[1]康志龙,张东婧,郭艳菊,等.基于过滤引导及随机性策略的差分搜索算法[J].燕山大学学报,2017,41(3):253-264.[doi:10.3969/j.issn.1007-791X.2017.03.009]
 KANG Zhilong,ZHANG Dongjing,GUO Yanju,et al.Differential search algorithm based on strategies of filtration guidance and random[J].Journal of YanShan University,2017,41(3):253-264.[doi:10.3969/j.issn.1007-791X.2017.03.009]
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基于过滤引导及随机性策略的差分搜索算法
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《燕山大学学报》[ISSN:1007-791X/CN:13-1219/N]

卷:
41
期数:
2017年第3期
页码:
253-264
栏目:
信息与计算机技术
出版日期:
2017-05-31

文章信息/Info

Title:
Differential search algorithm based on strategies of filtration guidance and random
文章编号:
1007-791X(2017)03-0253-12
作者:
康志龙1*张东婧1郭艳菊1张雪萍1陈雷23
1.河北工业大学 电子信息工程学院,天津,300401;2.天津商业大学 信息工程学院,天津 300134;3.天津大学 精密仪器与光电子工程学院,天津 300072
Author(s):
KANG Zhilong1ZHANG Dongjing1GUO Yanju1ZHANG Xueping1CHEN Lei23
1.School of Electronic Information Engineering,Hebei University of Technology,Tianjin 300401,China;2.School of Information Engineering,Tianjin University of Commerce,Tianjin 300134,China;3.School of Precision Instrument and Opto-Electronics Engineering,Tianjin University,Tianjin 300072,China
关键词:
差分搜索算法过滤搜索策略函数优化随机算子群体方式
Keywords:
differential search algorithmfiltration search strategyfunction optimizationrandom operatorswarm intelligence optimization
分类号:
TP301.6
DOI:
10.3969/j.issn.1007-791X.2017.03.009
文献标志码:
A
摘要:
差分搜索算法是一种新型高效的仿生智能优化算法。但该算法仍存在收敛速度较慢,搜索精度不够高等缺点。为此,本文提出一种基于过滤引导及随机性策略的差分搜索算法。一方面,将过滤择优策略引入到搜索方程中进行首次搜索,使得算法收敛速度及搜索精度得到提高;另一方面,提出随机算子引导搜索方程,使得算法可以快速达到全局收敛。对标准测试函数进行了优化求解实验,结果表明,所提出的改进策略有效地提高了算法的优化性能,较之其它算法更适合求解复杂度高且难度较大的多模态最优化问题。
Abstract:
Differential search algorithm is a new and efficient bionic intelligent algorithm.However,the algorithm convergences slowly,and its search accuracy is not high enough.Therefore,the differential search algorithm based on the strategies of filtration guidance and random is proposed.On the one hand,the strategy of filtration guidance is introduced to search equation for the first search,so that the convergence speed is improved to some extent;on the other hand,random operator guides the search equation,making the algorithm achieve global convergence quickly.Through solving the standard test functions,simulation results show that the proposed algorithm has effectively improved the performance of the algorithm.Compared with other algorithms,the proposed algorithm is more suitable to solve the complex and difficult larger multi-modal optimization problem.

备注/Memo

备注/Memo:
收稿日期:2016-10-18        责任编辑:孙峰
基金项目:国家自然科学基金资助项目(61401307);中国博士后科学基金资助项目(2014M561184);天津市应用基础与前沿技术研究计划项目(15JCYBJC17100);天津市科技特派员项目(16JCTPJC48400) 
作者简介:*康志龙(1971-),男,河北石家庄人,副研究员,主要研究方向仿生智能计算、半导体光电子学,Email:zhlk@hebut.edu.cn
更新日期/Last Update: 2017-07-07