A hybrid multi-objective evolutionary algorithm with feedback mechanism
Exploration and exploitation are two cornerstones for multi-objective evolutionary algorithms (MOEAs). To balance exploration and exploitation, we propose an efficient hybrid MOEA (i.e., MOHGD) by integrating multiple techniques and feedback mechanism. Multiple techniques include harmony search, gen...
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| Published in: | Applied intelligence (Dordrecht, Netherlands) Vol. 48; no. 11; pp. 4149 - 4173 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Springer US
01.11.2018
Springer Nature B.V |
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| ISSN: | 0924-669X, 1573-7497 |
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| Abstract | Exploration and exploitation are two cornerstones for multi-objective evolutionary algorithms (MOEAs). To balance exploration and exploitation, we propose an efficient hybrid MOEA (i.e., MOHGD) by integrating multiple techniques and feedback mechanism. Multiple techniques include harmony search, genetic operator and differential evolution, which can improve the search diversity. Whereas hybrid selection mechanism contributes to the search efficiency by integrating the advantages of the static and adaptive selection scheme. Therefore, multiple techniques based on the hybrid selection strategy can effectively enhance the exploration ability of the MOHGD. Besides, we propose a feedback strategy to transfer some non-dominated solutions from the external archive to the parent population. This feedback strategy can strengthen convergence toward Pareto optimal solutions and improve the exploitation ability of the MOHGD. The proposed MOHGD has been evaluated on benchmarks against other state of the art MOEAs in terms of convergence, spread, coverage, and convergence speed. Computational results show that the proposed MOHGD is competitive or superior to other MOEAs considered in this paper. |
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| AbstractList | Exploration and exploitation are two cornerstones for multi-objective evolutionary algorithms (MOEAs). To balance exploration and exploitation, we propose an efficient hybrid MOEA (i.e., MOHGD) by integrating multiple techniques and feedback mechanism. Multiple techniques include harmony search, genetic operator and differential evolution, which can improve the search diversity. Whereas hybrid selection mechanism contributes to the search efficiency by integrating the advantages of the static and adaptive selection scheme. Therefore, multiple techniques based on the hybrid selection strategy can effectively enhance the exploration ability of the MOHGD. Besides, we propose a feedback strategy to transfer some non-dominated solutions from the external archive to the parent population. This feedback strategy can strengthen convergence toward Pareto optimal solutions and improve the exploitation ability of the MOHGD. The proposed MOHGD has been evaluated on benchmarks against other state of the art MOEAs in terms of convergence, spread, coverage, and convergence speed. Computational results show that the proposed MOHGD is competitive or superior to other MOEAs considered in this paper. |
| Author | Gao, Liang Lu, Chao Zhou, Feng Li, Xinyu Zeng, Bing |
| Author_xml | – sequence: 1 givenname: Chao surname: Lu fullname: Lu, Chao email: luchao2006@163.com organization: Hubei Key Laboratory of Intelligent Geo-Information Processing (China University of Geosciences (Wuhan)), School of Computer Science, China University of Geosciences, State Key Laboratory of Digital Manufacturing Equipment & Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology – sequence: 2 givenname: Liang surname: Gao fullname: Gao, Liang organization: State Key Laboratory of Digital Manufacturing Equipment & Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology – sequence: 3 givenname: Xinyu surname: Li fullname: Li, Xinyu organization: State Key Laboratory of Digital Manufacturing Equipment & Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology – sequence: 4 givenname: Bing surname: Zeng fullname: Zeng, Bing organization: State Key Laboratory of Digital Manufacturing Equipment & Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology – sequence: 5 givenname: Feng surname: Zhou fullname: Zhou, Feng organization: The Army Engineering University of PLA (Wuhan) |
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| CitedBy_id | crossref_primary_10_1016_j_cie_2021_107198 crossref_primary_10_1016_j_swevo_2019_04_007 crossref_primary_10_1016_j_eswa_2025_127644 crossref_primary_10_1016_j_cie_2022_108126 crossref_primary_10_1109_JSYST_2021_3076481 crossref_primary_10_1016_j_swevo_2021_101007 crossref_primary_10_1109_TSMC_2021_3120702 crossref_primary_10_1007_s10489_020_01733_0 crossref_primary_10_1007_s10489_019_01424_5 crossref_primary_10_1016_j_asoc_2020_106382 crossref_primary_10_1016_j_asoc_2024_111508 crossref_primary_10_1109_TEVC_2024_3388527 crossref_primary_10_1007_s12008_024_01904_0 |
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| Keywords | Differential evolution Feedback mechanism Hybrid selection mechanism Harmony search Multi-objective evolutionary algorithm |
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| Title | A hybrid multi-objective evolutionary algorithm with feedback mechanism |
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