New search strategy for multi-objective evolutionary algorithm
To address the problem of low search efficiency of multi-objective evolutionary algorithm during iterations, we proposed a new idea which considering a single individual to generate better solutions in a single iteration as a starting point to improve the search performance of multi-objective evolut...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024169484 |
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| author | Liu Yuejun |
| author_facet | Liu Yuejun |
| author_sort | Liu Yuejun |
| collection | DOAJ |
| description | To address the problem of low search efficiency of multi-objective evolutionary algorithm during iterations, we proposed a new idea which considering a single individual to generate better solutions in a single iteration as a starting point to improve the search performance of multi-objective evolutionary algorithm and designedthe neighbor strategy and guidance strategy based on this improved approach in this paper. We used our proposed new search strategy to improve NSGA-III algorithm(named as NSGA-III/NG) and MOEA/D algorithm(named as MOEA/D-NG). On ZDT, DTLZ and WFG public test sets, the NSGA-III/NG algorithm using the new search strategy was compared with NSGA-II algorithm, NSGA-III algorithm, ANSGA-III algorithm and NSGA-II/ARSBX algorithm. The MOEA/D-NG algorithm using the new search strategy was compared with MOEA/D algorithm, MOEA/D-CMA algorithm, MOEA/D-DE algorithm and CMOEA/D algorithm. Experimental results indicate that the performance of NSGA-III/NG algorithm using our search strategy is superior to NSGA-II, NSGA-III,ANSGA-III and NSGA-II/ARSBX algorithm and the performance of MOEA/D-NG algorithm using our search strategy is superior toMOEA/D, MOEA/D-CMA,MOEA/D-DE and CMOEA/D algorithm. Our proposed search strategy can improve the convergence speed of NSGA-III algorithm and MOEA/D algorithm by 12.54 %,the accuracy of the non dominated solution set by 3.67 %. This situation indicates that our search strategy could significantly improve the search capability of the multi-objective evolutionary algorithm. In addition, this strategy has excellent applicability and could be combined with mainstream multi-objective evolutionary algorithms. |
| format | Article |
| id | doaj-art-0ea54064760e439298cda9c1a71edfcd |
| institution | OA Journals |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-0ea54064760e439298cda9c1a71edfcd2025-08-20T01:58:33ZengElsevierHeliyon2405-84402024-12-011024e4091710.1016/j.heliyon.2024.e40917New search strategy for multi-objective evolutionary algorithmLiu Yuejun0Software School of Anyang Normal University, Anyang, 455002, Henan, China; Key Laboratory of Oracle Bone Inscriptions Information Processing, Ministry of Education, Anyang, 455002, Henan, ChinaTo address the problem of low search efficiency of multi-objective evolutionary algorithm during iterations, we proposed a new idea which considering a single individual to generate better solutions in a single iteration as a starting point to improve the search performance of multi-objective evolutionary algorithm and designedthe neighbor strategy and guidance strategy based on this improved approach in this paper. We used our proposed new search strategy to improve NSGA-III algorithm(named as NSGA-III/NG) and MOEA/D algorithm(named as MOEA/D-NG). On ZDT, DTLZ and WFG public test sets, the NSGA-III/NG algorithm using the new search strategy was compared with NSGA-II algorithm, NSGA-III algorithm, ANSGA-III algorithm and NSGA-II/ARSBX algorithm. The MOEA/D-NG algorithm using the new search strategy was compared with MOEA/D algorithm, MOEA/D-CMA algorithm, MOEA/D-DE algorithm and CMOEA/D algorithm. Experimental results indicate that the performance of NSGA-III/NG algorithm using our search strategy is superior to NSGA-II, NSGA-III,ANSGA-III and NSGA-II/ARSBX algorithm and the performance of MOEA/D-NG algorithm using our search strategy is superior toMOEA/D, MOEA/D-CMA,MOEA/D-DE and CMOEA/D algorithm. Our proposed search strategy can improve the convergence speed of NSGA-III algorithm and MOEA/D algorithm by 12.54 %,the accuracy of the non dominated solution set by 3.67 %. This situation indicates that our search strategy could significantly improve the search capability of the multi-objective evolutionary algorithm. In addition, this strategy has excellent applicability and could be combined with mainstream multi-objective evolutionary algorithms.http://www.sciencedirect.com/science/article/pii/S2405844024169484Multi-objective evolutionary algorithmSearch efficiencySearch strategyGuidance strategyNeighbor strategy |
| spellingShingle | Liu Yuejun New search strategy for multi-objective evolutionary algorithm Heliyon Multi-objective evolutionary algorithm Search efficiency Search strategy Guidance strategy Neighbor strategy |
| title | New search strategy for multi-objective evolutionary algorithm |
| title_full | New search strategy for multi-objective evolutionary algorithm |
| title_fullStr | New search strategy for multi-objective evolutionary algorithm |
| title_full_unstemmed | New search strategy for multi-objective evolutionary algorithm |
| title_short | New search strategy for multi-objective evolutionary algorithm |
| title_sort | new search strategy for multi objective evolutionary algorithm |
| topic | Multi-objective evolutionary algorithm Search efficiency Search strategy Guidance strategy Neighbor strategy |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024169484 |
| work_keys_str_mv | AT liuyuejun newsearchstrategyformultiobjectiveevolutionaryalgorithm |