Affiliation:
1. Department of Informatics, Graduate School of Informatics and Engineering The University of Electro‐Communications 182‐8585 Tokyo 1‐5‐1 Chofugaoka, Chofu Japan
2. Guangdong Provincial Key Laboratory of Brain‐inspired Intelligent Computation, Department of Computer Science and Engineering Southern University of Science and Technology 518055 Shenzhen 1088 Xueyuan Avenue China
Abstract
AbstractPopulation‐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.
Subject
Electrical and Electronic Engineering
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