多策略改进的闪电连接过程算法及其应用

Improved lightning attachment procedure optimization based on multiple strategies and its application

针对闪电连接过程算法鲁棒性欠佳、个体多样性有限等不足的问题,提出一种多策略改进的闪电连接过程算法.首先,采用透射学习策略和准对立学习策略有效提升初始化实验点个体质量,避免自有随机因子过度影响算法收敛性能;然后,设计实验点自适应动态跟随机制,在提升算法寻优性能的同时兼顾探索开发能力的协调;接着,采用镜像均值替代策略替换当前最优解和种群均值,防止算法因个体同化程度升高造成局部停滞;最后,选取基准测试函数进行智能算法对比寻优实验、改进算法对比寻优实验以及Wilcoxon符号秩检验实验,实验结果表明了所提出改进算法寻优性能的优越性以及改进策略的有效性.通过多策略改进的闪电连接过程算法优化长短时门循环控制单元参数进行变压器故障诊断工程应用实验,进一步验证所提出改进算法的工程实用性.

Aiming at the disadvantages of the lightning attachment procedure optimization such as poor robustness and limited individual diversity,an improved algorithm of the lightning attachment procedure optimization is proposed.The transmission learning strategy and the quasi opposition learning strategy are used to effectively improve the quality of individuals at initial test points and to avoid the excessive impact of its random factors on the convergence performance of the algorithm.The adaptive dynamic tracking mechanism for test points is designed to improve the optimization performance of the algorithm while taking into account the coordination of exploration and exploitation capabilities.The mirror image mean substitution strategy is used to replace the current optimal solution and the population mean in order to prevent the algorithm from local stagnation due to the increased degree of individual assimilation.The benchmark test functions are selected to apply to the comparative experiments of the intelligent algorithms optimization and the improved algorithms optimization and Wilcoxon signed ranks test experiments,whose results intuitively demonstrate the superiority of the improved algorithm in terms of its optimal performance and the effectiveness of the improved strategies.The engineering application experiments of transformer fault diagnosis using the improved lightning attachment procedure optimization to optimize the parameters of the long-short-term gated recurrent unit further verify its engineering practicality.