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Volume 11 Issue 2
Feb.  2024

IEEE/CAA Journal of Automatica Sinica

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Article Contents
F. Ming, W. Gong, and  Y. Jin,  “Even search in a promising region for constrained multi-objective optimization,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 474–486, Feb. 2024. doi: 10.1109/JAS.2023.123792
Citation: F. Ming, W. Gong, and  Y. Jin,  “Even search in a promising region for constrained multi-objective optimization,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 474–486, Feb. 2024. doi: 10.1109/JAS.2023.123792

Even Search in a Promising Region for Constrained Multi-Objective Optimization

doi: 10.1109/JAS.2023.123792
Funds:  This work was partly supported by the National Natural Science Foundation of China (62076225)
More Information
  • In recent years, a large number of approaches to constrained multi-objective optimization problems (CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly fine-tuned strategy or technique might overfit some problem types, resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general properties. First, the constrained Pareto front (CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance (i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search, which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs.

     

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  • 1 Overfit in this work indicates that a method performs extremely well on some problems but unusable (i.e., cannot find a desirable number of applicable solutions) on others.
    2 Evidence can be found in Secion IV, where we perform various experiments to test performances of different CMOEAs on different CMOPs. The results clearly reveal that some CMOEAs using a specific or complicated strategy (e.g., BiCo and MFOSPEA2) perform very well on one kind of CMOP but cannot handle other features of CMOPs.
    3 Detailed discussions and analyses on all experimental results can be found in the supplementary file at: https: //wewnyin.github.io/wenyingong/Publication/CMOES-supp.pdf.
    4 Dimensions of m and n of the selected benchmarks are reported in Table S-I in the Supplementary file.
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    Highlights

    • A promising region concept that includes the CPF and possesses good properties
    • An even search method utilizing valuable solutions in the promising region to search the CPF
    • A new two-stage CMOEA that implements the even search method

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