基于邻域搜索的改进反向学习平衡优化器算法

An improved opposition-based learning equilibrium optimizer algorithm based on neighborhood searching

针对标准平衡优化器EO算法收敛精度低、易陷入局部最优解等问题,提出一种结合邻域拓扑搜索改进的反向平衡优化器算法IOLEONS。首先,利用双曲正切自适应算子修改平衡池中平均浓度值,提高算法收敛精度;然后,计算粒子之间的欧氏距离,引入邻域搜索机制,进一步增强算法的局部开发能力,更好地平衡算法开发和探索阶段;最后,利用添加Chebyshev映射的动态对称反向学习策略增强种群的扰动能力,提高种群的多样性,帮助种群跳出局部最优解。对改进算法进行收敛性分析并选取8个基准函数进行仿真实验,Wilcoxon符号秩检验和Friedman秩检验结果显示,改进算法具有较好的优化性能。

To address the problems of low convergence accuracy and easy local optima trapping in the standard Equilibrium Optimizer(EO)algorithm,this paper proposes an Improved Opposition-based learning Equilibrium Optimizer Algorithm based on Neighborhood Searching(IOLEONS)that combines neighborhood topology search.Firstly,the hyperbolic tangent adaptive operator is used to modify the average concentration value in the balance pool to improve the convergence accuracy of the algorithm.Then,the Euclidean distance between particles is calculated,and a neighborhood search mechanism is introduced to further enhance the algorithm's local development ability,better balancing the algorithm's development and exploration stages.Finally,the dynamic symmetric opposite learning strategy with Chebyshev mapping is used to enhance the population's disturbance ability,improve the diversity of the population,and help the population escape from local optima.The convergence of the improved algorithm is analyzed,and eight benchmark test functions are selected in the simulation experiments.The results of Wilcoxon signed-rank test and Friedman rank test show that the improved algorithm has better optimization performance.