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

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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Bibliographic Details
Main Authors: Yingxin Zhang, Gaige Wang, Hongmei Wang
Format: Article
Language:English
Published: MDPI AG 2023-04-01
Series:Mathematics
Subjects:
evolutionary algorithm
many-objective optimization
large-scale optimization
opposition-based learning
local search
Online Access:https://www.mdpi.com/2227-7390/11/8/1911

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