Improved Chimp optimization algorithm with multi-strategy integration

Abstract

Aiming at the shortcomings of Chimp optimization algorithm, such as easy to fall into local optimum, slow convergence speed and low convergence accuracy, an improved Chimp optimization algorithm based on multi-strategy fusion is proposed. Firstly, the population was initialized by nonlinear control parameter strategy, which enhance the quality of the initial individuals and the diversity of the population, and lay the foundation for the global optimization of the algorithm. Secondly, by improving position updating process of chimp, the leader position of the attacker chimpanzee is reflected and the global optimization ability of the algorithm is improved. Finally, The Cauchy-Gauss mutation strategy was used to improve the ability of maintaining population diversity, improve the convergence accuracy and speed of the algorithm. Eleven bench mark test functions with different characteristics are optimizated. The test results and Wilcoxon’s signed rank test results both show that the improved algorithm has better optimization accuracy, convergence performance and stability.