Affiliation:
1. University of Science and Technology of China, Hefei, Anhui, China
2. University of Birmingham, Birmingham, UK
Abstract
Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world applications. However, most MOEAs based on Pareto-dominance handle many-objective problems (MaOPs) poorly due to a high proportion of incomparable and thus mutually nondominated solutions. Recently, a number of many-objective evolutionary algorithms (MaOEAs) have been proposed to deal with this scalability issue. In this article, a survey of MaOEAs is reported. According to the key ideas used, MaOEAs are categorized into seven classes: relaxed dominance based, diversity-based, aggregation-based, indicator-based, reference set based, preference-based, and dimensionality reduction approaches. Several future research directions in this field are also discussed.
Funder
the Program for New Century Excellent Talents in University
the European Union Seventh Framework Programme
the 973 Program of China
the Science and Technological Fund of Anhui Province for Outstanding Youth
Royal Society Wolfson Research Merit Award
EPSRC
the National Natural Science Foundation of China
Publisher
Association for Computing Machinery (ACM)
Subject
General Computer Science,Theoretical Computer Science
Cited by
635 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献