An Improved Sand Cat Swarm Operation and Its Application in Engineering

Meta-heuristic algorithms have emerged as a popular approach for solving engineering optimization problems. In this paper, an Improved Sand Cat Swarm Operation (ISCSO) is proposed and applied to optimize double-layer spraying path parameters. The Sand Cat Swarm Operation (SCSO) has some limitations, such as poor initial population quality, slow convergence speed, and a tendency to fall into local optima. To overcome these limitations, three improvement strategies are introduced in ISCSO. Firstly, the SPM chaotic mapping is used to enhance the initial population quality. Secondly, a nonlinear cycle adjustment strategy is introduced to balance global exploration and local exploitation, thereby accelerating the convergence speed. Finally, integrating the Immune Algorithm (IA) enables ISCSO to avoid falling into local optima, resulting in improved solution accuracy. Furthermore, we extended our experiments to include 21 low-dimensional functions and 15 test functions of LSOPs, where ISCSO was compared with seven other popular algorithms. The experimental results highlight the promising performance of ISCSO in solving different types of functions, achieving both higher solution accuracy and faster convergence speed. In particular, the effectiveness of ISCSO has been demonstrated through experiments aimed at optimizing the parameters of the double-layer spraying path. The results of these experiments further highlight the utility of ISCSO in tackling challenging optimization problems.