混沌反馈共享和群体协同效应的蝴蝶优化算法

Butterfly Optimization Algorithm for Chaotic Feedback Sharing and Group Synergy

针对蝴蝶优化算法(BOA)寻优精度低和易陷入局部最优等缺点,提出了混沌反馈共享和群体协同效应的蝴蝶优化算法(CFSBOA)。首先,利用Hénon混沌初始化种群,能够使种群尽可能地覆盖搜索盲区,增加种群多样性,提高算法寻优性能;其次,利用反馈控制电路中正负反馈作用机制的思想,构建蝴蝶之间的反馈共享交流网络,使得蝴蝶个体能够接收来自多个方向的信息,帮助种群定位最优解的位置并执行精细搜索,增强算法逃离局部最优的能力和加快算法收敛的速度;最后,利用群体协同效应机制,提高和平衡全局与局部搜索的能力,增强算法的全局和局部的寻优能力。使用不同维度的基准测试函数、统计检验、Wilcoxon检验及多类型的CEC2014部分函数验证改进蝴蝶优化算法的性能,并与新改进的蝴蝶算法及其他群智能算法进行对比,实验结果表明该算法具有明显优势。

A butterfly optimization algorithm(BOA)based on chaotic feedback sharing and group synergy(CFSBOA)is proposed to solve the shortcomings of low precision and easy to fall into local optimum.Firstly,using Hénon chaos to initialize the population can make the population cover the search blind area as much as possible,increase the diversity of the population,and improve the ability of optimizing the algorithm.Secondly,using the ideas of positive and negative feedback mechanism in feedback control circuit,it builds butterfly feedback shared communication network,allowing individuals to receive information from multiple directions to help populations of positioning the location of the optimal solution and perform careful search,enhance the ability to escape from local optimum and accelerate the algorithm convergence speed.Finally,the collective synergistic effect mechanism is used to improve and balance the global and local search ability and enhance the global and local optimization ability of the algorithm.The performance of the improved butterfly optimization algorithm is verified by using different dimension benchmark test functions,statistical test,Wilcoxon test and multiple types of CEC2014 partial functions.Compared with the new improved butterfly algorithm and other swarm intelligence algorithms,the experimental results show that the proposed algorithm has obvious advantages.