A constrained multi-objective evolutionary algorithm via separate exploration and united exploitation strategy

Efficient allocation of search resources is paramount in solving constrained multi-objective optimization problems (CMOPs). This task becomes particularly challenging when striving to strike a balance among diversity, convergence, and feasibility, especially in CMOPs with intricate infeasible regions. To address this issue, we present an algorithm with two complementary search stages for efficient dynamic resource allocation on diversity, convergence, and feasibility. Firstly, the separate exploration stage independently explores the unconstrained and constrained Pareto fronts, efficiently traversing complex infeasible zones. Subsequently, in the united exploitation stage, the searches collaboratively exploit the constrained Pareto front. Furthermore, a $\theta$-constraint dominance principle-based environmental selection is incorporated to achieve a balance between constraint convergence and diversity. Comprehensive tests on 47 problems across four benchmark suites and 6 real-world CMOPs reveal that the proposed algorithm outperforms six state-of-the-art algorithms, demonstrating its superior efficacy.