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- Title
Evolutionary Many‐objective Optimization: Difficulties, Approaches, and Discussions.
- Authors
Sato, Hiroyuki; Ishibuchi, Hisao
- Abstract
Population‐based evolutionary algorithms are suitable for solving multi‐objective optimization problems involving multiple conflicting objectives. This is because a set of well‐distributed solutions can be obtained by a single run, which approximate the optimal tradeoff among the objectives. Over the past three decades, evolutionary multi‐objective optimization has been intensively studied and used in various real‐world applications. However, evolutionary multi‐objective optimization faces various difficulties as the number of objectives increases. The simultaneous optimization of more than three objectives, which is called many‐objective optimization, has attracted considerable research attention. This paper explains various difficulties in evolutionary many‐objective optimization, reviews representative approaches, and discusses their effects and limitations. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
- Subjects
JAPAN; EVOLUTIONARY algorithms; ELECTRICAL engineers; PERIODICAL publishing
- Publication
IEEJ Transactions on Electrical & Electronic Engineering, 2023, Vol 18, Issue 7, p1048
- ISSN
1931-4973
- Publication type
Article
- DOI
10.1002/tee.23796