Solving constrained multiobjective optimization problems (CMOPs) is highly challenging due to the presence of complicated feasible regions, intense conflicts among objectives, and unevenly distributed constraints. As a result, conventional methods relying on a single constraint-handling mechanism frequently fail to maintain a stable equilibrium among solution feasibility, diversity, and convergence. To overcome these bottlenecks, this article introduces AFFCMO, a novel adaptive feasibility-guided framework tailored for constrained multiobjective optimization. At its core, the proposed approach utilizes a coevolutionary dual-population architecture that divides the search process into two distinct tasks. Specifically, an auxiliary population is tasked with global exploration, while a primary population focuses on the intensive exploitation of discovered feasible areas. To achieve this, the primary population leverages a DE/current-to-pbest/1 differential evolution strategy to closely approximate the constrained Pareto front. Simultaneously, the auxiliary population expands the search space using a mutation operator that adapts to the current evolutionary stage. Furthermore, exploration is bolstered by a multicriterion environmental selection scheme designed for the auxiliary group. By combining Euclidean geometric distributions, constraint relaxation, and value modeling inspired by epidemic dynamics, this strategy successfully preserves valuable infeasible solutions that can guide the search. Additionally, a dynamic resource allocation strategy based on historical search feedback and Thompson sampling is incorporated. This mechanism continuously evaluates the recent search contributions of both populations and adaptively adjusts their offspring sizes, thereby reducing the bias introduced by static allocation schemes. This mechanism continuously assesses the actual search contributions of both populations, allowing for the adaptive resizing of offspring generations and thereby eliminating the inherent biases of static allocation methods. Comprehensive empirical evaluations are conducted on 47 benchmark problems from four distinct test suites. The results indicate that AFFCMO significantly outperforms seven contemporary multiobjective evolutionary algorithms in terms of exploring complex feasible regions, preserving solution diversity, and achieving high convergence accuracy.

Achieving a dynamic equilibrium among feasibility, convergence, and diversity remains a fundamental challenge in Constrained Multi-objective Optimization Problems (CMOPs). To address the limitations of conventional methods in handling complex constraints and resource allocation, this paper proposes a Dual-Population Cooperative Evolutionary Algorithm based on Relaxed Feasibility Selection and Shrinking Contribution Resource Allocation (RFSCMOEA). First, a relaxed feasibility selection strategy is designed with a dynamically shrinking threshold, allowing near-feasible solutions to survive in early stages to enhance boundary exploration. Second, a dual-criterion environmental selection mechanism integrates non-dominated sorting with k-nearest neighbor density estimation to prevent premature convergence and ensure solution uniformity. Furthermore, a dynamic resource allocation model optimizes computational configuration by adjusting offspring generation ratios based on the real-time evolutionary contribution of each population. Extensive experiments on 47 benchmark functions and 12 real-world engineering problems demonstrate that RFSCMOEA significantly outperforms eight state-of-the-art algorithms in Feasibility Rate, Inverted Generational Distance, and Hypervolume.