通信工程网络技术外文翻译文献翻译外文文献

1、外文翻译译文题目：在WDM代理网络中基于蚁群的动态路由和波长分配原稿题目：DynamicRoutingandWavelengthAssignmentinWDMNetworkswithAnt-BasedAgents原稿出处:EmbeddedandUbiquitousCompterscienceVolume829-838在WDM代理网络中基于蚁群的动态路由和波长分派【摘要】在这篇论文中，咱们提出一种在波长持续性约束波分复用（WDM）光网络中基于蚁群算法的动态路由与波长分派。通过采纳一个新的路由表结构和维持大量的蚂蚁在网络中合作探讨网络状态和不断更新路由表的方式，咱们新的蚁群算法能够有效地支持蚁群觅

2、食任务的路由选择波分复用（WDM）网络中波长分派，并许诺一个连接设置迅速抵达小的设置时刻。大量基于ns-2网络仿真结果模拟说明，该算法能够专门好得适应流量转变和达到一个比起固定路由算法较低的阻塞概率。【关键词】路由，波长分派，算法，WDM（波分复用），蚁群算法1 .介绍所有采纳波分复用（WDM）光网络都有一个庞大的带宽容量，他们显示成为下一代互联网骨干。在所有光网络中，数据路由在光学通道被叫做光路。路由和波长分派（RWA）问题是如何为一个连接请求确信路由和波长。没有了波长转换功能，一个光路必需在所有链接中利用相同的波长，这被称为波长持续性限制。路由和波长分派（RWA）问题通常被归类为静态和动态

3、两种。在静态路由和波长分派问题中，连接请问是预先给出的，问题就变成如何为所有请求成立光路，使得总数量的波长被最小化。静态路由和波长分派问题已经被证明是一个NP完全问题。在动态路由和波长分派问题中，流量是动态的和连接请求抵达的随机性使得它变得更为困难。启发式算法通常被用来解决那个问题。一样来讲，一个动态的路由和波长分派算法的目的是使在整个网络中总阻塞概率最小化。在咱们的工作中，咱们关注波长持续性约束的动态RWA问题。在高作中，动态RWA问题通常被分为两种子问题，别离能够解决：路由和波长分派。路由方案能够分为固定路由，固定备用路由和自适应路由。在固定的路由方案，有一个专门为源和目的的线路。每当一个

4、请求出此刻这一对源和目的中，这条线路就会试图对波长进行分派。固定路由简单，可是通常致使高阻塞概率。固定备用路由方式具有更好的性能在多途径节点对方面。在自适应路由方案中，当一个连接请求到来时，线路的计算是基于当前网络状况，从而取得最好的性能。但是，自适应路由需要很高的复杂计算。一个更详细的路由调查和波长分派能够在中找到。自适应RWA方案在论文中老是需要来自于操纵协议的特殊支持以取得全世界网络状态。另外，启发式算法在一个请求抵达以后执行路由和波长搜索任务必需衡量复杂度和性能。这也造成高设置延迟和操纵开销。一个可能的方式来克服这些问题是利用基于蚁群的移动代理3。基于蚁群的代理路由方式继承了移动代理行

5、为和蚁群系统的优势。最近的结果说明，这种方式能够在电路操纵的操纵和分组互换网络中产生高效的性能。在本文中，咱们要紧研究一个新的基于蚁群代理算法为波分复用网络的动态RWA问题在波长持续性约束之下。咱们的研究旨在通过利用适当数量的蚂蚁来减少阻塞概率和途径设置时刻，蚁群在连接请求带来之前持续执行途径搜索任务，如此的路由选择和波长分派请求的执行是简单地查找路由表。为了实现这一目标，为咱们新的算法开发一个新的路由表结构，一个蚁群操纵方案和一个信息素更新机制。本文余下部份组织如下：在第二节，咱们讨论相关工作。第三节，在波长持续性约束之下的波分复用网络的中咱们为动态RWA问题提出新的方式。第四节，描述咱们初

6、步仿真和分析结果。最后，咱们的结论和以后的工作在第五节进行了讨论。2 .相关工作最近的研究结果说明，通信网络中的路由能够通过蚁群优化（ACO）3方式有效地解决。路由解决方案成立在基于蚁群在代理网络状态中的觅食行为。这些集体代理通过环境中信息素拖拽（stigmergy）间接沟通。通过下面的的另一个信息素轨迹，一个代理能够找到一个“好”的线路，这条线路最短，从源到目的地路由数据最不拥堵。两种大体算法是由SchoonderwoerdetaL4提出的对网络的基于蚁群操纵（ABC）和由DiCaroetaL5提出的基于蚁群的分组互换网络。有一些后续的提高路由性能的改良方案，包括利用动态编程9的智能代理，增

7、强蚁群对环境适应能力10的强化学习和适应蚁群搜索进程操纵参数11的遗传算法。而以上的研究要紧集中在电子通信网络中的路由问题，咱们在本文中的爱好是波长持续性约束下的波分复用光网络的动态RWA问题。ValeraetaL12提出了一种蚁群算法来解决静态RWA问题。目标在于使一个给定网络拓扑和流量矩阵的波长要求数量尽可能减少。波长分派仅仅利用一个贪婪的方式，它为每一个链接指定最低可用波长。一个蚂蚁的路由选择是基于每一个连接的吸引力。每只蚂蚁都有自己的能够被其他蚂蚁拒绝的信息素。每只蚂蚁都留有一个用于线路回溯和循环回避的之前访问节点的“禁忌”列表。信息素的更新能够利用不同的方式。该方式最好的结果是吸引蚂

8、蚁的途径数量随着穿越的蚂蚁数量愈来愈多而取得全世界更新。那个结果能够相较于Nagasu启发式13,可是他需要更长的计算时亥h然后，那个方式不能直接应用于动态RWA问题。Garlicketal.14提出了一种基于蚁群的算法来解决动态RWA问题。当一个新的连接请求到来时，大量的蚂蚁从源动身到目的地。蚂蚁评估一条途径是基于其长度和这条途径的平都可用波长。当一只蚂蚁抵达目的地，全世界信息素更新被执行。信息素更新的需求依据：一旦一个连接被成立，网络信息素矩阵重置。为一个连接请求的最后最好途径的产生是当所有的蚂蚁完成他们的探讨任务。作者说明，该算法在所有可用波长中探求最短途径15比一个详尽的探讨具有更高的

9、性能。作为一个新组蚂蚁必需为新的连接请求启动，设置延时会由于大型网络等待蚂蚁而变得超级高。事实上,这种方式可不能显示来自于不同请求的蚂蚁的集体行为，这是基于蚁群系统的一个重要方面。3 .基于蚁群的RWA算法一个光学波分复用（WDM）网络能够表示为由N个节点和E链接的图。咱们假设每一个链接是双向容量的W波段和节点没有波长转换能力（波长持续性限制）。为了支持蚂蚁路由选择，每一个网络节点有一个路由表和N-1条款。在一个i和ki相邻的节点，路由表有一个ki序列。每一个条款对应到目的节点，每一列对应一个相邻节点。当一只蚂蚁向目的节点d运动时，那个值"）用作邻居节点n的选择概率。为了支持波长分派

10、，咱们引入了选择概率的每一个波长到路由表。关于每一个相邻的节点，让P，概率是一只蚂蚁选择波长j,当它移动到目的地d。图1所示的是当W=1的一个新的路由表的新的例子。当一个连接请求发生在源节点1和目的节点6,节点3将被选择作为下一跳，因为匕、。在这种情形下，因为P1VP2,波长2是优于波长1。Fig.1.Anetworkanditsroutingtablefromnode1在一个节点上，蚂蚁是由一个给定的概率随机选择P到目的地，每T个时刻单位。那个地址P和T是设计参数。一只蚂蚁被以为是一个移动代理:它负责在其旅行线路上搜集信息，执行路由表更新访问节点，并继续前进见图2。米AntlaunchedU

11、pdatepheromoneAntkilledFig.2.Ant'smovingandupdatingtasks一只蚂蚁从源移动到目的地，在一Fig.2.Ant飞movingandupdatingtasks个选定的波长上一个节点到一个节点运动。它的下一站是随机决定的：一个相邻点的选择概率是基于路山表的。当一只蚂蚁抵达目的地节点或当它不能选择一个空闲的波长选择的途径为其下一步行动时将被剔除。为了幸免“冻结”状态，所有蚂蚁专注于一个线路（停滞），随机方案介绍：每一个蚂蚁选择下一跳的随机与利用概率。当一个连接请求抵达时，途径将决定基于最高的选择概率相邻节点的条款。波长分派是基于路由表的波长选

12、择概率,或其他一些传统能够利用的启发式方式。当一只蚂蚁访问一个节点，它以其旅行进程中搜集的信息来更新路由表的元素。信息素更新的原理描述如下：假设一只蚂蚁从源移动到目标d后的S途径（S,d）.当蚂蚁抵达节点i,它将对应节点S更新条款。当其它相邻节点概率减少时，相邻i-1节点概率也减少。关于最近一次访问的相邻i-1节点，相应的空闲波长概率增加了，但是波长对应的概率忙碌程度降低了。更为正式的是，假设在时刻3蚂蚁访问节点i,因此在下次t+1路由条款是由下面的公式决定的（记住，所有的相邻总概率总和是1）：i"ts十2(1)(2)/1sU+1）=；1+d>rr(t)，九W211 +2Dyn

13、amicRoutingandWavelengthAssignmentinWDMNetworkswithAnt-BasedAgentsSon-HongNgo,XiaohongJiang,SusumuHoriguchi,andMinyiGuoGraduateSchoolofInformationScience,JapanAdvancedlnstitiiteofScienceTechnology,Japan2 SchoolofComputerScienceandEngineeringTheUniversityofAizu,AbstractInthispaper,weproposeanant-base

14、dalgorithmfordynamicroutingandwavelengthassignment(RWA)inWDMopticalnetworksunderthewavelengthcontinuityconstraint.Byadoptinganewroutingtablestructureandkeepinganumberofantsinthenetworktocooperativelyexplorethenetworkstatesandcontinuouslyupdatetheroutingtables,ournewantalgorithmcanefficientlysupportt

15、heants'foragingtasksofrouteselectionandwavelengthassignmentinWDMnetworks,andallowaconnectiontobesetuppromptlyonarrivalwithasmallsetuptime.Extensivesimulationresultsbasedonthens-2networksimulatorindicatethattheproposedalgorithmcanadaptwelltotrafficvariationsandachievesalowerblockingprobabilitytha

16、nthefixedroutingalgorithm.1 IntroductionAllopticalnetworksthatadoptwavelength-division-multiplexing(WDM)technologyhaveahugebandwidthcapacity,andtheyshowpromiseasthebackboneofthenextgenerationInternet.Inallopticalnetworks,dataareroutedinopticalchannelscalledlightpaths.TheRoutingandWavelengthAssignmen

17、t(RWA)problemishowtodeterminebotharouteandwavelengthsforaconnectionrequest.Withoutwavelengthconversioncapability,alightpathmustusethesamewavelengthonallthelinksalongitsroute,whichisreferredtoasthewavelengthcontinuityconstraint.TheRWAproblemisusuallyclassifiedasthestaticRWAproblemandthedynamicRWAprob

18、lem.InthestaticRWAproblem,theconnectionrequestsaregiveninadvance,andtheproblembecomeshowtoestablishlightpathsforalltheserequestssothatthetotalnumberofwavelengthsisminimized.StaticRWAhasbeenprovedtobeanNP-conipleteproblemI.InthedynamicRWAproblem,thetrafficisdynamicwithconnectionrequestsarrivingrandom

19、ly,makingitmoredifficult.Heuristicalgorithmsareusuallyemployedtoresolvethisproblem.Generally,adynamicRWAalgorithmaimstominimizethetotalblockingprobabilityintheentirenetwork0Inourwork,wefocusonthedynamicRWAproblemwithwavelengthcontinuityconstraint.Intheliterature,thedynamicRWAproblemisusuallydividedi

20、ntotwosub-problemsthatcanbesolvedseparately:routingandwavelengthassignment.Routingschemescanbeclassifiedintofixedrouting,fixed-alternateroutingandadaptiverouting.Inthefixedroutingscheme,onerouteisdedicatedforasourcedestinationpair.Wheneverarequestoccursbetweenthissource-destinationpair,thisrouteisat

21、temptedforwavelengthassignment.Thefixedroutingmethodissimplebutusuallycausesahighblockingprobability.Thefixed-alternateroutingmethodhasbetterperformancewithmultiplepathsdedicatedforanodepair.Intheadaptiveroutingscheme,therouteiscomputedatthetimetheconnectionrequestarrives,basedonthecurrentnetworksta

22、te,thusityieldsthebestperformance.However,adaptiveroutingrequireshighcomputationalcomplexity.Amoredetailedsurveyofroutingandwavelengthassignmentcanbefoundin2oTheadaptiveRWAsolutionsintheliteraturealwaysneedspecialsupportfromcontrolprotocoltoobtaintheglobalstateofthenetwork.Moreover,heuristicalgorith

23、msthatperformrouteandwavelengthsearchingtasksafterarequestarrivesmusttakeintoaccountthetradeoffbetweencomplexityandperformance.Thisalsocontributestohighsetupdelayandcontroloverhead.Apossibleapproachtoovercometheseproblemsistheuseofant-basedmobileagents3.Theant-basedagentroutingapproachinheritsadvant

24、agesfrombothmobileagentsbehaviorsandanantcolonysystem.Recentresultsshowthatthisapproachcouldyieldefficientperformanceinthecontrolofbothcircuitswitching4andpacketswitchingnetworks5oInthispaper,weinvestigateanewant-basedagentalgorithmforthedynamicRWAprobleminWDMnetworksundertheconstraintofwavelengthco

25、ntinuity.Ourstudyaimstoreducebothblockingprobabilityandpathsetuptimebyusingasuitableamountofants,whichcontinuouslyperformpathsearchingtasksbeforetheconnectionrequest'sarrivalsothattherouteselectionandwavelengthassignmentofarequestareperformedbysimplylookinguptheroutingtables.Toachievethatgoal,we

26、developanewroutingtablestructure,aschemeforantpopulationcontrolandamechanismforpheromoneupdating,forournewalgorithmoTherestofthispaperisorganizedasfollows:Insection2,wediscussrelatedworks.Section3presentsournewapproachtothedynamicRWAprobleminWDMnetworksunderwavelengthcontinuityconstraint.Section4des

27、cribesourpreliminarysimulationandanalysisresults.Finally,ourconclusionsandfutureworksarediscussedinSection52 RelatedWorkRecentresearchresultsshowthattheroutingincommunicationnetworkscanberesolvedefficientlybymeansofAntColonyOptimization(ACO)3.Theroutingsolutioncanbebuiltusingant-basedagentsbehaviori

28、ntheirforagingofnetworkstates.Thesecollectiveagentsindirectlycommunicatethroughpheromonetrailing(stigmergy)intheenvironment.Byfollowingthepheromonetrailofanother,anagentcanfinda“good”routeintermsofshortest,leastcongestedpathfromthesourcetothedestinationtoroutethenetworkdata.Twobasicalgorithmsareant-

29、basedcontrol(ABC)fortelephonenetworks,whichwasproposedbySchoonderwoerdetal.4andAntNetforpacketswitchingnetworks,whichwasproposedbyDiCaroetal.5.Somesubsequentenhancementschemestoimprovetheant-basedroutingperformanceincludesmartagentswhichusedynamicprogramming9,reinforcementlearningwhichenhancestheant

30、'sadaptabilitytoitsenvironment10,andageneticalgorithmwhichadaptstheantcontrolparameterstothesearchprocess11.WhiletheDynamicRoutingandWavelengthAssignmentinWDMNetworks831aboveresearchfocusesontheroutingprobleminelectroniccommunicationnetworks,ourinterestinthispaperisthedynamicRWAprobleminWDMoptic

31、alnetworkswiththeconstraintofwavelengthcontinuity.Valeraetal.12proposedanACOapproachforsolvingthestaticRWAproblem.Thegoalistominimizethenumberofwavelengthrequirementgivenanetworktopologyandatrafficmatrix.Thewavelengthassignmentsimplyusesagreedymethodthatassignsthelowestavailablewavelengthtoeachlink.

32、Ailant'srouteisselectedbasedontheweightofattractionofeachlink.Eachanthasitsownpheromonethatcanberepulsedbyothers.Eachantkeepsa“tabu”listofpreviouslyvisitednodeforroutebacktrackingandloopavoidance.Thepheromoneupdatingcanusedifferentmethods;thebestresultofthisapproachisobtainedthroughglobalupdatew

33、hentheweightofattractionofantforapathincreaseswiththenumberoftraversedants.TheresultcanbecomparedtotheconventionalNagatsuheuristic13,butitrequiresamuchlongercomputationaltime.However,thisapproachcannotbeapplieddirectlytothedynamicRWAproblem.Garlicketal.14proposedanACO-basedalgorithmtosolvethedynamic

34、RWAproblem.Whenanewconnectionrequestarrives,anumberofantsarelaunchedfromthesourcetothedestination.Antsevaluateapathbasedonitslengthandthemeanavailablewavelengthsalongthepath.Globalpheromoneupdatingisperformedwhenanantreachesitsdestination.Thepheromoneupdatingisonaper-demandbasis:thenetworkpheromonem

35、atrixisresetonceaconnectionisestablished.Thefinalbestpathforaconnectionrequestismadewhenallantscompletetheirexploitationtasks.Theauthorsshowedthatthisalgorithmhasbetterperformancethananexhaustivesearchoverallavailablewavelengthsfortheshortestpath15.Asanewsetofantsmustbelaunchedforeachnewconnectionre

36、quest,thesetupdelaywillbeveryhighduetothewaitingforantsinlargenetworks.Infact,thisapproachdoesnotshowthecollectivebehaviorofantsthatcomefromdifferentrequests,whichisanimportantaspectofant-basedsystemso3 Ant-BasedRWAAlgorithmAnopticalWDMnetworkisrepresentedbyagraphwithNnodesandElinks.Weassumethateach

37、linkisbi-directionalwithacapacityofWwavelengthsandnonodeshaveawavelengthconversioncapability(wavelengthcontinuityconstraint).Inordertosupporttherouteselectionbyants,eachnetworknodehasaroutingtablewithN-lentry.Inanodeiwithkineighbors,theroutingtablehasakicolumn.Eachentrycorrespondstoa1destinationnode

38、andeachcolumncorrespondstoaneighbornode.Thevalue几(isusedastheselectionprobabilityofneighbornodenwhenanantismovingtowardsitsdestinationnoded.Inordertosupportthewavelengthassignment,weintroducetheselectionprobabilityofeachwavelengthintotheroutingtable.Foreachneighbornode,letpbetheprobabilitythatanants

39、electsthewavelengthjwhenitmovestodestinationd.AnexampleofthenewroutingtablewhenW=2isshownin.Whenaconnectionrequestoccursbetweensourcenode1anddestinationnode6,node3willbeselectedasnexthopbecauseWavelength2ispreferredoverwavelength1becausePl<P2inthatcase.destinationeveryTtimeunits.HerePandTaredesig

40、nparameters.Eachantisconsideredtobeamobileagent:itcollectsinformationonitstrip,performsroutingtableupdatingonvisitednodes,andcontinuestomoveforwardasillustratedin.藻AntlaunchedUpdatepheromoneAntkilledFig.2.Ant'smovingandupdatingtasksAnantmovesfromasourcetoadestination,nodebynodeonaselectedwavelen

41、gth.Itsnexthopisdeterminedstochastically：aneighborisselectedbasedonitsselectingprobabilitiesintheroutingtable.Anantiskilledwhenitreachesitsdestinationnodeorwhenitcannotselectafreewavelengthontheselectedpathforitsnextmove.Toavoida“fiozen”statusinwliichallantsconcentrateononeroute(stagnation),arandoms

42、chemeisintroduced:eachantselectsitsnexthoprandomlywithanexploitingprobability(n).>noiseWhenaconnectionrequestarrives,thepathwillbedeterminedbasedonthehighestselectionprobabilitynodeamongneighbor'sentries.Thewavelengthassignmentisbasedonthewavelengthselectionprobabilitiesfromtheroutingtable,or

43、someothersconventionalheuristicscanbeusedoWheneveranantvisitsanode,itupdatestheroutingtableelementwiththeinformationgatheredduringitstrip.Theprincipleofpheromoneupdateisdescribedasfollows.Supposeanantmovesfromsourcestodestinationdfollowingthepath(s,，i-1,i,d).Whentheaiitamvesatnodei,itwillupdatetheen

44、tiyconespondingtothenodes:theprobabilityofneighbori-1isincreasedwhiletheprobabilitiesofothersneighborsisdecreased.Forthelastvisitedneighbori-1,theprobabilitiescorrespondingtofreewavelengthsareincreased,whereastheprobabilitiescorrespondingtobusywavelengthsaredecreased.Moreformally,supposethatattimet,

45、anantvisitsnodei,sothevaluesforroutingentryinnexttimet+1aredeterminedbythefollowingformula(rememberthatthesumofprobabilitiesforallneighborsisalways1):'乙一、_/:+5/r(0'n9s''.1(2)EWl1+2Asdescribedinapreviouswork9,smartagentscanefficientlyinimprovetheperformanceofant-basedroutingsystems.Ba

46、sedontheideaofsmartagents,thepheromoneupdatingwillaffectnotonlytheentrycorrespondingtothesourcenode,butalsowillaffectalltheentriescorrespondingtopreviousnodesalongthepath.Inordertofacilitysmartupdating,anantmustpushtheinformationaboutvisitednodesintoitsstack:nodeidentification,abinarymaskthatdetermi

47、nesthestatusoffreewavelengthsonthelinksittraversed(thismaskhasWbitscorrespondingtothenumberofwavelengths).Thisstackalsoservesforloopdetectionandbacktracking,toensurethatantswillnotmoveforeveronthenetworkoThereasonforusingawavelengthmaskisthatunderwavelengthcontinuityconstraint,thenumberoffreewavelen

48、gths(congestedinformation)canbefoundexactlyalongapath;thiswillenhancetheperformanceoftheACOapproach.Ateachnode,thewavelengthmaskisupdatedasbelow：MQm=Mw®Mlink(3)and7guaranteethat£二。人=1(thenormalizationconditionforwavelengthselectionprobability),andtheyalsoensurethattheamountofincreasedphero

49、moneisproportionaltothenumberoffreewavelengths.Forthewavelengthassignment;weuseasimpleheuristic:thewavelengthwiththehighestprobabilityamongthefreewavelengthswillbeselectedoOuralgorithmisbrieflydescribedasfollows:Antgeneration)DoForeachnodeinnetworkSelectarandomdestination;Launchantstothisdestination

50、withaprobabilityEndforIncreasetimebyatime-stepforants'generationUntil(endofsimulation)Antforaging)Foreachantfromsourcestodestinationddo(inparallel)Whilecurrentnodei<>dUpdateroutingtableelementsPushtrip'sstateintostackIf(foundanexthop)MovetonexthopElseKillantEndifEndwhileEndforRoutingan

51、dWavelengthSelectionForeachconnectionrequestdo(inparallel)SelectapathwithhighestprobabilitySearchafreewavelengthwithhighestprobabilityIf(found)SetupalightpathElseConsiderablockingcaseEndifEndfor4 SimulationResultsandAnalysisAnextensiveexperimentalstudybasedonNetworkSimulatorns-216hasbeenperformedtov

52、alidateournewant-basedalgorithmforRWA.Astheoriginalns-2supportspacketswitching,thisfeaturewasusedtosimulatetheants'moves.WesupposethatthecontrolplaneforopticalWDMnetworksisimplementedinanelectronicnetDynamicRoutingandWavelengthAssignmentinWDMNetworks835workthathasasametopologyastheopticalnetwork

53、.Anopticalroutingmodulewasaddedintons-2tosimulateourRWAalgorithmoWeusedthefixedroutingschemewithshortestpathalgorithmforperformancecomparison.AllthetestswereconductedbasedontheNSFnetworktopologywith14nodesand21linksasshownin,andW=8andW=16wereconsideredinourexperimentso5 ConclusionandFutureWorksInthi

54、spaper,wehaveproposedanant-basedmobileagentsapproachtosolvingtheroutingandwavelengthassignmentproblemsindynamicWDMnetworks.Wedevelopedanewroutingtablestructureandalsoawaytoadapttheroutingtableaccordingtonetworkstate,usingasuitablenumberofantsthatcontinuouslyexploitthenetwork.Oursimulationshowsthatth

55、enewant-basedalgorithmoutperformsthefixedroutingalgorithmusingshortestpathandFirstFitwavelengthassignmentscheme.AnadvantageofthisnewalgorithmisthatthepathforaconnectionrequestisdeterminedoComparisonsbetweennewAnt-basedalgorithmandFixedroutingalgorithm,(a)ComparisonresultswhenW=8.(b)Comparisonresults

56、whenW=16oImmediatelyonarrival,basedontheadaptingroutingtable,sothesetupdelaytimeissignificantlyreducedcomparedtothefixedroutingscheme.Ournewalgorithmisveryflexibleinthesensethatthenumberofantsinthenetworkcanbeefficientlycontrolledbysimplyadjustthelaunchingprobabilityofantstoachievethebestperformance

57、oInourfuturework,wewillextendthisalgorithmbyusingareinforcementlearningapproachsuchthatothersACOcontrolparameterscouldbeautomaticallyadjustedforagivennetworkcondition.TheotherheuristicsforroutingandwavelengthassignmentwithwavelengthconversionwillalsobeinvestigatedoAcknowledgement.ThisresearchispartlysupportedbytheGrand-In-Aidofscientificresearch(B)and,JapanSciencePro