An adaptive reference vector guided many-objective optimization algorithm based on the pareto front density estimation

The performance of evolutionary algorithms using reference vectors to guide the evolution process mainly depends on the adaptive reference vector update strategy. In order to solve the challenging many-objective optimization problems with irregular Pareto fronts, this paper proposes an adaptive refe...

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Veröffentlicht in:Swarm and evolutionary computation Jg. 88; S. 101601
Hauptverfasser: Xu, Ying, Li, Fusen, Zhang, Huan, Li, Wei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.07.2024
Schlagworte:
Evolutionary algorithm
Many-objective evolutionary algorithm
Many-objective optimization
Multi-objective optimization
Many-objective evolutionary algorithm
Multi-objective optimization
Many-objective optimization
Evolutionary algorithm
ISSN:2210-6502
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Zusammenfassung:The performance of evolutionary algorithms using reference vectors to guide the evolution process mainly depends on the adaptive reference vector update strategy. In order to solve the challenging many-objective optimization problems with irregular Pareto fronts, this paper proposes an adaptive reference vector update strategy based on the Pareto front density estimation, which estimates the true Pareto front by finding sparse regions while ensuring the uniform distribution of reference vectors. In addition, an improved environmental selection strategy using the angle-based neighborhood density estimation has been proposed for estimating the neighborhood density to effectively guide the population evolution. On this basis, this paper proposes an adaptive reference vector guided many-objective optimization algorithm based on Pareto front density estimation (MaOEA-PDE). Experimental results on a large number of benchmark problems show MaOEA-PDE achieves better performance compared with some state-of-the-art algorithms in the literature.
ISSN:2210-6502
DOI:10.1016/j.swevo.2024.101601