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

• Published in: IEEE/CAA journal of automatica sinica Vol. 8; no. 2; pp. 303 - 318
• Main Authors: Hua, Yicun, Liu, Qiqi, Hao, Kuangrong, Jin, Yaochu
• Format: Journal Article
• Language: English
• Published: Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.02.2021; College of Information Sciences and Technology, Donghua University, Shanghai 201620, China%Department of Computer Science, University of Surrey, Guildford, Surrey GU27XH, U.K%College of Information Science and Technology, Donghua University, Shanghai 201620, China; Department of Computer Science, University of Surrey, Guildford, Surrey GU27XH, U.K
• Subjects: Benchmark testing; Evolutionary algorithm; Evolutionary algorithms; Evolutionary computation; Genetic algorithms; irregular Pareto fronts; machine learning; Mopping; multiobjective optimization problems (MOPs); Multiple objective analysis; Optimization; Pareto optimization; Taxonomy; irregular Pareto fronts; machine learning; multi-objective optimization problems (MOPs); Evolutionary algorithm
• ISSN: 2329-9266; 2329-9274
• DOI: 10.1109/JAS.2021.1003817

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 concepts, 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 algorithms are reviewed with a discussion of their strengths and weaknesses. Finally, open challenges are pointed out and a few promising future directions are suggested.