An Adaptive Hybrid Genetic Algorithm and Differential Evolution Strategy with Sub-constraint Population Combination Approach for Multiconstraint Multiobjective Optimization

Multiconstraint multi-objective optimization problems (mCMOP) are extremely challenging, as multiple conflicting objectives subject to various multi-constraints must be simultaneously optimized. Current approaches cannot effectively balance convergence and diversity, and trade-off multiconstraint and multiobjective optimization remains challenging. In this paper, we propose an Adaptive Hybrid Genetic Algorithm and Differential Evolution strategy with a Sub-constraint Population Combination Approach (AHGADE-SCPCA), introducing a sub-constraint population combination (SCPC) framework that integrates an adaptive hybrid genetic algorithm and differential evolution (HGADE) strategy. Precisely, the adaptive HGADE strategy dynamically balances exploration and exploitation by integrating the global search capabilities of GA with the local refinement strengths of DE. The SCPC approach categorizes each two sub-constraint population (SCP) into distinct groups based on their constraint pareto fronts (CPF), enabling targeted recombination to enhance the balance of convergence and diversity. Experiment results on mCMOP benchmarks demonstrate that the proposed method achieves superior performance compared to six state-of-the-art methods.