Many-Objective Evolutionary Algorithm with Adaptive Reference Vector

•It is observed that regular convergence indicators are more focused on the convergence and may neglect the extent of the spread.•This paper designs an adaptive reference vector strategy, which is able to take into account the convergence and the extent of the spread, simultaneously.•A new algorithm...

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Veröffentlicht in:Information sciences Jg. 563; S. 70 - 90
Hauptverfasser: Zhang, Maoqing, Wang, Lei, Li, Wuzhao, Hu, Bo, Li, Dongyang, Wu, Qidi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 01.07.2021
Schlagworte:
Adaptive reference vector strategy
Convergence
Hierarchical clustering strategy
Many-objective optimization problems
Spread
Spread
Hierarchical clustering strategy
Many-objective optimization problems
Convergence
Adaptive reference vector strategy
ISSN:0020-0255, 1872-6291
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Zusammenfassung:•It is observed that regular convergence indicators are more focused on the convergence and may neglect the extent of the spread.•This paper designs an adaptive reference vector strategy, which is able to take into account the convergence and the extent of the spread, simultaneously.•A new algorithm is proposed and tested on multiple test suites.. Convergence is always a major concern for many-objective optimization problems. Over the past few decades, various methods have been designed for measuring the convergence. However, according to our mathematical and empirical analyses, most of these methods are more focused on the convergence, and may neglect the exploration of boundary solutions, resulting in the incomplete Pareto fronts and the poor extent of spread achieved among the obtained non-dominated solutions. Regarding this issue, this paper proposes a Many-Objective Evolutionary Algorithm with Adaptive Reference Vector (MaOEA-ARV). In MaOEA-ARV, an adaptive reference vector strategy is designed to dynamically adjust the reference vectors according to the current distribution of candidate solutions for ensuring the spread and convergence simultaneously. Additionally, a hierarchical clustering strategy is employed to adaptively partition candidate solutions into multiple clusters for the diversity of candidate solutions. Experimental results on DTLZ, BT, ZDT and WFG test suites with up to 12 objectives demonstrate the effectiveness of MaOEA-ARV.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2021.01.015