基于MapReduce的蚁群算法[]-大数据文档资料.pptx

文章编号：1006-5911(2012)07-1503-07

基于MapReduce的蚁群算法

吴昊1,2,倪志伟1,2,王会颖1,2,3+

(1.合肥工业大学管理学院，安徽合肥230009;

2.合肥工业大学教育部过程优化与智能决策重点实验室，安徽合肥230009;

3.安徽财贸职业学院电子信息系，安徽合肥230601)

摘要：云计算环境下应用蚁群算法分布式并行对问题进行求解的研究较少，且蚁群算法存在有哪些信誉好的足球投注网站时间长和易收敛于非最优解的缺陷，当问题的规模较大时求解困难。为此应用云计算技术将蚁群算法并行化，提出基于MapReduce的蚁群算法。该算法将分治思想和模拟退火算法融入蚁群算法，改进其缺陷，并应用于求解较大规模的旅行商问题。仿真实验取得了较好的效果，且获得了测试实例gr666的新解。

关键词：云制造；云计算；蚁群算法；分治；模拟退火算法；旅行商问题

中图分类号：TP301文献标志码：A

MapReduce-basedantcolonyoptimization

WUHao1,2,NIZhi-wei1,2,WANGHui-ying1,2,3+

(1.SchoolofManagement,HefeiUniversityofTechnology,Hefei230009,China;

2.KeyLaboratoryofProcessOptimizationandIntelligentDecision-making,MinistryofEducation,

HefeiUniversityofTechnology,Hefei230009,China;

3.DepartmentofElectronicsandInformation,AnhuiFinanceTradeVocationalCollege,Hefei230601,China)Abstract:TheresearchesonsolvingproblemswithAntColonyOptimization(ACO)distributedparallelundercloudcomputingwereless,andACOhaddefectsinlongsearchtimeandconvergenceinnon-optimalsolution.Whenthescaleofproblemwaslarge,itwastoohardtosolve.Therefore,MapReduce-basedACOwasproposedbyusingcloudcomputingtoparallelACO.Inthisalgorithm,dividingconquerandsimulatedannealingalgorithmweremer-gedintoACOtoimprovethedefects.Itwasalsoappliedtosolvelarge-scaleofTravelingSalesmanProblem(TSP).Thesimulationexperimentgotawelleffectandthenewsolutionsoftestgr666wereobtained.

Keywords:cloudmanufacturing;cloudcomputing;antcolonyoptimization;divideconquer;simulatedannealingal-gorithm;travelingsalesmanproblem

收稿日期：2012-01-18;修订日期：2012-04-14。Received18Jan.2012;accepted14Apr.2012.

基金项目：国家863计划资助项目(2011AA040501);国家社会科学基金资助项目(10CGL024);安徽省教育厅自然科学重点资助项目(KJ2011A006)。Founda