Differential Evolution Algorithm Based on Coupling-Coordination-Based Mutation Strategy
The differential evolution (DE) algorithm is a heuristic, stochastic, parallel search algorithm. The mutation operation is an integral part of the DE algorithm, relating to the basis and difference among vectors. Recently, many improved variations of mutation strategies have been proposed, and promi...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10769447/ |
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| author | Peiyuan Jin Juxiang Huang Quanxi Feng Jianming Cen Renjie Chu |
| author_facet | Peiyuan Jin Juxiang Huang Quanxi Feng Jianming Cen Renjie Chu |
| author_sort | Peiyuan Jin |
| collection | DOAJ |
| description | The differential evolution (DE) algorithm is a heuristic, stochastic, parallel search algorithm. The mutation operation is an integral part of the DE algorithm, relating to the basis and difference among vectors. Recently, many improved variations of mutation strategies have been proposed, and promising results have been achieved. However, under modifications related to the difference vector, individuals are selected for the difference vector mainly based on fitness values, which might decrease the population diversity and affect the algorithm performance. This paper proposes a coupling-coordination-based mutation strategy for the DE (in short for CCDM) to improve the selection of individuals in the difference vector. First, the coupling-coordination degree, which comprehensively considers individuals’ fitness values and distribution, is used to determine similarity between individuals. Then, the population individuals are clustered into four subpopulations according to their similarity. The subpopulation that contains the basis vector individuals serves as a similarity archive for the last vector in the difference vector. Finally, the concept of quantile is used to construct the elite archive for the first vector in the difference vector to accelerate the convergence. The effectiveness of the CCDM is verified through numerical experiments on the CEC2017 test function set using different types of mutation strategies and DE variants. Compared with the existing difference vector improvement strategies, the CCDM can further enhance searchability and convergence. |
| format | Article |
| id | doaj-art-4a92f4dad7a54a75bf6eb6bf78b05589 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4a92f4dad7a54a75bf6eb6bf78b055892024-12-11T00:04:56ZengIEEEIEEE Access2169-35362024-01-011217907717909010.1109/ACCESS.2024.350671610769447Differential Evolution Algorithm Based on Coupling-Coordination-Based Mutation StrategyPeiyuan Jin0Juxiang Huang1Quanxi Feng2https://orcid.org/0000-0002-7720-4453Jianming Cen3Renjie Chu4School of Mathematics and Statistics, Guilin University of Technology, Guilin, ChinaGuangxi Colleges and Universities Key Laboratory of Applied Statistics, Guilin University of Technology, Guilin, ChinaSchool of Mathematics and Statistics, Guilin University of Technology, Guilin, ChinaSchool of Mathematics and Statistics, Guilin University of Technology, Guilin, ChinaSchool of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, ChinaThe differential evolution (DE) algorithm is a heuristic, stochastic, parallel search algorithm. The mutation operation is an integral part of the DE algorithm, relating to the basis and difference among vectors. Recently, many improved variations of mutation strategies have been proposed, and promising results have been achieved. However, under modifications related to the difference vector, individuals are selected for the difference vector mainly based on fitness values, which might decrease the population diversity and affect the algorithm performance. This paper proposes a coupling-coordination-based mutation strategy for the DE (in short for CCDM) to improve the selection of individuals in the difference vector. First, the coupling-coordination degree, which comprehensively considers individuals’ fitness values and distribution, is used to determine similarity between individuals. Then, the population individuals are clustered into four subpopulations according to their similarity. The subpopulation that contains the basis vector individuals serves as a similarity archive for the last vector in the difference vector. Finally, the concept of quantile is used to construct the elite archive for the first vector in the difference vector to accelerate the convergence. The effectiveness of the CCDM is verified through numerical experiments on the CEC2017 test function set using different types of mutation strategies and DE variants. Compared with the existing difference vector improvement strategies, the CCDM can further enhance searchability and convergence.https://ieeexplore.ieee.org/document/10769447/Coupling-coordination degreedifferential evolutionelite archivesimilarity archivequantile |
| spellingShingle | Peiyuan Jin Juxiang Huang Quanxi Feng Jianming Cen Renjie Chu Differential Evolution Algorithm Based on Coupling-Coordination-Based Mutation Strategy IEEE Access Coupling-coordination degree differential evolution elite archive similarity archive quantile |
| title | Differential Evolution Algorithm Based on Coupling-Coordination-Based Mutation Strategy |
| title_full | Differential Evolution Algorithm Based on Coupling-Coordination-Based Mutation Strategy |
| title_fullStr | Differential Evolution Algorithm Based on Coupling-Coordination-Based Mutation Strategy |
| title_full_unstemmed | Differential Evolution Algorithm Based on Coupling-Coordination-Based Mutation Strategy |
| title_short | Differential Evolution Algorithm Based on Coupling-Coordination-Based Mutation Strategy |
| title_sort | differential evolution algorithm based on coupling coordination based mutation strategy |
| topic | Coupling-coordination degree differential evolution elite archive similarity archive quantile |
| url | https://ieeexplore.ieee.org/document/10769447/ |
| work_keys_str_mv | AT peiyuanjin differentialevolutionalgorithmbasedoncouplingcoordinationbasedmutationstrategy AT juxianghuang differentialevolutionalgorithmbasedoncouplingcoordinationbasedmutationstrategy AT quanxifeng differentialevolutionalgorithmbasedoncouplingcoordinationbasedmutationstrategy AT jianmingcen differentialevolutionalgorithmbasedoncouplingcoordinationbasedmutationstrategy AT renjiechu differentialevolutionalgorithmbasedoncouplingcoordinationbasedmutationstrategy |