Constraint-Pareto Dominance and Diversity Enhancement Strategy-Based Evolutionary Algorithm for Solving Constrained Multiobjective Optimization Problems
提出一种约束-帕累托支配关系来平衡目标与可行性，并设计多样性增强策略，在保持可行解多样性的同时保留有潜力的不可行解，实验表明优于现有算法。
The utilization of both constrained and unconstrained-based optimization for solving constrained multi-objective optimization problems (CMOPs) has become prevalent among recently proposed constrained multiobjective evolutionary algorithms (CMOEAs). However, the constrained-based optimization which adopted by many CMOEAs typically gives priority to feasible solutions over infeasible ones regardless of their objective values, potentially leading to degraded performance due to the elimination of promising infeasible solutions with strong convergence and diversity. Furthermore, many existing CMOEAs have difficulty in maintaining diversity while focusing on feasibility, thereby hindering their ability to effectively address CMOPs characterized by complex feasible regions. To tackle these challenges, a constraint-Pareto dominance relationship is proposed in this paper to evaluate solutions based on both objectives and feasibility, to improve the optimization potential by reduce the elimination probability of promising infeasible solutions. A diversity enhancement strategy is also designed to enable simultaneously focus on both diversity and feasibility, thus effectively ensuring the diversity of the feasible solutions obtained. Empirical results from benchmark suites and real-world problems demonstrate that our proposed algorithm surpasses state-of-the-art CMOEAs.