引用本文:赵新秋,段思雨,马学敏.基于阈值搜索的多目标人工蜂群算法[J].控制与决策,2020,35(8):1793-1802
【打印本页】   【HTML】   【下载PDF全文】   查看/发表评论  【EndNote】   【RefMan】   【BibTex】
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览次   下载 本文二维码信息
码上扫一扫!
分享到: 微信 更多
基于阈值搜索的多目标人工蜂群算法
赵新秋1,2, 段思雨1, 马学敏1
(1. 燕山大学工业计算机控制工程河北省重点实验室,河北秦皇岛066004;2. 燕山大学国家冷轧板带装备及工艺工程技术研究中心,河北秦皇岛066004)
摘要:
为了平衡人工蜂群算法局部开发能力和全局搜索能力,提高算法收敛速度,提出一种基于阈值搜索的人工蜂群算法.首先,提出一种混沌镜像初始化方法,保证初始种群的多样性和优异性;然后,利用个体阈值动态调整搜索半径,提高搜索精度和收敛速度,考虑外部档案解的开发次数,合理选择精英解来引导进化.在11种测试函数上与其他几种算法对比的仿真结果表明,所提出算法具有较好的分布性和收敛性.
关键词:  多目标优化  人工蜂群算法  镜像初始化  阈值搜索  Pareto支配关系
DOI:10.13195/j.kzyjc.2018.1512
分类号:TP273
基金项目:河北省自然科学基金项目(F2016203249).
A multi-objective artificial bee colony based on limit search strategy
ZHAO Xin-qiu1,2,DUAN Si-yu1,MA Xue-min1
(1.Key Lab of Industrial Computer Control Engineering of Hebei Province,Yanshan University,Qinhuangdao066004,China;2. National Engineering Research Center for Equipment and Technology of Cold Strip Rolling,Yanshan University,Qinhuangdao066004,China)
Abstract:
In order to balance the local exploitation ability and the global exploration ability of artificial bee colony,and accelerate the rate of convergence, a multi-objective artificial bee colony based on the limit search(LSABC) strategy is proposed. Firstly, a chaotic mirroring strategy is proposed in the initialization phase to ensure diversity and superiority. Then, the radius of search is dynamically adjusted while the employed bees and the onlooker bees searching according to the individual limit, which improves the search accuracy and convergence speed. The development times of the external archive are combined to choose elite solution so as to guide evolution reasonably. Finally, experiments are conducted on a set of 11 benchmark functions, and the results show that the proposed algorithm performs well in convergence and distribution.
Key words:  multi-objective optimization  artificial bee colony  chaotic mirroring initialization  limit search strategy  Pareto dominance

用微信扫一扫

用微信扫一扫