A Survey of Evolutionary Algorithms for Multi-Objective Optimization Problems With Irregular Pareto Fronts

• 作者: Yicun Hua Qiqi Liu Kuangrong Hao Yaochu Jin
• 来源期刊: 自动化学报（英文版）
• 年，卷（期）: 2021,（2）
• 所属期刊栏目: REVIEWS
• 页码范围: 303-318
• 页数: 16页
• 语种: 英文
• DOI: 10.1109/JAS.2021.1003817

摘要

Evolutionary algorithms have been shown to be very successful in solving multi-objective optimization problems (MOPs). However, their performance often deteriorates when solving MOPs with irregular Pareto fronts. To remedy this issue, a large body of research has been performed in recent years and many new algorithms have been proposed. This paper provides a comprehensive survey of the research on MOPs with irregular Pareto fronts. We start with a brief introduction to the basic con-cepts, followed by a summary of the benchmark test problems with irregular problems, an analysis of the causes of the irregularity, and real-world optimization problems with irregular Pareto fronts. Then, a taxonomy of the existing methodologies for handling irregular problems is given and representative algori-thms are reviewed with a discussion of their strengths and weaknesses. Finally, open challenges are pointed out and a few promising future directions are suggested.