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Dec.  2025

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  >  12(12): 2455-2472
W. Huang, R. Wang, T. Zhang, S. Qi, and L. Wang, “Fuzzy constraint dominance strategy for constrainted multiobjective optimization problems with multiple constraints,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 12, pp. 2455–2472, Dec. 2025. doi: 10.1109/JAS.2025.125255
Citation: W. Huang, R. Wang, T. Zhang, S. Qi, and L. Wang, “Fuzzy constraint dominance strategy for constrainted multiobjective optimization problems with multiple constraints,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 12, pp. 2455–2472, Dec. 2025. doi: 10.1109/JAS.2025.125255
shu

Fuzzy Constraint Dominance Strategy for Constrainted Multiobjective Optimization Problems With Multiple Constraints

doi: 10.1109/JAS.2025.125255
Funds:  This work was supported by the Scientific Research Project of Xiang Jiang Lab (22XJ02003), the University Fundamental Research Fund (23-ZZCX-JDZ-28), the National Science Fund for Outstanding Young Scholars (62122093), the National Natural Science Foundation of China (72421002, 62403477), the Science and Technology Innovation Program of Hunan Province (ZC23112101-10), and the Hunan Natural Science Foundation Regional Joint Project (2023JJ50490)
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    Abstract
  • Solving constrained multiobjective optimization problems (CMOPs) is a highly challenging work. Numerous complex nonlinear constraints significantly add to the complexity of CMOPs, resulting in an exceptionally intricate feasible region. Makes it difficult for the algorithm to search for the complete constraint PF. In addition, under the influence of multiple complex nonlinear constraints, the conventional calculation method of overall constraint violation is inefficient for assessing the quality of infeasible solutions, potentially misguiding the evolutionary direction of the population. In response to these challenges, this paper proposes the fuzzy constraint dominance strategy (FCDS). This novel approach facilitates nuanced comparisons of solutions to strike a better balance between objectives and constraints. The fuzzy constraint violation introduced in FCDS mitigates the misleading impact of complex nonlinear constraints. Moreover, FCDS divides the solution process of complex CMOP into multiple stages from easy to difficult, and uses adaptive methods to increase the difficulty level of the problem. Systematic experiments on four test suites and three real-world applications have conclusively demonstrated the superior competitiveness of FCDS against leading algorithms.

     

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