摘要
布谷鸟搜索(Cuckoo Search,CS)算法高效简单,但在求解复杂问题时收敛效率较低.为提高CS算法的寻优精度和收敛速度,提出了一种基于精英反向学习的混沌扰动布谷鸟搜索算法(CH-EOBCCS).该算法引入精英个体,通过精英个体反向学习生成精英反向解,从当前解和精英反向解中挑选优异个体作为下一代种群,同时,在迭代中对鸟巢位置采用混沌扰动策略,扩大种群多样性,有效的提高了算法全局搜索能力和搜索精度.通过8个标准测试函数对比实验,结果表明加入混沌扰动的精英反向学习布谷鸟搜索算法具有较强的搜索能力和较高的寻优精度.
CS（Cuckoo Search）algorithm is efficient and simple,but also exists the problem of low efficiency of convergence for complex problems.In order to improve the convergence precision and global exploration ability,a Cuckoo search algorithm using elite opposition-based learning and chaotic disturbance is proposed.The elite individual is introduced to generate their opposite solutions by Elite opposition-based Learning.This mechanism is helpful to enhance the global explorative ability of CS.At the same time,chaotic perturbation operator is added in the parasitic nest position in the iteration,thereby the population diversity is expanded and the algorithm accuracy is improved.The experiments are conducted on 8classic Benchmark functions,and the results show that the new algorithm has much better search performance than CS,which remarkably improves the ability of CS to jump out of the local optima.
出处
《武汉大学学报（理学版）》
CAS
CSCD
北大核心
2015年第4期356-362,共7页
Journal of Wuhan University:Natural Science Edition
基金
国家自然科学基金项目(61070009
61373038)资助项目
关键词
布谷鸟搜索算法
精英反向学习
混沌扰动
Cuckoo algorithm
elite opposition-based learning
chaotic disturbance
