Evolutionary multiobjective optimization via efficient sampling-based offspring generation

With the rising number of large-scale multiobjective optimization problems from academia and industries, some evolutionary algorithms (EAs) with different decision variable handling strategies have been proposed in recent years. They mainly emphasize the balance between convergence enhancement and diversity maintenance for multiobjective optimization but ignore the local search tailored for large-scale optimization. Consequently, most existing EAs can hardly obtain the global or local optima. To address this issue, we propose an efficient sampling-based offspring generation method for large-scale multiobjective optimization, where convergence enhancement and diversity maintenance, together with ad hoc local search, are considered. First, the decision variables are dynamically classified into two types for solving large-scale decision space in a divide-and-conquer manner. Then, a convergence-related sampling strategy is designed to handle those decision variables related to convergence enhancement. Two additional sampling strategies are proposed for diversity maintenance and local search, respectively. Experimental results on problems with up to 5000 decision variables have indicated the effectiveness of the algorithm in large-scale multiobjective optimization.