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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Veröffentlicht in:Expert systems with applications Jg. 252; S. 124226
Hauptverfasser: Zhang, Quan, Yang, Na, Wu, Ying, Tang, Zhenzhou
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 15.10.2024
Schlagworte:
Evolutionary algorithm
Lp-norm
Many-objective optimization
Selective evolution
Lp-norm
Selective evolution
Many-objective optimization
Evolutionary algorithm
ISSN:0957-4174, 1873-6793
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Zusammenfassung: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.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2024.124226