An Enhanced Adaptive Differential Evolution Algorithm With Multi-Mutation Schemes and Weighted Control Parameter Setting
Differential evolution (DE) algorithm is one of the most effective and efficient heuristic approaches for solving complex black box problems. But it still easily suffers from premature convergence and stagnation. To alleviate these defects, this paper presents a novel DE variant, named enhanced adap...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10239140/ |
| _version_ | 1797680898096758784 |
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| author | Mengnan Tian Yanhui Meng Xingshi He Qingqing Zhang Yanghan Gao |
| author_facet | Mengnan Tian Yanhui Meng Xingshi He Qingqing Zhang Yanghan Gao |
| author_sort | Mengnan Tian |
| collection | DOAJ |
| description | Differential evolution (DE) algorithm is one of the most effective and efficient heuristic approaches for solving complex black box problems. But it still easily suffers from premature convergence and stagnation. To alleviate these defects, this paper presents a novel DE variant, named enhanced adaptive differential evolution algorithm with multi-mutation schemes and weighted control parameter setting (MWADE), to further strengthen its search capability. In MWADE, a multi-schemes mutation strategy is first proposed to properly exploit or explore the promising information of each individual. Herein, the whole population is dynamically grouped into three subpopulations according to their fitness values and search performance, and three different mutant operators with various search characteristics are respectively adopted for each subpopulation. Meanwhile, in order to ensure the exploration of algorithm at the later evolutionary stage, a weight-controlled parameter setting is proposed to suitably assign scale factors for different differential vectors. Moreover, a random opposition mechanism with greedy selection is introduced to avoid trapping in local optima or stagnation, and an adaptive population size reduction scheme is devised to further promote the search effectiveness of algorithm. Finally, to illustrate the performance of MWADE, thirteen typical algorithms are adopted and compared with MWADE on 30 functions from IEEE CEC 2017 test suite with different dimensions, and the effectiveness of its proposed components are also investigated. Numerical results indicate that the proposed algorithm has a better search performance. |
| first_indexed | 2024-03-11T23:36:56Z |
| format | Article |
| id | doaj.art-7a85e9d7f8384b95b5117d60be3ddb1f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T23:36:56Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7a85e9d7f8384b95b5117d60be3ddb1f2023-09-19T23:01:24ZengIEEEIEEE Access2169-35362023-01-0111988549887410.1109/ACCESS.2023.331201010239140An Enhanced Adaptive Differential Evolution Algorithm With Multi-Mutation Schemes and Weighted Control Parameter SettingMengnan Tian0https://orcid.org/0009-0007-5908-4543Yanhui Meng1https://orcid.org/0009-0009-2106-5608Xingshi He2https://orcid.org/0009-0006-9180-4996Qingqing Zhang3https://orcid.org/0000-0002-5507-466XYanghan Gao4https://orcid.org/0009-0003-2556-3686School of Sciences, Xi’an Polytechnic University, Xi’an, ChinaSchool of Sciences, Xi’an Polytechnic University, Xi’an, ChinaSchool of Sciences, Xi’an Polytechnic University, Xi’an, ChinaSchool of Sciences, Xi’an Polytechnic University, Xi’an, ChinaSchool of Sciences, Xi’an Polytechnic University, Xi’an, ChinaDifferential evolution (DE) algorithm is one of the most effective and efficient heuristic approaches for solving complex black box problems. But it still easily suffers from premature convergence and stagnation. To alleviate these defects, this paper presents a novel DE variant, named enhanced adaptive differential evolution algorithm with multi-mutation schemes and weighted control parameter setting (MWADE), to further strengthen its search capability. In MWADE, a multi-schemes mutation strategy is first proposed to properly exploit or explore the promising information of each individual. Herein, the whole population is dynamically grouped into three subpopulations according to their fitness values and search performance, and three different mutant operators with various search characteristics are respectively adopted for each subpopulation. Meanwhile, in order to ensure the exploration of algorithm at the later evolutionary stage, a weight-controlled parameter setting is proposed to suitably assign scale factors for different differential vectors. Moreover, a random opposition mechanism with greedy selection is introduced to avoid trapping in local optima or stagnation, and an adaptive population size reduction scheme is devised to further promote the search effectiveness of algorithm. Finally, to illustrate the performance of MWADE, thirteen typical algorithms are adopted and compared with MWADE on 30 functions from IEEE CEC 2017 test suite with different dimensions, and the effectiveness of its proposed components are also investigated. Numerical results indicate that the proposed algorithm has a better search performance.https://ieeexplore.ieee.org/document/10239140/Differential evolutionnumerical optimizationmutation strategyparameter controlpopulation size reduction scheme |
| spellingShingle | Mengnan Tian Yanhui Meng Xingshi He Qingqing Zhang Yanghan Gao An Enhanced Adaptive Differential Evolution Algorithm With Multi-Mutation Schemes and Weighted Control Parameter Setting IEEE Access Differential evolution numerical optimization mutation strategy parameter control population size reduction scheme |
| title | An Enhanced Adaptive Differential Evolution Algorithm With Multi-Mutation Schemes and Weighted Control Parameter Setting |
| title_full | An Enhanced Adaptive Differential Evolution Algorithm With Multi-Mutation Schemes and Weighted Control Parameter Setting |
| title_fullStr | An Enhanced Adaptive Differential Evolution Algorithm With Multi-Mutation Schemes and Weighted Control Parameter Setting |
| title_full_unstemmed | An Enhanced Adaptive Differential Evolution Algorithm With Multi-Mutation Schemes and Weighted Control Parameter Setting |
| title_short | An Enhanced Adaptive Differential Evolution Algorithm With Multi-Mutation Schemes and Weighted Control Parameter Setting |
| title_sort | enhanced adaptive differential evolution algorithm with multi mutation schemes and weighted control parameter setting |
| topic | Differential evolution numerical optimization mutation strategy parameter control population size reduction scheme |
| url | https://ieeexplore.ieee.org/document/10239140/ |
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