A Two-phase Constrained Multi-Objective Evolutionary Algorithm Based on the Constrained Decomposition Approach

As existing multi-objective constraint handling methods have defiencies under the complex constraints, a constrained multi-objective optimization algorithm (C-TPEA) with two-phase constraint handling is proposed in this paper. Unlike the existing algorithms, which pay more attention to feasibility, C-TPEA aims to better balance convergence, diversity and feasibility. In the first phase, C-TPEA explores the entire space without considering the constraints, the working population can go through the complex infeasible regions and avoid local optimum. In the second phase, the algorithm adds feasibility considerations and the working population gradually converges to the constraint boundary. In the experimental studies, the performance of C-TPEA on CMOPs has been verified.