Balancing convergence and diversity while traversing infeasible regions remains a formidable challenge in constrained multi-objective optimization problems (CMOPs) with discrete or narrow feasible domains. However, existing multi-population methods typically employ static collaboration strategies, which sometimes struggle to adapt to the differentiated demands of search behaviors across different evolutionary stages. To address this issue, this paper proposes a two-stage multi-population constrained multi-objective evolutionary algorithm with a dynamic collaborative mechanism (TMDCEA). The optimization process of TMDCEA is partitioned into two distinct stages: global exploration and local exploitation. In the first stage (the global exploration stage), the algorithm establishes a weak coevolutionary mode among a main population and two auxiliary populations (a diversity-enhancing auxiliary population and an unconstrained auxiliary population). In the second stage (the local exploitation stage), the algorithm establishes a strong coevolutionary mode between the main population and the diversity-enhancing auxiliary population. Through this dynamic coevolutionary framework, the algorithm achieves an excellent balance among convergence, feasibility, and diversity. To drive the algorithm towards the constrained Pareto front while maintaining solution diversity, this paper employs a reference vector-based diversity-enhancing strategy in the environmental selection of the diversity-enhancing auxiliary population. Furthermore, a dynamic constraint-guided environmental selection strategy is proposed for the main population, which assists the population in progressively traversing vast infeasible regions by dynamically activating constraints step by step. Experimental results on three CMOP benchmark test suites and twelve real-world CMOPs demonstrate that, compared with ten state-of-the-art algorithms, TMDCEA exhibits competitive performance in handling complex constrained problems.
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