混合人工蜂群策略的粒子群优化算法  

Hybrid Particle Swarm Optimization Algorithm with Artificial Bee Colony Strategy

作　　者：陈永刚 陈艳格[1] 何洁 丁锦琳 CHEN Yonggang;CHEN Yange;HE Jie;DING Jinlin(College of Information Technology,Xuchang University,Xuchang 461000,China)

机构地区：[1]许昌学院信息工程学院,河南许昌461000

出　　处：《许昌学院学报》2025年第2期100-105,共6页Journal of Xuchang University

基　　金：国家自然科学基金地区基金项目(62462018);河南省创新科技人才队伍建设项目(CXTD2017099)。

摘　　要：传统人工蜂群算法具有较好的探索能力,但局部开采能力较弱;粒子群优化算法具有较好的开采能力而探索能力较弱.受到合作策略的启发,采用两种算法取长补短相结合的方法,提出混合人工蜂群策略的粒子群优化算法(HABCPSO).该算法还采用了拟牛顿法来进一步提高其局部开采能力.实验结果表明,与其他一些优化算法相比,HABCPSO在数值优化方面具有较好的搜索精度.The basic artificial bee colony algorithm has good exploration ability but weak local exploitation ability.The particle swarm optimization(PSO)algorithm has good exploitation ability but weak exploration ability.Inspired by the cooperation strategy,this paper proposed a hybrid artificial bee colony particle swarm optimization algorithm(HABCPSO)by combining the advantages of the two algorithms.The algorithm also uses the Quasi-Newton method to further improve its local mining ability.Finally,several standard test functions are selected for experiments.The experimental results show that HABCPSO has better search accuracy in numerical optimization than some other optimization algorithms.

关 键 词：人工蜂群算法 粒子群优化 拟牛顿法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]