology Russian Academy of Sciences, Vavilova str. 32 Moscow 119991 Russia
- Faculty of Biology Lomonosov Moscow State University Moscow 119991 Russia
| | - Yoshiyuki Henning
- University Hospital Essen Hufelandstr Essen 45141 Germany
- Institute of Physiology Department of General Zoology University of Duisburg Essen Germany
| | - Andreas Hoeflich
- Division Signal Transduction Institute for Genome Biology, Leibniz‐Institute for Farm Animal Biology, FBN Dummerstorf, Wilhelm‐Stahl‐Allee 2 Dummerstorf 18196 Germany
| | - Annika Höhn
- Department of Molecular Toxicology German Institute of Human Nutrition (DIfE) Potsdam‐Rehbrücke Nuthetal 14558 Germany
- German Center for Diabetes Research (DZD) München‐Neuherberg 85764 Germany
| | - Tobias Jung
- Department of Molecular Toxicology German Institute of Human Nutrition (DIfE) Potsdam‐Rehbrücke Nuthetal 14558 Germany
| | - Dania Hamo
- Charité ‐ Universitätsmedizin Berlin Berlin Institute of Health Center for Regenerative Therapies (BCRT) Berlin 13353 Germany
- German Rheumatism Research Centre Berlin (DRFZ) Berlin 10117 Germany
| | - Arne Sahm
- Computational Biology Group Leibniz Institute on Aging – Fritz Lipmann Institute Jena 07745 Germany
| | - Yury Shebzukhov
- Engelhardt Institute of Molecular Biology Russian Academy of Sciences, Vavilova str. 32 Moscow 119991 Russia
- Charité ‐ Universitätsmedizin Berlin Berlin Institute of Health Center for Regenerative Therapies (BCRT) Berlin 13353 Germany
| | - Radim Šumbera
- Faculty of Science University of South Bohemia České Budějovice 37005 Czech Republic
| | - Satomi Miwa
- Biosciences Institute, Edwardson building, Campus for Ageing and Vitality Newcastle University Newcastle upon Tyne NE4 5PL U.K
| | - Mikhail Y. Vyssokikh
- Belozersky Institute of Physico‐Chemical Biology Lomonosov Moscow State University Moscow 119991 Russia
| | - Thomas Zglinicki
- Biosciences Institute, Edwardson building, Campus for Ageing and Vitality Newcastle University Newcastle upon Tyne NE4 5PL U.K
| | - Olga Averina
- Belozersky Institute of Physico‐Chemical Biology Lomonosov Moscow State University Moscow 119991 Russia
| | - Thomas B. Hildebrandt
- Department of Reproduction Management Leibniz‐Institute for Zoo and Wildlife Research Berlin 10315 Germany
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Salgotra R, Gandomi M, Gandomi AH. Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming. CHAOS, SOLITONS, AND FRACTALS 2020; 138:109945. [PMID: 32508399 PMCID: PMC7260529 DOI: 10.1016/j.chaos.2020.109945] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 05/26/2020] [Indexed: 05/18/2023]
Abstract
COVID-19 declared as a global pandemic by WHO, has emerged as the most aggressive disease, impacting more than 90% countries of the world. The virus started from a single human being in China, is now increasing globally at a rate of 3% to 5% daily and has become a never ending process. Some studies even predict that the virus will stay with us forever. India being the second most populous country of the world, is also not saved, and the virus is spreading as a community level transmitter. Therefore, it become really important to analyse the possible impact of COVID-19 in India and forecast how it will behave in the days to come. In present work, prediction models based on genetic programming (GP) have been developed for confirmed cases (CC) and death cases (DC) across three most affected states namely Maharashtra, Gujarat and Delhi as well as whole India. The proposed prediction models are presented using explicit formula, and impotence of prediction variables are studied. Here, statistical parameters and metrics have been used for evaluated and validate the evolved models. From the results, it has been found that the proposed GEP-based models use simple linkage functions and are highly reliable for time series prediction of COVID-19 cases in India.
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Affiliation(s)
- Rohit Salgotra
- Dept. of ECE, Thapar Institute of Engineering & Technology, Patiala, India
| | - Mostafa Gandomi
- School of Civil Engineering, University of Tehran, Tehran, Iran
| | - Amir H Gandomi
- Faculty of Engineering & Information Technology, University of Technology Sydney, NSW 2007, Australia
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33
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Molina D, Poyatos J, Ser JD, García S, Hussain A, Herrera F. Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations. Cognit Comput 2020. [DOI: 10.1007/s12559-020-09730-8] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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34
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Askari Q, Younas I, Saeed M. Political Optimizer: A novel socio-inspired meta-heuristic for global optimization. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.105709] [Citation(s) in RCA: 107] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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