Abstract: To address the defects of the Chinese pangolin optimization algorithm(CPO), such as uneven initial population distribution, a tendency to fall into local optima in high dimensions, and convergence oscillation in the later stage, an improved Chinese pangolin optimization algorithm(ICPO) based on multi-strategy enhancement is proposed. The Logistic chaotic map is integrated to optimize the initial population distribution and improve the coverage of the solution space. A dynamically triggered Cauchy mutation mechanism is introduced to enhance the ability of the algorithm to escape from local optima. A full-process elite strategy is designed, including elite pool construction, guided update, and protection mechanisms, to strengthen the guidance of high-quality solutions and improve convergence stability. In the simulation experiments, 12 test functions are adopted to compare the performance of ICPO with that of CPO and other seven swarm intelligence optimization algorithms. Friedman and Wilcoxon rank-sum tests confirm that its performance advantages are statistically significant. The proposed algorithm is further applied to three types of engineering optimization problems. The results show that the ICPO algorithm has better search accuracy and stability, which verifies its adaptability and favorable performance.

Key words: Chinese pangolin optimization algorithm, logistic chaotic map, Cauchy mutation, elite strategy, engineering optimization