Abstract: The chimp optimization algorithm (ChOA) is characterized by high population diversity and fast convergence speed. However, there remains room for improvement in its search capability and methods for escaping from local optima. Therefore, this paper proposes an improved chimp optimization algorithm based on multi-strategy fusion. Firstly, a double-cross infinite-fold iterative chaotic map is introduced to initialize the population, enhancing the quality of initial solutions and facilitating subsequent optimization by the algorithm. Subsequently, a hybrid position update mechanism that combines  sinecosine weight factors and an individual best following strategy is employed to update individual positions, thereby improving the algorithm’s optimization capability and convergence accuracy. Finally, a CauchyGaussian variation mechanism is introduced to mutate the current best individual, and a greedy selection strategy is used to select the optimal individual, enhancing the algorithm’s ability to escape local optima. In numerical experiments, the Wilcoxon rank sum test is utilized to comparatively analyze the optimization performance of the improved algorithm using 10 benchmark functions. The results demonstrate that the proposed algorithm exhibits enhanced optimization performance compared to the compared algorithms and further validates its effectiveness in solving 3D path planning problems.

Key words: chimp optimization algorithm(ChOA), 2D sine ICMIC double cross map（2D-SIDCM）, sinecosine weight factor, individual best following strategy, CauchyGaussian variation, path planning