A Sparse Region Aware Based Constrained Multi-Objective Optimization Algorithm

When addressing constrained multi-objective optimization problems (CMOPs), complex feasible region structures often hinder existing constrained multi-objective evolutionary algorithms (CMOEAs) from maintaining a proper balance between convergence and diversity, leading to incomplete coverage of constrained Pareto front (CPF). To address this issue, we developed a sparse region aware based constrained multi-objective optimization algorithm (SRACMO) using a dual-population co-evolutionary framework. We define a metric called region potential metric (RPM) to quantify the exploration value of different regions in the objective space and incorporate RPM into the parent selection process, enabling the algorithm actively to identify and utilize individuals located in sparse regions and boundaries, thereby enhancing the coverage ability of unexplored regions. In addition, a population state detection mechanism based on multi-index is designed to distinguish between progressive state and stagnant state. When the population gets stuck in stagnation, a recovery strategy combining regional collaborative mating and adaptive ϵ -constraint relaxation is introduced to enhance the regional search ability. Experimental results on 33 benchmark problems show that SRACMO achieves competitive overall performance compared with other 8 classic CMOEAs, especially performing stronger frontier coverage ability in complex feasible region structures.