多策略改进的中华穿山甲优化算法

doi:10.16088/j.issn.1001-6600.2026032101

摘要/Abstract

摘要： 针对中华穿山甲优化算法(Chinese pangolin optimizer, CPO)初始种群分布不均、高维易陷局部最优、后期收敛震荡的缺陷,本文提出一种多策略改进的中华穿山甲优化算法(improved Chinese pangolin optimizer, ICPO)。ICPO算法融合Logistic混沌映射优化初始种群分布,提升解空间覆盖度;引入动态触发柯西变异机制,增强算法跳出局部最优能力;设计全流程精英策略,通过精英池构建、引导更新与保护机制,强化优质解的引导作用并提升收敛稳定性。仿真实验选用12个测试函数,将ICPO算法与CPO及其他7种群智能优化算法进行对比,通过Friedman检验与Wilcoxon秩和检验证实其性能优势具有统计学显著性。将ICPO算法应用于3类工程优化问题,结果表明,其具备更好的搜索精度与稳定性,验证了算法的适应性和良好性能。

关键词: 中华穿山甲优化算法, Logistic混沌映射, 柯西变异, 精英策略, 工程优化

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

中图分类号: TP301.6

李泽琪, 袁书娟, 孔闪闪, 杨爱民. 多策略改进的中华穿山甲优化算法[J]. 广西师范大学学报（自然科学版）, 2026, 44(6): 100-121.

Li Zeqi, Yuan Shujuan, Kong Shanshan, Yang Aimin. Multi-strategy improved Chinese pangolin optimization algorithm[J]. Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(6): 100-121.

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