A Cα-dominance-based solution estimation evolutionary algorithm for many-objective optimization

Balancing convergence and diversity is a key issue for many-objective optimization problems (MaOPs), which is a great challenge to the classical Pareto-based multi-objective algorithms due to its severe lack of selection pressure. To relieve the above challenge, a Cα-dominance-based solution estimat...

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Veröffentlicht in:	Knowledge-based systems Jg. 248; S. 108738
Hauptverfasser:	Liu, Junhua, Wang, Yuping, Cheung, Yiu-ming
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 19.07.2022 Elsevier Science Ltd
Schlagworte:	Algorithms C[formula omitted]-dominance method Convergence Dominance Estimation Evolutionary algorithm Evolutionary algorithms Genetic algorithms Many-objective optimization Multiple objective analysis Nonlinear analysis Objectives Optimization Parameters Pareto optimum Selection pressure Solution estimation Selection pressure Many-objective optimization Cα-dominance method Evolutionary algorithm Solution estimation
ISSN:	0950-7051, 1872-7409
Online-Zugang:	Volltext
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