A Constrained Multi-Objective Evolutionary Algorithm with Weak Constraint–Pareto Dominance and Angle Distance-Based Diversity Preservation

In recent years, many constrained multi-objective evolutionary algorithms (CMOEAs) have primarily emphasized feasible solutions, overlooking the useful information contained in infeasible ones. This tendency effectively prioritizes feasibility over objective quality, often leading to the premature removal of infeasible solutions with strong convergence or diversity, thereby reducing performance on constrained multi-objective optimization problems (CMOPs) with complex or irregular feasible regions. To overcome these limitations, this paper introduces a weak constraint–Pareto dominance relation that integrates feasibility with objective performance, thereby preventing the premature elimination of infeasible solutions that may offer strong convergence or diversity. Moreover, an angle distance-based diversity maintenance strategy is proposed to preserve population diversity while ensuring solution feasibility. By combining these two mechanisms, we design the CMOEA-WA algorithm. Extensive experiments on benchmark and real-world problems confirm that the proposed method consistently outperforms state-of-the-art CMOEAs, achieving a more effective balance among feasibility, convergence, and diversity.