NSGA-II/SDR-OLS: A Novel Large-Scale Many-Objective Optimization Method Using Opposition-Based Learning and Local Search

Recently, many-objective optimization problems (MaOPs) have become a hot issue of interest in academia and industry, and many more many-objective evolutionary algorithms (MaOEAs) have been proposed. NSGA-II/SDR (NSGA-II with a strengthened dominance relation) is an improved NSGA-II, created by repla...

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Veröffentlicht in:	Mathematics (Basel) Jg. 11; H. 8; S. 1911
Hauptverfasser:	Zhang, Yingxin, Wang, Gaige, Wang, Hongmei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.04.2023
Schlagworte:	Convergence Covariance matrix Efficiency evolutionary algorithm Evolutionary algorithms Food science Genetic algorithms large-scale optimization Learning local search many-objective optimization Mathematical optimization Mathematical research Methods Multiple objective analysis Objectives opposition-based learning Optimization Sorting algorithms China
ISSN:	2227-7390, 2227-7390
Online-Zugang:	Volltext
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