基于非线性自适应的改进浣熊优化算法及应用

doi:10.6040/j.issn.1672-3961.0.2025.003

摘要/Abstract

摘要： 针对浣熊优化算法(coati optimization algorithm, COA)全局搜索能力不足、易陷入局部最优和收敛速度慢的问题,提出一种基于非线性自适应的改进浣熊优化算法(improved coati optimization algorithm based on nonlinear adaptation, NACOA)。采用Logistic-Tent映射初始化浣熊种群,提升算法初始搜索空间覆盖度,生成更加分散且高质量的初始解;引入莱维飞行策略,利用其长跳跃特性,增强算法的全局搜索能力,有效避免算法陷入局部最优;利用非线性递减惯性权重提高种群的适应性与搜索效率,平衡全局搜索和局部搜索能力,并通过黄金正弦策略提高种群收敛精度。在基准测试函数上进行对比仿真试验,结果表明NACOA具有更好的收敛速度和寻优精度。将NACOA应用到工程问题设计中,证明了该算法的有效性和实用性。

关键词: 浣熊优化算法, Logistic-Tent映射, 非线性递减惯性权重, 黄金正弦策略, 工程应用

Abstract: Aiming to address the problems of insufficient global search capability, easily falling into local optima, and slow convergence speed of the coati optimization algorithm(COA), an improved coati optimization algorithm based on nonlinear adaptation(NACOA)was proposed. A Logistic-Tent mapping was used to initialize the coati population, which improved the initial search space coverage of the algorithm and generated more dispersed and high-quality initial solutions. The Levy flight strategy was introduced, which made use of its long-jump characteristic to enhance the global search capability of the algorithm and effectively avoided the algorithm from falling into local optima. The nonlinearly diminishing inertia weight was used to increase the adaptability of the population and the search efficiency, balance the global and local search capabilities, and improve the population convergence accuracy through the golden sine strategy. Comparative simulation experiments were conducted on benchmark test functions, and the results showed that NACOA had better convergence speed and optimization accuracy. The NACOA was applied to the design of engineering problems, which proved the effectiveness and practicality of this algorithm.

Key words: coati optimization algorithm, Logistic-Tent mapping, nonlinearly diminishing inertia weight, golden sine strategy, engineering application

中图分类号:

TP301.6

柳宗元,李小光,侯宇翔,丁昊. 基于非线性自适应的改进浣熊优化算法及应用[J]. 山东大学学报 (工学版), 2026, 56(1): 49-62.

LIU Zongyuan, LI Xiaoguang, HOU Yuxiang, DING Hao. Improved coati optimization algorithm based on nonlinear adaptation and applications[J]. Journal of Shandong University(Engineering Science), 2026, 56(1): 49-62.

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