Journal
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
Volume 8, Issue 2, Pages 303-318Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JAS.2021.1003817
Keywords
Evolutionary algorithm; machine learning; multi-objective optimization problems (MOPs); irregular Pareto fronts
Categories
Funding
- National Natural Science Foundation of China [61806051, 61903078]
- Natural Science Foundation of Shanghai [20ZR1400400]
- Agricultural Project of the Shanghai Committee of Science and Technology [16391902800]
- Fundamental Research Funds for the Central Universities [2232020D-48]
- Project of the Humanities and Social Sciences on Young Fund of the Ministry of Education in China [20YJCZH052]
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This paper provides a comprehensive survey of research on solving multi-objective optimization problems with irregular Pareto fronts, covering basic concepts, benchmark test problems, analysis of irregularity causes, real-world optimization problems, existing methodologies, representative algorithms, strengths, weaknesses, open challenges, and future directions.
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.
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