Hybrid cat swarm optimization with war strategy optimization for solving optimization problems

Abstract

The user-friendly and adaptable nature-inspired meta-heuristic algorithms have significantly contributed to their increasing appeal across several scientific fields, including computer science, mathematics, artificial intelligence, and operations research. The algorithms are constructed upon the core notions of exploration and exploitation. This study presents a fresh technique to optimizing issues by utilizing the War Strategy Optimization (WSO) method. The recommended technique involves utilizing the WSO algorithm in conjunction with the Cat Swarm Optimization (CSO) algorithm. The term used to describe this algorithm that combines different methods is C-WSO. The implementation of swarm intelligence approaches has led to the improvement of the capabilities of both algorithms.
A total of fifty benchmark test functions were adopted to assess the efficacy of the newly proposed C-WSO approach. Definite functions exhibited multimodality, while others were unimodal, and they were executed across diverse dimensions. Our investigations revealed that the C-WSO algorithm outperformed the original WSO approach. The method's performance has been assessed using a diverse range of measures, including the median, mean, and standard deviation of the fitness function values. Repeated evidence has exposed that the C-WSO approach surpasses the WSO algorithm in terms of effectiveness, creating it a viable and practical choice for solving complex optimization problems.