Constrained multiobjective optimization problems (CMOPs) are prevalent in engineering and scientific applications, where the core difficulty is to simultaneously optimize multiple conflicting objectives subject to complex constraints. Existing multi-population co-evolutionary algorithms often suffer from imbalanced resource allocation and insufficient adaptability when coordinating feasible-region exploitation with exploratory search beyond the feasible boundary, which limits their overall effectiveness. To address these issues, this paper proposes an Information-Competitive Dual-Population Evolutionary Algorithm (ICDEA). The proposed framework establishes an information-competitive interaction mechanism between a primary population and an auxiliary population to enable adaptive allocation of search resources. The primary population performs intensive exploitation within the feasible region to promote convergence and constraint satisfaction, whereas the auxiliary population conducts broader exploration over infeasible and boundary-adjacent regions to maintain search coverage and support diversity preservation. In addition, a dynamic offspring allocation strategy, termed InfoGameAlloc, is designed to regulate inter-population resource distribution by jointly considering convergence tendencies, feasibility status, population diversity, distributional information volume, and improvement potential. Extensive experimental evaluations on 47 benchmark problems from the CF, DAS-CMOP, LIR-CMOP, and MW test suites and 12 real-world engineering problems, in comparison with nine competitive constrained multiobjective evolutionary algorithms, demonstrate that ICDEA achieves competitive and well-balanced overall performance in terms of convergence, solution distribution quality, and feasible solution ratio. These results indicate that the proposed framework provides an effective and adaptive way to coordinate feasible-region exploitation and exploratory search in constrained multiobjective optimization.
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