Prey-Impatience-Driven Sand Cat Swarm Optimization with Perturbation Learning for Global Optimization and Engineering Applications

Abstract

Sand Cat Swarm Optimization (SCSO) is a swarm intelligence algorithm characterized by a simple structure and a small number of control parameters. However, when solving complex optimization problems, SCSO suffers from several limitations, including an uneven initial population distribution, excessive dependence on the current best individual during the search process, insufficient local exploitation accuracy, and susceptibility to local optima. To address these limitations, a Collaborative Multi-Strategy Sand Cat Swarm Optimization algorithm (CMSCSO) is proposed. The good point set method is adopted to generate a uniformly distributed initial population. An adaptive random reuse strategy is designed to selectively inherit dimensional information from the best individual according to differences in individual fitness. A prey impatience coefficient is introduced to dynamically adjust the local search intensity according to the distance between the population and the current best solution. In addition, a refractive-mechanism-based opposition-based learning strategy for the worst individuals is incorporated to update low-quality individuals and improve the ability of the algorithm to escape from local optima. CMSCSO was evaluated using the 30-dimensional CEC2017 and 10-dimensional CEC2022 benchmark suites. Its performance was compared with that of SCSO and several recently developed metaheuristic algorithms. The experimental results show that CMSCSO achieved the best mean values on 24 of the 29 CEC2017 benchmark functions and on 10 of the 12 CEC2022 benchmark functions. In the Wilcoxon tests conducted on CEC2017 and CEC2022, CMSCSO achieved 220 and 90 statistically significant wins, respectively. It also ranked first in the Friedman tests for both benchmark suites. For engineering optimization problems, the results obtained from six types of engineering design problems demonstrate that CMSCSO can consistently obtain high-quality feasible solutions that satisfy the specified constraints. In two-dimensional and three-dimensional wireless sensor network coverage optimization problems, coverage rates of 96.30% and 89.54% were achieved. For photovoltaic model parameter identification, CMSCSO achieved the highest identification accuracy. The numerical and engineering test results demonstrate that CMSCSO provides high optimization accuracy, strong stability, and good adaptability to complex engineering problems. It can therefore serve as an effective solution method for optimization tasks in structural design, mechanical engineering, and other related fields.