Abstract In constrained multi-objective optimization (CMOP), effectively exploiting infeasible solutions is essential for global exploration and for accurately approximating the constrained Pareto front (CPF). Nevertheless, when feasible regions are sparse or highly fragmented, many existing methods still suffer from slow feasibility attainment, a high proportion of ineffective evaluations, and inadequate front coverage, leading to premature clustering. Following the principle of “broad exploration first, robust convergence later”, DPNCMO (Novelty-augmented Population-Differentiated Cooperative Multi-objective Optimization) is developed as a cooperative dual-population framework that explicitly decouples exploration from exploitation. The main population is initialized via Latin hypercube sampling and evolved using a genetic algorithm equipped with a feasibility-aware adaptive constraint-relaxation mechanism, which progressively tightens the admissible violation level in response to the evolving feasibility state, thereby steering the search from informative infeasible regions toward accurate CPF refinement. In parallel, an assistant population is randomly initialized and evolved using a DE-based operator with a novelty–crowding synergistic diversity-maintenance mechanism. By constructing a behavior space that integrates objective and constraint information, the mechanism emphasizes novelty-driven selection when feasibility is scarce to enhance coverage and suppress clustering, and then gradually shifts toward crowding-driven exploitation once feasibility becomes sufficient to stabilize convergence and control computational overhead. Collectively, the population-differentiated cooperation, feedback-driven constraint relaxation, and stage-wise novelty-guided selection reduce ineffective evaluations, accelerate feasibility climbing, and improve CPF coverage and robustness on fragmented feasible landscapes. Extensive experiments on 55 CMOP benchmark instances and 12 real-world engineering problems demonstrate that DPNCMO achieves superior or at least comparable performance to representative state-of-the-art optimizers across convergence, distribution, and feasibility, with consistent improvements across multiple metrics.
Chen et al. (Mon,) studied this question.