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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Mathematics (Basel) Ročník 11; číslo 8; s. 1911
Hlavní autori:	Zhang, Yingxin, Wang, Gaige, Wang, Hongmei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.04.2023
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!