Improving Dual-Population Differential Evolution Based on Hierarchical Mutation and Selection Strategy
The dual-population differential evolution (DDE) algorithm is an optimization technique that simultaneously maintains two populations to balance global and local search. It has been demonstrated to outperform single-population differential evolution algorithms. However, existing improvements to dual...
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| Format: | Article |
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MDPI AG
2023-12-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/13/1/62 |
| _version_ | 1797358970842644480 |
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| author | Yawei Huang Xuezhong Qian Wei Song |
| author_facet | Yawei Huang Xuezhong Qian Wei Song |
| author_sort | Yawei Huang |
| collection | DOAJ |
| description | The dual-population differential evolution (DDE) algorithm is an optimization technique that simultaneously maintains two populations to balance global and local search. It has been demonstrated to outperform single-population differential evolution algorithms. However, existing improvements to dual-population differential evolution algorithms often overlook the importance of selecting appropriate mutation and selection operators to enhance algorithm performance. In this paper, we propose a dual-population differential evolution (DPDE) algorithm based on a hierarchical mutation and selection strategy. We divided the population into elite and normal subpopulations based on fitness values. Information exchange between the two subpopulations was facilitated through a hierarchical mutation strategy, promoting a balanced exploration–exploitation trade-off in the algorithm. Additionally, this paper presents a new hierarchical selection strategy aimed at improving the population’s capacity to avoid local optima. It achieves this by accepting discarded trial vectors differently compared to previous methods. We expect that the newly introduced hierarchical selection and mutation strategies will work in synergy, effectively harnessing their potential to enhance the algorithm’s performance. Extensive experiments were conducted on the CEC 2017 and CEC 2011 test sets. The results showed that the DPDE algorithm offers competitive performance, comparable to six state-of-the-art differential evolution algorithms. |
| first_indexed | 2024-03-08T15:09:52Z |
| format | Article |
| id | doaj.art-e23a59f6e5004bd19c57ea579d42d754 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-08T15:09:52Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-e23a59f6e5004bd19c57ea579d42d7542024-01-10T14:54:17ZengMDPI AGElectronics2079-92922023-12-011316210.3390/electronics13010062Improving Dual-Population Differential Evolution Based on Hierarchical Mutation and Selection StrategyYawei Huang0Xuezhong Qian1Wei Song2School of Artificial Intelligence and Computer Science, Jiangnan University, Lihu Avenuc, Wuxi 214122, ChinaSchool of Artificial Intelligence and Computer Science, Jiangnan University, Lihu Avenuc, Wuxi 214122, ChinaSchool of Artificial Intelligence and Computer Science, Jiangnan University, Lihu Avenuc, Wuxi 214122, ChinaThe dual-population differential evolution (DDE) algorithm is an optimization technique that simultaneously maintains two populations to balance global and local search. It has been demonstrated to outperform single-population differential evolution algorithms. However, existing improvements to dual-population differential evolution algorithms often overlook the importance of selecting appropriate mutation and selection operators to enhance algorithm performance. In this paper, we propose a dual-population differential evolution (DPDE) algorithm based on a hierarchical mutation and selection strategy. We divided the population into elite and normal subpopulations based on fitness values. Information exchange between the two subpopulations was facilitated through a hierarchical mutation strategy, promoting a balanced exploration–exploitation trade-off in the algorithm. Additionally, this paper presents a new hierarchical selection strategy aimed at improving the population’s capacity to avoid local optima. It achieves this by accepting discarded trial vectors differently compared to previous methods. We expect that the newly introduced hierarchical selection and mutation strategies will work in synergy, effectively harnessing their potential to enhance the algorithm’s performance. Extensive experiments were conducted on the CEC 2017 and CEC 2011 test sets. The results showed that the DPDE algorithm offers competitive performance, comparable to six state-of-the-art differential evolution algorithms.https://www.mdpi.com/2079-9292/13/1/62mutation strategyselection strategymulti-populationdifferential evolution |
| spellingShingle | Yawei Huang Xuezhong Qian Wei Song Improving Dual-Population Differential Evolution Based on Hierarchical Mutation and Selection Strategy Electronics mutation strategy selection strategy multi-population differential evolution |
| title | Improving Dual-Population Differential Evolution Based on Hierarchical Mutation and Selection Strategy |
| title_full | Improving Dual-Population Differential Evolution Based on Hierarchical Mutation and Selection Strategy |
| title_fullStr | Improving Dual-Population Differential Evolution Based on Hierarchical Mutation and Selection Strategy |
| title_full_unstemmed | Improving Dual-Population Differential Evolution Based on Hierarchical Mutation and Selection Strategy |
| title_short | Improving Dual-Population Differential Evolution Based on Hierarchical Mutation and Selection Strategy |
| title_sort | improving dual population differential evolution based on hierarchical mutation and selection strategy |
| topic | mutation strategy selection strategy multi-population differential evolution |
| url | https://www.mdpi.com/2079-9292/13/1/62 |
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