Effect of chaos on the performance of spider wasp meta-heuristic optimization algorithm for high-dimensional optimization problems

The spider wasp optimization (SWO) algorithm is a new nature-inspired meta-heuristic optimization algorithm based on the hunting, nesting, and mating behaviors of female spider wasps. This paper aims to apply chaos theory to the steps of the SWO algorithm in order to increase its convergence speed. Four versions of chaotic algorithms are constructed using the traditional spider wasp optimizer. The proposed chaotic spider wasp optimization (CSWO) algorithms select various chaotic maps and adjust the main parameters of the SWO optimizer to ensure the balance between exploration and exploitation stages. Furthermore, the constructed CSWO algorithms are benchmarked on eight well-known test functions divided into unimodal and multimodal problems. The experimental results and statistical analysis are carried out to demonstrate that CSWO algorithms are very suitable for searching optimal solutions for the benchmark functions. Specifically, the implementation of chaotic maps can significantly enhance the performance of the SWO algorithm. As a result, the new algorithm has high flexibility and outstanding robustness, which we can apply to engineering design problems.