Affiliation:
1. Anhui University, Hefei, China
2. Southern University of Science and Technology, Shenzhen, China
3. The Hong Kong Polytechnic University, Hong Kong SAR
4. University of Surrey, Guildford, U.K.
Abstract
Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in solving various optimization problems, but their performance may deteriorate drastically when tackling problems containing a large number of decision variables. In recent years, much effort been devoted to addressing the challenges brought by large-scale multi-objective optimization problems. This article presents a comprehensive survey of stat-of-the-art MOEAs for solving large-scale multi-objective optimization problems. We start with a categorization of these MOEAs into decision variable grouping based, decision space reduction based, and novel search strategy based MOEAs, discussing their strengths and weaknesses. Then, we review the benchmark problems for performance assessment and a few important and emerging applications of MOEAs for large-scale multi-objective optimization. Last, we discuss some remaining challenges and future research directions of evolutionary large-scale multi-objective optimization.
Funder
National Key R&D Program of China
National Natural Science Foundation of China
Hong Kong Scholars Program
Anhui Provincial Natural Science Foundation
Collaborative Innovation Program of Anhui
Research Grants Council of the Hong Kong Special Administrative Region
Royal Society International Exchanges Program
Publisher
Association for Computing Machinery (ACM)
Subject
General Computer Science,Theoretical Computer Science
Cited by
115 articles.
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