Evolutionary Many-objective Optimization: Difficulties, Approaches, and Discussions

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.