全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
-  2017 

多启发式信息蚁群优化算法求解取样送检路径规划问题 Ant Colony Optimization with Multi-Heuristic Information for Sampling Inspection Path Planning Problem

Keywords: 取样送检路径规划问题,组合优化,蚁群优化,多启发式信息

Full-Text   Cite this paper   Add to My Lib

Abstract:

蚁群优化算法是一种求解组合优化问题的通用算法框架.取样送检路径规划问题是一种带约束的组合优化问题,本文给出了一种求解该问题的数学模型.为求解该问题提出了一种多启发式信息蚁群优化算法(MACO),在选择下一访问节点的概率计算公式中增加了一项启发式信息——起点到被选择点之间距离的倒数,并从理论上分析了该算法的收敛性.在9个算例上进行了仿真实验和分析,说明了新增启发式信息的有效性和适用性,验证了MACO算法可以有效求解该问题,并能获得质量更好的解

References

[1]  顾险峰,焦剑.海关取样工作的现状及路径创新[J].上海海关学院学报,2008,29(1):44-49.GU X F,JIAO J.The status of customs sampling and path innovation[J].Journal of Shanghai Customs College,2008,29(1):44-49.
[2]  COLORNI A,DORIGO M,MANIEZZO V.Distributed optimization by ant colonies[C]//Proceedings of the First European Conference on Artificial Life.Amsterdam(Dutch):Elsevier,1991:134-142.
[3]  DORIGO M,BIRATTARI M,STTZLE T.Ant colony optimization[J].Computational Intelligence Magazine,IEEE,2006,1(4):28-39.DOI:10.1109/MCI.2006.329691.
[4]  MOHAN B C,BASKARAN R.A survey:Ant colony optimization based recent research and implementation on several engineering domain[J].Expert Systems with Applications,2012,39(4):4618-4627.DOI:10.1016/j.eswa.2011.09.076.
[5]  BOUSSAD I,LEPAGNOT J,SIARRY P.A survey on optimization metaheuristics[J].Information Sciences,2013,237:82-117.DOI:10.1016/j.ins.2013.02.041.
[6]  CHANDRA B,KARLOFF H,TOVEY C.New results on the old k-opt algorithm for the traveling salesman problem[J].SIAM Journal on Computing,1999,28(6):1998-2029.DOI:10.1137/S0097539793251244.
[7]  刘彦鹏.蚁群优化算法的理论研究及其应用[D].杭州:浙江大学.2007.LIU Y P.Research on Ant Colony Optimization and Its Application[D].Hangzhou:Zhejiang University,2007.
[8]  苏兆品,蒋建国,梁昌勇,等.蚁群算法的几乎处处强收敛性分析[J].电子学报,2009,37(8):1646-1650.SU Z P,JIANG J G,LIANG C Y,et al.An almost everywhere strong convergence proof for a class of ant colony algorithms[J].Acta Electronica Sinica,2009,37(8):1646-1650.
[9]  GERHARD R.TSPLIB[EB/OL].[2016-12-19].http://elib.zib.de/pub/Packages/mp-testdata/tsp/tsplib/tsplib.html.
[10]  DERRAC J,GARCA S,MOLINA D,et al.A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms[J].Swarm and Evolutionary Computation,2011,1(1):3-18.DOI:10.1016/j.swevo.2011.02.002.
[11]  APPLEGATE D L.The Traveling Salesman Problem:A Computational Study[M].New Jersey:Princeton University Press,2011:1-5.
[12]  DORIGO M,GAMBARDELLA L M.Ant colony system:A cooperative learning approach to the traveling salesman problem[J].IEEE Transactions on Evolutionary Computation,1997,1(1):53-66.DOI:10.1109/4235.585892.
[13]  STTZLE T,LPEZ-IBNEZ M,PELLEGRINI P,et al.Parameter Adaptation in Ant Colony Optimization[M].Berlin Heidelberg:Springer,2012:191-215.
[14]  赵友虎,刘聪,张贺全.济钢45t转炉炼钢精益管理实践[J].山东冶金,2015,37(3):57-58.DOI:10.16727/j.cnki.issn1004-4620.2015.03.030.ZHAO Y H,LIU C,ZHANG H Q.Lean management practice of 45tconverter steelmaking in Jinan Iron and Steel[J].Shangdong Metallurgy,2015,37(3):57-58.DOI:10.16727/j.cnki.issn 1004-4620.2015.03.030.
[15]  STTZLE T,DORIGO M.A short convergence proof for a class of ant colony optimization algorithms[J].IEEE Transactions on Evolutionary Computation,2002,6(4):358-365.DOI:10.1109/TEVC.2002.802444.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133