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IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  > In Press, Accepted Manuscript
X. Ban, J. Liang, K. Qiao, K. Yu, Y. Wang, J. Peng, and B. Qu, “A decision variables classification-based evolutionary algorithm for constrained multi-objective optimization problems,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125276
Citation: X. Ban, J. Liang, K. Qiao, K. Yu, Y. Wang, J. Peng, and B. Qu, “A decision variables classification-based evolutionary algorithm for constrained multi-objective optimization problems,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125276
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A Decision Variables Classification-Based Evolutionary Algorithm for Constrained Multi-Objective Optimization Problems

doi: 10.1109/JAS.2025.125276
Funds:  This work was supported in part by National Natural Science Foundation of China (U23A20340, 62176238, 61806179, 61876169, 62106230 and 61976237), National Natural Science Fund for Outstanding Young Scholars of China (61922072), Key R&D projects of the Ministry of Science and Technology of China (2022YFD2001200), China Postdoctoral Science Foundation (2020M682347, 2021T140616, 2021M692920), Training Program of Young Backbone teachers in Colleges and universities in Henan Province (2020GGJS006), Henan Provincial Young Talents Lifting Project (2021HYTP007), Natural Science Foundation project of Henan Province (242300420277), and Chongqing University of Posts and Telecommunications Key Laboratory of Big Data open fund project (BDIC-2023-B-005)
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    Abstract
  • Solving constrained multi-objective optimization problems (CMOPs) is a challenging task due to the presence of multiple conflicting objectives and intricate constraints. In order to better address CMOPs and achieve a balance between objectives and constraints, existing constrained multi-objective evolutionary algorithms (CMOEAs) predominantly focus on devising various strategies by leveraging the relationships between objectives and constraints, and the designed strategies usually are effective for the problems with simple constraints. However, these methods most ignore the relationship between decision variables and constraints. In fact, the essence of optimization is to find appropriate decision variables to meet various complex constraints. Therefore, it is hoped that the problem can be analyzed from the perspective of decision variables, so as to obtain more excellent results. Based on the above motivation, this paper proposes a decision variables classification approach, according to the relationship between decision variables and constraints, variables are divided into constraint-related variables ($ CR $) and constraint-independent variables ($ CI $). Consequently, by optimizing these two types of variables independently, the population can sustain a favorable balance between feasibility and diversity. Furthermore, specific offspring generation strategies are proposed for the two categories of decision variables in order to achieve rapid convergence while maintaining population diversity. Experimental results on 31 test problems as well as 20 real-world problems demonstrate that the proposed algorithm is competitive compared to some state-of-the-art constrained multi-objective optimization algorithms.

     

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