TS-SSA: An improved two-stage sparrow search algorithm for large-scale many-objective optimization problems

Abstract

Large-scale many-objective optimization problems (LSMaOPs) are a current research hotspot. However, since LSMaOPs involves a large number of variables and objectives, state-of-the-art methods face a huge search space, which is difficult to be explored comprehensively. This paper proposes an improved sparrow search algorithm (SSA) that manages convergence and diversity separately for solving LSMaOPs, called two-stage sparrow search algorithm (TS-SSA). In the first stage of TS-SSA, this paper proposes a many-objective sparrow search algorithm (MaOSSA) to mainly manages the convergence through the adaptive population dividing strategy and the random bootstrap search strategy. In the second stage of TS-SSA, this paper proposes a dynamic multi-population search strategy to mainly manage the diversity of the population through the dynamic population dividing strategy and the multi-population search strategy. TS-SSA has been experimentally compared with 10 state-of-the-art MOEAs on DTLZ and LSMOP benchmark test problems with 3-20 objectives and 300-2000 decision variables. The results show that TS-SSA has significant performance and efficiency advantages in solving LSMaOPs. In addition, we apply TS-SSA to a real case (automatic test scenarios generation), and the result shows that TS-SSA outperforms other algorithms on diversity.