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Feb.  2021

IEEE/CAA Journal of Automatica Sinica

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Yicun Hua, Qiqi Liu, Kuangrong Hao and Yaochu Jin, "A Survey of Evolutionary Algorithms for Multi-Objective Optimization Problems With Irregular Pareto Fronts," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 303-318, Feb. 2021. doi: 10.1109/JAS.2021.1003817
Citation: Yicun Hua, Qiqi Liu, Kuangrong Hao and Yaochu Jin, "A Survey of Evolutionary Algorithms for Multi-Objective Optimization Problems With Irregular Pareto Fronts," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 303-318, Feb. 2021. doi: 10.1109/JAS.2021.1003817

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

doi: 10.1109/JAS.2021.1003817
Funds:  This work was supported in part by the 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), the Fundamental Research Funds for the Central Universities (2232020D-48), and the Project of the Humanities and Social Sciences on Young Fund of the Ministry of Education in China (Research on swarm intelligence collaborative robust optimization scheduling for high-dimensional dynamic decision-making system (20YJCZH052))
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  • 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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    Highlights

    • A survey of evolutionary algorithms for irregular multi-objective optimization problems
    • A definition for irregular Pareto fronts is suggested, illustrated with illustrative examples
    • A list of irregular multi-objective optimization test functions and real-world problems
    • Open questions are discussed and future research directions are suggested.

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