Improved Chimpanzee Search Algorithm with Multi-Strategy Fusion and Its Application

Abstract

An improved chimpanzee optimization algorithm incorporating multiple strategies (IMSChoA) is proposed to address the problems of initialized population boundary aggregation distribution, slow convergence speed, low precision, and proneness to fall into local optimality of the chimpanzee search algorithm. Firstly, the improved sine chaotic mapping is used to initialize the population to solve the population boundary aggregation distribution problem. Secondly, a linear weighting factor and an adaptive acceleration factor are added to join the particle swarm idea and cooperate with the improved nonlinear convergence factor to balance the global search ability of the algorithm, accelerate the convergence of the algorithm, and improve the convergence accuracy. Finally, the sparrow elite mutation and Bernoulli chaos mapping strategy improved by adaptive change water wave factor are added to improve the ability of individuals to jump out of the local optimum. Through the comparative analysis of benchmark functions seeking optimization and the comparison of Wilcoxon rank sum statistical test seeking results, it can be seen that the IMSChoA optimization algorithm has stronger robustness and applicability. Further, the IMSChoA optimization algorithm is applied to two engineering examples to verify the superiority of the IMSChoA optimization algorithm in dealing with mechanical structure optimization design problems.