Chaotic sparrow search algorithm with manta ray spiral foraging for engineering optimization

Abstract

Targeting the problem of traditional sparrow search algorithms being prone to falling into local optima, a new algorithm called the Chaotic Sparrow Search Algorithm with Manta Ray Spiral Foraging (abbreviated as MSSA) is proposed. The Logistic-Sine-Cosine chaotic map and elite Reverse learning strategy are fused to initialize the population. It is experimentally demonstrated that this hybrid strategy outperforms the population after random initialization in reducing ineffective sparrow individuals. In the vigilante update stage, the spiral foraging behaviour of the manta ray population in the integrated manta ray optimization algorithm, the sparrows search around the best food source, which enhances the sparrow search algorithm's ability to explore the optimal solution. To enhance the stability of the algorithm to search for the optimum, a mixed Gaussian variational and logistic perturbation strategy is proposed to further improve the performance of the algorithm. Finally, using 12 commonly used benchmark test functions and the Wilcoxon rank sum test, MSSA was compared with other original algorithms and advanced improved algorithms, it is demonstrated that MSSA has higher accuracy and convergence performance, and the improved MSSA algorithm is applied to three types of engineering optimization problems with constraints, demonstrating its feasibility and effectiveness.