Dynamic Bayesian Gaussian Mixture Model Based Decision Variable Grouping for Large-Scale Multi-Objective Optimization

Large-scale multi-objective optimization problems (LSMOPs) typically involve hundreds or thousands of decision variables, posing significant challenges for evolutionary algorithms in balancing convergence efficiency and population diversity. To address these challenges, this paper proposes a dynamic Bayesian Gaussian mixture model based decision variable grouping evolutionary algorithm (TDGEA). First, a dynamic Bayesian Gaussian mixture model framework is employed to formulate the problem and perform preliminary grouping of decision variables, which categorizes variables into convergence-related variables and diversity-related variables. Then, a dynamic differential grouping strategy is introduced to further partition variables according to their interactions, ensuring strong intra-group coupling and weak inter-group coupling. Finally, an adaptive dual-population cooperative optimization strategy is adopted to independently optimize convergence-related and diversity-related variables. Experimental evaluations on standard benchmark test suites show that the proposed TDGEA consistently outperforms several state-of-the-art large-scale optimization algorithms with respect to convergence speed, optimization accuracy, and scalability.