A Thompson Sampling-Based Sparse Evolutionary Operator for Sparse Large-Scale Multiobjective Optimization
针对稀疏大规模多目标优化问题中变量多且最优解大多为零的难点，受强化学习启发，提出一种基于汤普森抽样的稀疏进化算子，帮助算法从高维密集空间转向稀疏空间，在三个实际问题和八个基准问题上表现良好。
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.