A dual-population based two-archive coevolution algorithm for constrained multi-objective optimization problems

How to balance the objectives and constraints better is the key to solving constrained multi-objective optimization problems (CMOPs). Many evolutionary algorithms struggle to fully converge to the entire Pareto front, especially in CMOPs with narrow and complex feasible regions, which posing signifi...

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Bibliographic Details
Published in:	Engineering applications of artificial intelligence Vol. 158; p. 111419
Main Authors:	Chen, Miao, Zhao, Shijie, Zhang, Tianran, Zhang, Lei
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 15.10.2025
Subjects:	Constrained multi-objective optimization Dual-population Evolutionary algorithm Removal mechanism Two-archive Constrained multi-objective optimization Two-archive Removal mechanism Dual-population Evolutionary algorithm
ISSN:	0952-1976
Online Access:	Get full text
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Summary:	How to balance the objectives and constraints better is the key to solving constrained multi-objective optimization problems (CMOPs). Many evolutionary algorithms struggle to fully converge to the entire Pareto front, especially in CMOPs with narrow and complex feasible regions, which posing significant challenges in solving CMOPs. To handle this problem, the paper proposes a dual-population based two-archive coevolution algorithm (DPTAC). The main population evolves towards the true Pareto front while accounting for considering the original problem. The auxiliary population ignores the constraints and approximates unconstrained Pareto front. To assist main population in crossing larger infeasible regions, enhancing its diversity, and discovering more feasible regions, a two-archive strategy is proposed, which stores the potentially valuable non-dominated infeasible solutions and non-dominated solutions generated by the evolution of the main population and the auxiliary population respectively. In addition, a removal mechanism is introduced and integrated into the auxiliary population to reduce computational resource waste. This can help the main population have more computational resources in the late stage of evolution to find narrow feasible regions and improve the convergence of the population. Experimental results demonstrate that DPTAC outperforms 9 state-of-the-art constrained multi-objective evolutionary algorithms (CMOEAs) across 5 test suites comprising 62 benchmark functions and 6 real-world problems, confirming its superior competitiveness.
ISSN:	0952-1976
DOI:	10.1016/j.engappai.2025.111419