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IEEE/CAA Journal of Automatica Sinica

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K. Qiao, J. Liang, K. Yu, X. Ban, C. Yue, B. Qu, and P. N. Suganthan, “Constraints separation based evolutionary multitasking for constrained multi-objective optimization problems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 8, pp. 1819–1835, Aug. 2024. doi: 10.1109/JAS.2024.124545
Citation: K. Qiao, J. Liang, K. Yu, X. Ban, C. Yue, B. Qu, and P. N. Suganthan, “Constraints separation based evolutionary multitasking for constrained multi-objective optimization problems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 8, pp. 1819–1835, Aug. 2024. doi: 10.1109/JAS.2024.124545

Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems

doi: 10.1109/JAS.2024.124545
Funds:  This work was supported in part by the National Key Research and Development Program of China (2022YFD2001200), the National Natural Science Foundation of China (62176238, 61976237, 62206251, 62106230), China Postdoctoral Science Foundation (2021T140616, 2021M692920), the Natural Science Foundation of Henan Province (222300420088), the Program for Science & Technology Innovation Talents in Universities of Henan Province (23HASTIT023), and the Program for Science & Technology Innovation Teams in Universities of Henan Province (23IRTSTHN010)
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  • Constrained multi-objective optimization problems (CMOPs) generally contain multiple constraints, which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions, thus they propose serious challenges for solvers. Among all constraints, some constraints are highly correlated with optimal feasible regions; thus they can provide effective help to find feasible Pareto front. However, most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints, and do not consider judging the relations among constraints and do not utilize the information from promising single constraints. Therefore, this paper attempts to identify promising single constraints and utilize them to help solve CMOPs. To be specific, a CMOP is transformed into a multitasking optimization problem, where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint respectively. Besides, an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance relationships. Moreover, an improved tentative method is designed to find and transfer useful knowledge among tasks. Experimental results on three benchmark test suites and 11 real-world problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.

     

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