计算机工程与应用2016，Vol.52Issue(15)：183-189,7.DOI:10.3778/j.issn.1002-8331.1510-0099
基于SQP局部搜索的蝙蝠优化算法
Hybrid bat algorithm based on sequential quadratic programming local search
摘要
Abstract
In view of the basic bat algorithm has a few problems in low optimization accuracy, slow convergence speed and high possibility of being trapped in local optimum and so on, a hybrid bat algorithm based on Sequential Quadratic Programming(SQP)is proposed. The uniform initial population is constructed by the method of good point set, which enhances the ergodic ability of the initial population. In order to avoid premature convergence, Cauchy mutation operation is used to ensure diversity. In the late iterations, the best individual is used by SQP local search to improve the local bat depth search capabilities, which can ensure that the individual can find the global optimal solution close to the global optimal value, and to accelerate the evolution of population. The experimental results show that the improved bat algorithm has better performance, good optimization accuracy and fast convergence speed.关键词
蝙蝠算法/序贯二次规划(SQP)/柯西变异/佳点集/早熟收敛/寻优精度Key words
bat algorithm/Sequential Quadratic Programming(SQP)/Cauchy mutation/good point set/premature con-vergence/optimization accuracy分类
计算机与自动化引用本文复制引用
刘万军,杨笑,曲海成..基于SQP局部搜索的蝙蝠优化算法[J].计算机工程与应用,2016,52(15):183-189,7.基金项目
国家高技术研究发展计划（863）（No.2012AA12A405）；国家自然科学基金（No.61172144）；辽宁省教育厅科学技术研究一般项目（No.L2015216）。 （）
