IMSCSO: An Intensified Sand Cat Swarm Optimization With Multi-Strategy for Solving Global and Engineering Optimization Problems
Xuewei Li, Yonglan Qi, Qian Xing, Yongtao Hu
Abstract
Optimization challenges are becoming more complex as the world advances. Since deterministic and heuristic approaches are no longer sufficient to deal with such complex problems, metaheuristics have recently emerged as a viable option to address optimization difficulties. Since Sand Cat Swarm Optimization (SCSO) is a famous meta-heuristic algorithm, SCSO has a weak ability to balance search between exploration and exploitation and slow convergence, so it may not be effective in finding the global optima, particularly for complex problems. Hence, this paper proposes an intensified SCSO with multiple strategies (IMSCSO). The performance of the IMSCSO algorithm was evaluated on 23 standard test functions and test suites of CEC 2017, CEC 2019, and CEC 2020. Experimental results show that the IMSCSO algorithm performs significantly better than or is on par with other state-of-the-art optimizers. The statistical results obtained from the Wilcoxon signed-rank test and the Friedman test also indicate that the IMSCSO algorithm has a high ability to significantly outperform and rank first among all methods. Moreover, seven typical engineering issues were employed to estimate the efficacy of IMSCSO in optimizing constrained problems. The experimental findings show that the suggested IMSCSO method can efficiently handle real-world application issues.