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Journal of System Simulation

Abstract

Abstract: To address the challenges of balancing the constraint satisfaction and objective function optimization, and dealing with the complex feasible regions in constrained multi-objective optimization problems(CMOPs), a classification-based search approach is proposed based on different Pareto front relationships. A dual-population dual-phase framework is proposed in which an auxiliary population Pa and a main population Pm are evolved and the evolution process is divided into a learning phase and a search phase. During the learning phase, Pa explores unconstrained Pareto front (UPF) and Pm explores constrained Pareto front(CPF), through which the relationship between UPF and CPF is determined. After completing the learning phase, the different classified relationships guide the subsequent search strategies. In the search phase, the algorithm adaptively adjusts the search strategy of Pa to provide effective assistance for Pm according to the different classification relationships between UPF and CPF. Based on this framework, Pareto front relationships for different CMOPs are classified to achieve the more effective searching for CPF. Experimental results show that the proposed algorithm has a better performance compared with the seven state-of-the-art constrained multi-objective evolutionary algorithms (CMOEAs). Through learning and utilizing the relationship between UPF and CPF, the more appropriate search strategies can be selected to handle CMOPs with different characteristics and a more advantageous final solution set can be got.

First Page

901

Last Page

914

CLC

TP18

Recommended Citation

Wang Yubo, Hu Chengyu, Gong Wenyin. Handling Constrained Multi-objective Optimization Problems Based on Relationship Between Pareto Fronts[J]. Journal of System Simulation, 2024, 36(4): 901-914.

Corresponding Author

Hu Chengyu

DOI

10.16182/j.issn1004731x.joss.22-1430

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