Reference Vector Adaptation and Mating Selection Strategy via Adaptive Resonance Theory-Based Clustering for Many-Objective Optimization

Decomposition-based multiobjective evolutionary algorithms (MOEAs) with clustering-based reference vector adaptation show high optimization performance for many-objective optimization problems (MaOPs). Especially, algorithms that employ a clustering algorithm with a topological structure (i.e., a ne...

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出版年:	IEEE Access
主要な著者:	Takato Kinoshita, Naoki Masuyama, Yiping Liu, Yusuke Nojima, Hisao Ishibuchi
フォーマット:	論文
言語:	英語
出版事項:	IEEE 2023-01-01
主題:	Decomposition-based many-objective evolutionary optimization reference vector adaptation adaptive resonance theory
オンライン･アクセス:	https://ieeexplore.ieee.org/document/10314513/