基于威布尔飞行和警戒机制的沙猫群优化算法及应用

doi:10.3778/j.issn.1002-8331.2402-0176

摘要/Abstract

摘要： 针对沙猫群优化算法收敛速度慢、寻优精度低等问题，提出了一种多策略改进的沙猫群优化算法。利用拉丁超立方抽样进行初始化，提升种群多样性；在搜索猎物阶段提出威布尔飞行，增强算法搜索能力；提出一种警戒机制，进一步提升算法的寻优能力与收敛速度。使用具有挑战性的CEC2017函数进行函数测试，基于基准函数定性分析、寻优精度分析、改进策略有效性分析、收敛曲线分析以及Wilcoxon秩和检验、Friedman检验进行综合评价。实验结果表明，相比于其他3种沙猫群算法以及6种元启发式算法，所提出的算法在复杂函数上的寻优精度和收敛方面具有显著优越性。将该算法应用至变压器故障诊断实例中，进一步验证了ESCSO算法的有效性。

关键词: 元启发式算法, 沙猫群算法, 拉丁超立方抽样, 威布尔飞行, 警戒机制, 变压器故障诊断

Abstract: Aiming at the problems of slow convergence and low optimization accuracy of sand cat swarm optimization algorithm, a multi-strategy improved sand cat swarm optimization algorithm is proposed. Firstly, Latin hypercube sampling is used for initialization to improve population diversity. Secondly, Weibull flight is proposed in the search stage to enhance the search ability of the algorithm. Finally, an alert mechanism is proposed to further improve the optimization ability and convergence speed of the algorithm. The challenging CEC2017 function is used for functional testing, and comprehensive evaluation is performed based on qualitative analysis of benchmark function, optimization precision analysis, effectiveness analysis of improved strategy, convergence curve analysis, Wilcoxon rank sum test and Friedman test. The experimental results show that compared with other three sand cat swarm algorithms and six meta-heuristic algorithms, the proposed algorithm has significant advantages in the optimization accuracy and convergence of complex functions. The algorithm is applied to transformer fault diagnosis cases, and the effectiveness of ESCSO algorithm is further verified.

Key words: meta-heuristic algorithm, sand cat swarm algorithm, Latin hypercube sampling, Weibull flight, warning mechanism, transformer fault diagnosis

杨宇鸽, 郝杨杨, 王逸文. 基于威布尔飞行和警戒机制的沙猫群优化算法及应用[J]. 计算机工程与应用, 2025, 61(2): 145-157.

YANG Yuge, HAO Yangyang, WANG Yiwen. Sand Cat Swarm Optimization Algorithm Based on Weibull Flight and Warning Mechanism and Its Application[J]. Computer Engineering and Applications, 2025, 61(2): 145-157.

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