SEA: Many-objective evolutionary algorithm with selection evolution strategy
Balancing the convergence and diversity of the population is crucial for solving multi-objective problems. As the number of objectives increases, the inherent conflict between maintaining diversity and ensuring convergence becomes more significant. To address this challenge, we propose a novel evolu...
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| Vydáno v: | Expert systems with applications Ročník 252; s. 124226 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
15.10.2024
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| Témata: | |
| ISSN: | 0957-4174, 1873-6793 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Balancing the convergence and diversity of the population is crucial for solving multi-objective problems. As the number of objectives increases, the inherent conflict between maintaining diversity and ensuring convergence becomes more significant. To address this challenge, we propose a novel evolutionary algorithm that selectively emphasizes either convergence or diversity, guided by the convergence and diversity indicators of the current population and a predefined priority criterion. During the iterative process, the algorithm strategically aims to either approach the true Pareto front to improve convergence or foster a more uniform distribution within the current Pareto layer to enhance diversity. Continuous monitoring of these indicators enables the algorithm to effectively manage and fine-tune the convergence and diversity of the population. We meticulously evaluated the performance of the proposed algorithm by comparing it with eight state-of-the-art evolution algorithms on 31 benchmark problems. The experimental results unequivocally demonstrated the outstanding performance of the proposed algorithm in solving multi-objective problems. Furthermore, the algorithm can be seamlessly incorporated into other evolution algorithms to strike a delicate balance between diversity and convergence, thereby empowering them to tackle challenging many-objective optimization tasks with enhanced efficiency and accuracy.
•A mechanism for managing convergence-diversity trade-offs in MaOPs as objectives grow.•An MOEA balances convergence and diversity via indicators and priority criterion.•Extensive experiments validated SEA’s superior performance in MaOPs. |
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| ISSN: | 0957-4174 1873-6793 |
| DOI: | 10.1016/j.eswa.2024.124226 |