SHEN, Huayu

SUN, Yugeng

WANG, Zhaoxia

WANG, Zhiyong

English

Institutional Knowledge at Singapore Management University

Singapore Management University Institutional Knowledge at Singapore Management University Research Collection School Of Information Systems School of Information Systems 2-2007 Quality of service routing strategy using supervised genetic algorithm Zhaoxia WANG Singapore Management University, zxwang@smu.edu.sg Yugeng SUN Zhiyong WANG Huayu SHEN Follow this and additional works at: https://ink.library.smu.edu.sg/sis_research  Part of the Operations Research, Systems Engineering and Industrial Engineering Commons, and the Theory and Algorithms Commons Citation 1 This Journal Article is brought to you for free and open access by the School of Information Systems at Institutional Knowledge at Singapore Management University. It has been accepted for inclusion in Research Collection School Of Information Systems by an authorized administrator of Institutional Knowledge at Singapore Management University. For more information, please email cherylds@smu.edu.sg. Transactions of TianjinISSN 1006-Vol. 134982No. 1UniversityP冈48一052Feb. 2007Quality of Service Routing Strategy UsingSupervised Genetic Algorithm*WANG Zhaoxia(王兆霞)1,2，SUN Yugeng(孙雨耕)’，WANG Zhiyong (-E志勇)“，                    SHEN Huayu(沈花玉)’(1. School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072，China;2. Tianjin Key laboratory of Film Electronic and Communication Device, Tianjin University of Technology, Tianjin 300191，China)Abstract:A supervised genetic algorithm(SGA)is proposed to solve the quality of service(QOS)routing problems in computer networks. The supervised rules of inteligent concept are introducedinto genetic algorithms(GAs)to solve the constraint optimization problem. One of the main charac-teristics of SGA is its searching space can be limited in feasible regions rather than infeasible re-gions. The superiority of SGA to other GAs lies in that some supervised search rules in which theinformation comes from the problems are incorporated into SGA. The simulation results show thatSGA improves the ability of searching an optimum solution and accelerates the convergent processup to 20 times.Keywords:supervised genetic algorithm;supervised search rules;QoS routing    Genetic algorithms(GAs)can  deal  withcomplex   optimization   problems  beter  thantraditional optimization techniques [1'2].The use ofGAs for optimization tasks has become popular withthe  constant  development  of  new  algorithms，theoretical achievement and novel applications [ 3, 41.GAs aim at understanding computational systemsand developing more robust and eficient ones forsolving  complex  real-world  problems [41.Someresearchers solved routing problem with improvedgenetic  algorithms  which  were  proved  to befeasible [2, 41.The networks require the route tosupport the quality of service(QoS) requests. Tosupport the requirement of extensive QoS，routingalgorithm  needs  the  complicated  matrix        tocharacterize the network with indexes，such asdelay, bandwidth，packet loss rate，and cost’一，。〕.Thus，the routing problem based on QoS can beconverted to the optimum-searching problem thatsatisfies several objective functions simultaneously，and it is proved to be NP-complete problems, 61.    GA issuitable for solving NP problems.However, GA is not able to bias eficiently thesearch towards the feasible region in the constrainedsearch spaces. In real-world problems，it is dificultto design operators that avoid constraints entirelywhile stil being efective in locating useful feasiblesolution [21.    Thepurpose of this paper is to present anapproach in which most time is spent on searchingin feasible regions rather than infeasible regions.The constraints are transformed into the searchrules. The unique characteristic of the proposedsupervised genetic algorithm (SGA)that difersfrom other GAs is that there are some techniqueswhich can be built into SGA，and guarantee theSGA to search in feasible space. Here，SGA arisesfrom the investigation of real-world QoS routing  Accepted date:2006一06-29.  WANG Zhaoxia, born in 1972，  E-mail:zhxwang@ tju. edu. en.*Supported by China Postdoctoral  (No.2003005607)and Tianjinfemale，Dr, associate Prof.Foundation(No.2005037529)，Doctoral Foundation of Education Ministry of ChinaHigh Education Science Development Foundation(No.20041325).WANG Zhaoxia et al:Quality of Service Routing Strategy Using Supervised Genetic Algorithmproblem，which wilSections 2 and 3，andbe described in detail ininfeasibility of the proposedother GAs.Section 4SGA andwe analyze thecompare it with1  QoS routing problem    QoSrouting is composed of cost，bandwidth，packet los  rate，and delay s-'1.The QoS routingproblem focuses on finding a set of routes satisfyingthese factors.    Wemodel the topology network structure withundirected graph G=(V, E)，where V is the setof network nodes and E is the set of bidirectionallinks in the graph. The aim of routing selection is tosearch for the optimal path while satisfying the QoSrequirement between the source and the end nodes.Associated with each edge hj E E，B (l})is thebandwidth capacity，D(lij)the delay，W(lij)thecost，and E(hj)the eror rate.    Therouting request q of a single destinationconsists of source node，destination node and QoSrequest which includes bandwidth capacity Bu，eror rate L.，cost俄，and delay D, In general，the eror rate is almost 0 and the delay is smalwhen the signals transmit in the optical fibers. Weshould consider the eror rate and the delay of thenodes. The folowing conditions must be satisfied.    Delayproduced by the middle nodes fromthe network has N nodes and L=(Ii)NxN representsthe topology  network  matrix.                                                                                       Except diagonalelements，each element coresponds to the likelyexisting link hi，evidently，i=hi. The element hilocated in the matrix coresponds to the link lij fromnode i to j.1，if hiexistsin the topology network;0， otherw了场ise。resZe  -                                              (4)    Therouting problem can be described byN-dimensional binary routing evolution matrix，A=(:。)*、，and each element of the evolution matrixcoresponds to the link:。from node i to j，locatedon row 1 and column j.viSimilarly，  ifthe optimal route includes  link from node i to j; (5)  otherwise.the link elements that are notl0  护IJ、...Lsource to destination nodes    D}= 艺D (h,)                        l;aEincluding destination node)      The totalerror rate fromthe folowing formula:is  middle  nodes，              (1)located in the selected route are set to 0. We set thevalues of the diagonal elements as 0，thus the valueof。。presents whether the link hi from i to j isselected.    Thedecoding of this genetic algorithm mapsthe N-dimensional matrix to the representation ofbinary aray. Therefore，the optimal state in thegenetic evaluation routing matrix represents  anoptimal path from the initial source node to the finaldetermination node. For example，there are 6 nodesin the topology of optical fiber communicationnetworks;the matrix A can represent a route froma, to a6·is(jend to end must satisfyThe route is a。一a,、a4 -> a3 -+ a5(1一L})=n(1一L( hj) ) (j is middle 乙=(Ii)、、=I任E,nodes, including destination node)      The costfrom end to end is0hvz    气‘了乙10门leewewee|||||weeseswe000010nUO1  0  0OCUO0、，少产、，.2勺‘丹j/浮、Zf、      W.where l}j=Y,W(2。)    l�eEE E,，E, is the selected route，Eu C E; A=0  1D(hi)is the handling delay of node j; L(hi)is thelos  rate of node j ; W(hi)is the cost of link hi.2  Routing optimization based on SGA2.1 Coding and decoding mechanisms of SGA  by a    Thegenetic representation wil be crucial to the0 1 0 0It is obvious that  0 0 00 0 0each route00can be representedmatrix. The binaryconcatenation ofstringrepresenting a route isrow sthe success of GAs[41.details were describedmatrix.networkEvidently，only iftopology structure，(or columns)  in theincludes link li,. in the1，The simplified binary codingin Ref.[9」.Assuming that e. h,.= the element.rt·L49Transactions cj} Tianjin University Vol. 13  No.] 2007al, in the evaluation routing matrix may take 1.      Because the  elements  in the  link  matrixrepresent the real link from node 1 to j,     theinexistence of links are always set to constant 0.Values of the diagonal elements are always 0. Fig.1 gives the example of the direct encoding scheme ofthis routing problem. In general，the matrix of thenetwork topology and the route matrix are sparse.The useful information is stored in the non-zeroelements.  八’fl(1一:ijl'-ij)了二1(1一:‘)]’          (6)同The selected fitness function isEwhere F，G and H denote                    (7)the weight coeficients ofthree constraints;t i，1-cijsignal error rate and costsand wij denote the delay，be obtained bymeasuring，respectively，which cannetwork as the vital indexof routing;Du，L�，砚‘and doQOS.are designated by2.3 Supervised rules and  supervised  mutationoperatorA good search operatorprobability  of  findingshouldglobalincreaseoptimum。theX41No-free-lunch theorems̀  t01 also indicate the importanceof incorporating problem-specific knowledge into thebehavior of the algorithm. The supervised rules andsupervised mutation operator are used as searchoperators to ensure searching in feasible space.    From the  above  coding  methods， theconstraints for the QoS problems are described bythe folowing inequation or equation:、、!||||||||||||少0001101  1  0  0OnU八曰01  1  0  01  1  0  00  0  1  1oljwenUOO厂了1|le|||||||eses、(a) Network topology (b) Network topology matrix、、|||||||||||||尹0000101  0  0  0卫nU000000001  1  0  00  0  1  10  0  1  10  0  0  00  0  0  00 0 0 00  00  00  00  00 00000002了le||||Ieseseseswees‘、、||||||||||||||00011000000CU/声‘|性....1||||||(e) Upper triangle matrix (d) Route matrix gI 艺a。二01，=12，⋯，N; j=1,2，⋯,N、叉川，2Fig. 1 Network topology structure and its representation          withmatrix      Since therepresented bytopology  network  structure  is         92a i)0the undirected graph G=(V, E)，we only need to represent the upper triangle of thematrix which is given by Fig. 1(c)to reduce thechromosome length. The element  Ii indicates the=:1N-I N-IN; j=1,2，⋯,N93 艺aji=0=2，”’，N一1;j=2, --,N一1presence or absence of aj.  Once  the  topologyconnection  networkfrom node  structurei to  is      Itis to transform the above constraintsconfirmed，and the 0's wil be determined. We canreduce the length of the chromosome more bygeting rid of the 0's. Similarly，the routes can beincorporated into such representation.  Fig. l(d)shows a route from node 1 to 6，where the elementindicates the presence or absence of a selectedconnection from node 1 to 6 in the route.2.2  Establishment of evaluation function    Theevaluation function is as folows [9]:into ruleseasy  for supervisingmutationmethodsoperator. The neware as follows.  search operator andconcepts of SGA searchE,=_F  N2(}客wi}一。z·G。   N    N2    tijvi,一(刀。一d.)]2十乙二j 了    Definition 1(Search rule) The nodes(exceptsource and destination nodes)must be the inceptivenode of the next link，if it is the arriving node.    Definition 2(Preference rule)The destinationnode and the source node have the priority to beselected over other nodes when a node connectsmany nodes among which there is a destination nodeor source node.    Definition 3(Supervised mutation operatorand regeneration)  An integrated chromosome can50WANG Zhaoxia et al:Quality of Service Routing Strategy Using Supervised Genetic Algorithmbe divided into 2 parts from the select position inwhich the bit element value must be l，as caledsupervised mutation operator，diferent from thetraditional mutation operator which randomly flipssome of the bits in a chromosome.  Traditionalmutation can occur at each bit position in a stringwith some probability.  However，the supervisedmutation operator must occur at those positionswhere the bit value must be l.Then the selectedpart wil  regenerate the rest part by obeying thesearch rule and preference rule.  We cal  thisprocess regeneration operator.2.4  Realization of SGA    Performing specific routing algorithm is asfollows.    (1)Present route delay matrix}t +l}，costmatrix{w }1}and los  rate matrix  } I.L},}as theinformation of delay，cost and loss rate of currentlink，respectively.  Translate curent matrices tothree items.    (2)Generate the initial population matricesPO(P,，t=0)and translate current matrices tothree items according to the above rules andoperators. By using fitness function，the fitness ofal solutions in the colony is evaluated and the bestone is labeled.    (3)Create ofspring population P,,  fromparent population P, according to the rules andoperators of SGA. The fitness of al solutions in thecolony is evaluated and the best one is labeledcompared with the former if the former generationexists，then choose the beter one as the best one ofcurrent generation.    (4)Rule of stopping testing. If it is satisfied，stop;otherwise，t=t+1，turn to(3).3    Simulation analysis of SGA    Fig.2and Fig. 3 show two examples of thenetwork system topology [8' 9].One consists of 6nodes and 8 edges，and the other consists of 13nodes and several edges connecting the nodes.Each of nodes and edges is denoted by parameters.The elements between brackets are error rate anddelay，respectively. Cost marks are given on theside of edges.    Example I Employing the network topologystructure as shown in Fig. 2. Perform routing fromthe source node a, to the destination node a6.Population dimension is set to 10，and coeficient(1,(1 0、0斗) a,(10 ()510、.0.2)‘才、(10、.0.3) a,(1 0、.0.11Fig. 2 Topology structure and parameters of Example 103(10”.()2)07(10气.住3)13(10(川11、，()7) 02(10 ()5)Fig. 3 Topology structure and parameters of Example 2F = G = H 二proposedsolutionabove， canBy  employing  the  SGAobtain the global optimalas early as in the first generation.Example 2 Employing the network topologystructurenode 01as shown in Fig. 3.Perform routing fromto 10，and thesize of the population is setto 40. We can obtain the optimal path，and throughseveral simulations themean number of generationsto gain optimal solution is 3.Tab. 1 and Fig. 4 show experimental resultsobtained with SGA.The data clearly indicates thatthe genetic erasGAs [x' y].of SGA are less than those of otherTab. 1  Simulation results of SGA and other GAsMean genetic eraExample PopulationSGA Improved GA}9j CGA1562 6270O14    Theresults presented in Fig. 4 ilustrate thebenefits of SGA compared with CGA and otherimproved  GAs  because  SGA  increases  theconvergent speed greatly. The results of simulationvalidate that SGA can find the optimal path from                                                        一 51一Transactions of Tianjin University Vol. 13  No. 1 2007initial source node to final determination node in asingle run.  This algorithm can search out theoptimal routing under limited time for the problem.To take an in-depth analysis，the simulation of SGAcan also be employed in a large-scale network，which contains more than 100 nodes.independent that it does not rely on any specificnetwork which ensures it extensive applicability innetwork systems.Acknowledgments      The authors would like to thank Prof. Yao Xinand members of the CERCIA in University ofBirmingham for useful discussions.ReferencesSGAImproved GAC( i八:▲100    200    300              Genetic400    500    600.‘............‘....‘，....lesesC目OQn6.4气︸n曰                                                                      …10000    u。与。u曰仍的。ul公已。二。月-叮;仁。之era(a) Whole 600 genetic eraSGAImproved GACG八:今n月臼n64，乙0      -，:‘10000    U。一乞Ue叭的。u︺唱uo一户用~川州杯﹄0之20 40         60Genetic era9〔 100(b) First 100 genetic eraFig. 4          Normalizationfitness function vs. generation4                     Conclusions    In this paper，the  supervised  rules  ofinteligent concept are introduced into GA.  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Quality of service routing strategy using supervised genetic algorithm

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Quality of service routing strategy using supervised genetic algorithm

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