Constrained multiobjective optimization with compression mapping mechanism
Yongchao Li, Heming Jia, Riqing Chen, Xinyan Lin, Yangqin Feng · Journal of Computational Design and Engineering · 2026
Abstract The resolution of constrained multiobjective optimization problems involves simultaneously optimizing multiple objectives while adhering to specific constraints. Effective constrained multiobjective optimization solvers must strike a balance between objective optimization and constraint satisfaction. This study introduces a novel approach that compresses the mapping between the objective and constraint spaces to obscure their explicit information. This compression is achieved by normalizing both spaces and projecting them onto an $n$-dimensional spherical volume. The key advantage of this compression mapping is that it reduces fine-grained information redundancy while retaining the dominant convergence trend toward the ideal region, thereby providing a compact surrogate view for selection under constraints. Although this transformation inevitably causes some information loss, it retains the key property of convergence toward the origin. Based on the mapped space, a selection mechanism combining fast non-dominated sorting and crowding distance is employed to guide the population across infeasible regions and maintain diversity. To further enhance computational efficiency, a dynamic archiving strategy is introduced. This mechanism regulates the number and quality of archived individuals, storing only valuable solutions and comparing offspring with existing archives to prevent redundant evaluations. These components are integrated into a new constrained multiobjective evolutionary algorithm. The algorithm is comprehensively validated on 42 benchmark functions and 12 real-world problems. Experimental results demonstrate that the proposed method achieves superior performance compared to state-of-the-art constrained multiobjective evolutionary algorithms in terms of convergence and computational efficiency.