A Thompson Sampling-Based Sparse Evolutionary Operator for Sparse Large-Scale Multiobjective Optimization

Traditional multiobjective evolutionary algorithms (MOEAs) face challenges when addressing sparse large-scale multiobjective optimization problems (SLSMOPs) with many zero decision variables. The “large-scale” refers to the high dimensionality of the decision space, making it difficult for traditional MOEAs to traverse vast expanses efficiently with limited computational resources. Furthermore, In sparse contexts, most variables in Pareto optimal solutions are zero. It is difficult for traditional MOEAs to identify nonzero variables’ positions efficiently. In reinforcement learning, Thompson sampling employs a probability distribution to estimate each item’s value or success probability. Drawing inspiration from this concept, we propose a Thompson sampling-based sparse evolutionary operator (TSSEO). TSSEO maintains a probability distribution for each variable and utilizes this distribution to recommend for the variable, assisting MOEAs in transitioning from high-dimensionality dense to sparse spaces. Experimental results show that when integrated with representative MOEAs, TSSEO performs competitively in three real-world problems and eight benchmark problems involving up to 10041 decision variables, compared to algorithms designed explicitly for SLSMOPs.