Reliable and Efficient Routing Using Adaptive Genetic Algorithm in Packet Switched Networks

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1 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 1, No 3, January 2012 ISSN (Onlne): Relable and Effcent Routng Usng Adaptve Genetc Algorthm n Packet Swtched Networks Rakesh Kumar 1 and Mahesh Kumar 2 1 Department of Computer Scence & Applcatons, Kurukshetra Unversty Kurukshetra, Haryana, Inda 2 Research Scholar, Department of Computer Scence & Applcatons, Kurukshetra Unversty Kurukshetra, Haryana, Inda Abstract To dentfy the optmal route s a complex task n packet swtched network because optmzaton depends upon a number of parameters. In ths paper Genetc Algorthm s used to locate the optmal route. Genetc Algorthm starts wth a number of solutons where each soluton s represented n the form of chromosome usng the permutaton encodng scheme. The success of Genetc Algorthm depends upon the number of operators such as selecton, mutaton and crossover. Needless to say crossover s most nnovatve. In ths paper crossover operators proposed namely 1-pont, 2-pont, and adaptve, have been customzed accordng to the need of computer network. The ftness of each soluton s evaluated n terms of hstorcal relablty factor, node success/falure and delay. The performance of the proposed approach has been compared wth Djkstra Algorthm and mprovement has been observed. Keywords:, Delay, Djkstra Algorthm, Genetc Algorthm, Relablty, Routng 1. Introducton Telecommuncatons networks technologes are growng faster and becomng more complex to handle dfferent types of physcal traffc. Today computer networks have applcaton n varous domans lke busness, educaton, research, Industry, e-commerce and many others. Network based applcatons are becomng more popular and easy to use wth the growth of Internet & development of new technologes & standards. In Data communcaton networks, such as the Internet and the Moble Ad-hoc Networks, routng s one of the most mportant areas that have a sgnfcant mpact on congeston and network s performance [1]-[3]. Research of the routng strategy s becomng the key theoretcal topc of the new generaton network archtecture. The growth of nternet based applcatons such as on lne shoppng, Vdeo on demand, vdeo conferencng, on lne bankng, e-tcket bookng, stock exchange and other real tme applcatons has generated new requrements for one-to-one and one-to-many relable and tme effcent communcatons. An deal routng algorthm should strve to fnd an optmum path for packet transmsson wthn a specfed tme so as to satsfy the Qualty of Servce [3]-[5]. For Shortest path problem there are number of search algorthms such as Bellman Ford algorthm, the Djkstra algorthm etc. to name a few [2]. Many of these algorthms fnd the shortest path consderng hop count/mnmum cost measures. They exhbt unacceptably hgh computatonal complexty for communcaton nvolvng rapdly changng network topologes and nvolvement of other routng metrcs lke path relablty, delay, bandwdth etc. for computaton of optmal path from source to destnaton [4], [5]. The selecton of routes n large-scale computer communcaton networks s extremely complex network optmzaton problems. Such problems belong to the class of nonlnear combnatoral optmzaton problems most of whch are NP-hard. The problem can be formulated as fndng a mnmal cost path wth greater path ftness that contans the desgnated destnaton and source nodes. The Shortest and relable Path routng problem s a Classcal combnatoral optmzaton problem. Evolutonary algorthms such as, genetc algorthms, neural networks, ant colony optmzaton etc. promse the soluton for such problems, as these have been used successful n many practcal applcatons. Neural Networks and Genetc Algorthms may also not be promsng canddates for supportng real-tme applcatons n networks because they nvolve a large number of teratons n general but wll be able to provde the adequate soluton. In lterature GA s the most popular technque to solve complex mult objectve optmzaton problems. Researchers have appled GAs to the Shortest Path routng problem, mult castng routng problem, ATM bandwdth allocaton problem, capacty and flow Copyrght (c) 2012 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

2 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 1, No 3, January 2012 ISSN (Onlne): assgnment (CFA) problem, and the dynamc routng problem [6]-[13]. It s noted that all these problems can be formulated as some sort of a combnatoral optmzaton problem. Genetc algorthms are multple, teratve, stochastc, general purpose searchng algorthms based on natural evoluton [14], [15]. They aren't nstantaneous, or even close, but they do an excellent job of searchng through a large and complex search space [16]. The man objectve of the current work s to fnd the best relable path usng personalzed GA wth varous crossover operators for data transmsson. The structure of the paper s organzed as follows: A method of representng the solutons s gven n secton 2. The proposed GA based routng technque s gven n secton 3. The bref revew of crossover technques s gven n secton 4. The expermental results of proposed work are presented n secton 5. Conclusons are gven n secton Genetc Algorthm Chromosome Representaton To apply GA for any optmzaton problem, one has to thnk a way for encodng solutons as feasble chromosomes so that the crossovers of feasble chromosomes result n feasble chromosomes [17]. The technques for encodng solutons vary by problem and, nvolve a certan amount of art. Proposed work has consdered permutaton encodng for chromosome representaton. Each gene of a chromosome takes a label of node such that no node can appear twce n the same chromosome. For example, let {1, 2, 8, 10, 12, 14} be the labels of nodes n a 6 node nstance, then a route { } may be represented as (1, 2, 8, 10, 12, 14). 3. Routng Technque based on Relablty and Delay Measures One of the most common problems encountered n networks s obtanng the shortest, relable, optmum path problem. The objectve of the proposed technque s to fnd the best relable path n a communcaton network usng personalzed GA wth hstorcal relablty factor, node success/falure and delay measures. Let us consder a pont-to pont communcaton network modeled by the smple connected graph G ( V, E ), where 'V ' s the set of nodes (or processors or routers) and ' E ' s a set of edges (or bdrectonal communcatonal lnks). Each element ( u, v ) n ' E ' s an edge jonng node u to node v. A path n a graph from a source node s to a destnaton node d s a sequence of nodes ( V 0, V 1, V 2,... V k ) where s V 0 and d.v k. The proposed technque uses the obtaned hstorcal relablty factor generated randomly between 1 and 100, bnary representaton ( 1 for successful transmsson of packets and 0 for falure) of node success/falure detals n the past n tme perod and a delay component at each node n path for the choosng the best relable path to sent the packets from source to destnaton. For current work n s taken as Relable and Optmzed Route Selecton The purpose of the proposed technque s to provde the optmum, relable route between the source and destnaton consderng hstorcal relablty factor, node success falure and delay constrants. Proposed Algorthm: 1. Perform Intalzaton of ntal Chromosomes/Routes 2. Repeat ( untl termnated) 3. Ftness Evoluton of each vald chromosome on bass of ftness functon 4. Perform selecton from evaluated chromosomes usng rank selecton for crossover 5. Perform crossover usng 1-pont/2- pont/adaptve technques 6. Perform mutaton wth probablty Qut the process f termnaton crtera (No. of Generatons) meet; It can be Tme, Mnmum ftness threshold satsfed 8. Go to step 2 f Not Termnatng Intally all the possble, connected paths from source to destnaton, whch are the chromosomes of GA, are generated, subsequently from the generated chromosomes, the x number of chromosomes are selected randomly to create the ntal populaton and then the ftness of each chromosomes n the populaton are calculated. Accordng to the rankng selecton wth the ftness value, the best y chromosomes are selected for crossover. Durng crossover operaton, we wll use the 1-pont, 2-pont and adaptve crossover to analyze the path ftness. After performng crossover, the repetton of smlar chromosomes n the produced offsprng as well as repetton of nodes n a chromosome s checked out and duplcaton s removed. Next, all process from the ftness evaluaton to repetton checkng are carred out C no of tmes to obtan the best relable path. Copyrght (c) 2012 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

3 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 1, No 3, January 2012 ISSN (Onlne): Intal Populaton Generaton and Chromosome Representaton The route table whch contans the possble paths from s to d s generated. Let ' RT ' be the generated route table consstng of the possble path from source to destnaton. The each path n the route table becomes as the chromosome of GA. The gene represents the node whle the chromosome represents the network path. Populaton s the collecton of x possble paths selected randomly from the ' RT '. The proposed technque uses the permutaton encodng n whch each gene represents the node number n a path. Chromosome representaton of the possble network path may be as whch s consttuted by nodes and the frst node s the source node and the last node s the destnaton. Hop count of the path wll be hop ( l 1 ) where l the total no of nodes n the path. The ftness functon s used to numercally evaluate the qualty of the each chromosome wthn the populaton. least one node s common to both the parent chromosomes [14]. If there s more than one common node, the frst occurrng common node from left s consdered for operaton. For example, n Fg.1 (a) the chromosome A and B are the parent chromosomes havng the node 7 as common node. Fg. 1 (a) 1-Pont operator 2-Pont : Select the parents from the populaton on bass of ther rankng. Randomly two ponts are chosen n parent chromosomes [14], [19]. The nodes n between the chosen ponts are exchanged to generate the chldren chromosomes Ftness Evaluaton The genetc algorthm searches for the optmum route wth hghest ftness where the ftness functon s used to assess the qualty of a gven schedule wthn the populaton [18]. The ftness functon that nvolves computatonal effcency and accuracy s defned n equaton (1): hop hop ( N ) ( N ) ( N ) ( N ) f 1 1 count ( A [ N ][ j ] ) ( N ) Where, n s the hstorcal satsfed relablty rato of the node N and s the mnmum requred relablty for transformng count ( B [ N ][ j ] 1 ( N ) the packets [18]. n s the hstorcal packet transmsson success rato of the node ( N ) N, s the capacty of the node N, ' b ' s the ( N ) packet sze and s the delay tme of the node N. The ftness of every chromosome n the populaton s evaluated and based on rankng selecton method the best y chromosomes are chosen for crossover operaton Operatons hop b (1) Three crossover methods 1-pont, 2-pont and adaptve crossovers are explaned below: Fg. 1 (b) 2-Pont operator Adaptve : From the selected parents A and B, fnd all the same nodes except source node and destnaton node, and establsh common nodes set Ψ [20]. If Ψ=Φ, there s no crossover operaton between A and B. Otherwse, select common node close to source node as a crossover pont (for Ψ >1, buldng block hypothess) from Ψ, then crossover the part after A and B. For example, f source node s 1 and destnaton node s 14. operaton s as follows: Fg. 1 (c) Adaptve crossover operaton The proposed work uses the 1-pont/2-pont/adaptve crossover. After performng crossover operaton, all the obtaned offsprng are subjected to repetton checkng and duplcate chromosome are removed subsequently and each chromosome s checked for node repetton and mantans the unqueness of node n a chromosome. Fnally the crossover operaton provdes the best z chromosomes. 1-Pont : In ths crossover operaton choose the parent chromosomes from the populaton such that, at Copyrght (c) 2012 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

4 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 1, No 3, January 2012 ISSN (Onlne): Selecton of the Best Route The processes n to are repeated C number of tme perod wth the next populaton set as ' follows: z x, where z s the obtaned set of ' offsprng from crossover operaton and x s the next set of chromosomes selected randomly from the route table. After completng the full teratons the best chromosome havng the hghest ftness s selected from the obtaned group of chromosomes. Snce the relablty of the nodes havng the dynamc nature, the relablty factor of the each node get changes n each tme perod as follows: Aˆ Aˆ A ˆ., where s the relablty devaton factor. The obtaned best chromosome represents the best relable path from the source s to the destnaton s. 4. Related Work on Operators Tradtonally, GAs have used one-pont or two-pont crossover [15]. Researchers have also carred out experments wth mult-pont crossover: n-pont crossover and unform crossover [21], [22]. Wth the n- pont crossover, n cut ponts are randomly chosen wthn the strngs and the n-1 segments between the n cut ponts of the two parents are exchanged. Unform crossover s the generalzaton of n-pont crossover. 5. Expermental Results Ths secton detals the expermentaton and performance evaluaton of the proposed work. The proposed work s mplemented n the MATLAB platform (verson 7.12) on Wndows 7 platform wth Intel Core 2 Duo processor (1.83 GHz) and 2 GB RAM. To llustrate the proposed work consder the example communcaton network wth 15 nodes shown n Fg.2. The mantenance management system of the dstrbuton network mantans the relablty statstcs, delay as well as the node falure status statstcs for the analyss purpose. The proposed technque uses assumed hstorcal relablty factor, assumed node success/falure status and randomly generated delay. Let us consder the source node as 1 and the destnaton node as 14. To fnd the optmum relable path, the route table contanng the possble p paths s generated usng a standard route generaton algorthm. From that, selected x paths forms populaton subjected to the personalzed genetc algorthm, consequently the ftness of the chromosome are calculated, subjected to the genetc operator crossover to produce best offsprng and hence produce new populaton. The above process s repeated for C tmes and fnally the best relable path wth hgh ftness s obtaned. The dynamc property of the relablty factor s carred out n each teraton (.e.) the relablty factor s updated n each tme perod. The table-1 llustrates some of the possble paths from the source 1 to the destnaton 14 accordng to the communcaton network n the Fg. 2, whch are generated usng a standard route generaton method and table-2 shows the change n path ftness durng generatons. Table 1: Routes No Path 1 [1,2,3,4,14] 2 [1,2,3,5,14] 3 [1,2,3,6,14] 4 [1,2,3,7,14] 5 [1,2,3,8,14] 6 [1,2,3,10,14] 7 [1,2,3,11,14] 8 [1,2,3,12,14] 9 [1,2,3,13,14] 10 [1,2,3,15,14] Fg. 3 Relable and Optmum path Table 2: Ftness values Iteraton No Ftness Performance Analyss Fg. 2 Communcaton Network The performance of the proposed technque s evaluated n the statc envronment wth the same source node 1 and destnaton node 14 and ts ftness s analyzed usng dfferent crossover methods wth the Djkstra algorthm. Comparson s made usng delay component n ftness functon as well as wthout delay component n Copyrght (c) 2012 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

5 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 1, No 3, January 2012 ISSN (Onlne): ftness functon wth dfferent crossover methods. The table-3 summarzes the ftness values of the varous best paths obtaned for proposed technque wthout delay wth dfferent crossover methods and Djkstra algorthm n statc envronment for dfferent number of teratons. The table reflects that the ftness of the best path obtaned usng proposed technque s hgher than the Djkstra algorthm. The proposed technque s evaluated for dynamc envronment by determnng the path wth dynamcally varyng relablty factors. The proposed technque s evaluated wth the dfferent no of nodes and generatons. From table-3 t can be observed that adaptve crossover gves better result n path ftness. Overall also from route ftness bass proposed technque represents hgh performance n the statc envronment wth the Djkstra algorthm. Table 3: wthout delay component n ftness functon 30 Iteratons Ftness Ftness Ftness Ftness Djkstra 1-Pont 2-Pont Adaptve Path Ftness Ftness wth delay for 30 Iteratons Fg. 4 Performance Comparsons 1st Run Ftness 2nd Run Ftness 3rd Run Ftness Average Ftness Convergence Graph for ftness n varous teratons usng crossover s shown n fgure-5 (a) and fgure-5 (b) below: From table-4 and fgure-4 t can be observed that adaptve crossover gves better result n path ftness wth delay. Table 4: wth delay component n ftness functon 30 Iteratons Ftness Ftness Ftness Ftness (Djkstra) 1-Pont 2-Pont Adaptve Fg. 5 (a) Convergence Graph Fg. 5 (b) Convergence Graph 6. Concluson In ths research work, the use of Genetc Algorthm has been proposed to fnd out the optmal route n packet swtched network. The three dfferent types of crossover operators have been proposed.e. 1-pont crossover, 2- pont crossover and adaptve crossover and the performance have been evaluated usng a ftness functon Copyrght (c) 2012 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

6 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 1, No 3, January 2012 ISSN (Onlne): that employed the parameters hstorcal relablty, node success/falure and delay at each node. Whle dong the comparson of performance t has been observed that adaptve crossover has outperformed the 1-pont crossover and 2-pont crossover. Moreover all the varant of Genetc algorthm.e. 1-pont, 2-pont and adaptve crossover have outperformed the Djkstra algorthm. The researchers are of the opnon that further mprovement can be obtaned by hybrdzng the genetc algorthm wth other ntellgent optmzaton technques such as tabu search and ant colony optmzaton. References [1] W.A. Chang, and R.S. Ramakrshna, A Genetc Algorthm for Shortest Path Routng Problem and the Szng of Populatons, IEEE transactons on evolutonary computaton, vol. 6, no. 6, pp , Dec [2] W. Stallng, Hgh-Speed Networks TCP/IP and ATM Desgn Prncples, Prentce-Hall, [3] M.K. Al and F. Kamoun, Neural networks for shortest path computaton and routng n computer networks, IEEE Trans. Neural Networks, vol.4, pp , Nov [4] D.C. Park, and S.E Cho, A neural network based mult-destnaton routng algorthm for communcaton network, n Proc. Jont Conf. Neural Networks, pp , [5] C.W Ahn, R.S Ramakrshna, C.G Kang, and I.C Cho, Shortest Path routng algorthm usng Hopfeld neural network, Electron. Lett., vol. 37, no.19, pp , Sept [6] M. Munemoto, Y. Taka, amd Y. Sato, A mgraton scheme for the genetc adaptve routng algorthm, n Proc. IEEE Int. Conf. Systems, Man, and Cybernetcs, pp , [7] J. Inagak, M. Haseyama, and H. Ktajma, A genetc algorthm for determnng multple routes and ts applcatons, In Proc. IEEE Int. Symp. Crcuts and Systems, pp , 1999, [8] Y. Leung, G. L, and Z.B. Xu, A genetc algorthm for the multple destnaton routng problems, IEEE Trans. Evol. Comput., vol. 2, pp , Nov [9] Z. Xawe, C. Changja, and Z. Gang, A genetc algorthm for mult castng routng problem, n Proc. Int. Conf. Communcaton Technology (WCC- ICCT2000), pp , [10] Q. Zhang, and Y.W. Leung, An orthogonal genetc algorthm for multmeda multcast routng, IEEE Trans. Evol. Comput., vol. 3, pp.53 62, Apr [11] H. Pan, and I.Y. Wang, The bandwdth allocaton of ATM through genetc algorthm n Proc. IEEE GLOBE COM 91, pp , [12] M.E. Mostafa, and S.M.A. Ed,, A genetc algorthm for jont optmzaton of capacty and flow assgnment n packet swtched networks, n Proc. 17th Natonal Rado Scence Conf., pp. C5-1 C5-6, [13] N. Shmamoto, A. Hramatsu, and K. Yamasak, A dynamc routng control based on a genetc algorthm, n Proc. IEEE Int. Conf. Neural Networks, pp , [14] D.E. Goldberg, Genetc Algorthms n Search, Optmzaton, and Machne Learnng Addson Wesley, New York, [15] J. Holland, Adaptaton n Natural and Artfcal Systems The Unversty of Mchgan Press, Ann Arbor, [16] A.M. Saad, M. Mustafa, N. Amer, and AbuAl Hybrd dynamc routng protocol for fndng an optmal path to routers Internatonal Journal of Academc Research, Vol. 3. No. 2, Part IV, Mar., [17] A.H. Zakr Genetc Algorthm for the Travelng Salesman Problem usng Sequental Constructve Operator Internatonal Journal of Bometrcs & Bonformatcs (IJBB), Volume (3): Issue (6), 2004 [18] Rakesh Kumar and Mahesh Kumar, A novel routng technque for computer networks usng personalzed GA, (Under revew process n Journal) [19] K.A. DeJong, Analyss of the Behavor of a Class of Genetc Adaptve Systems PhD Thess, Unversty of Mchgan, Ann Arbor, [20] W. Xlo-Yan, and L. Yang, Routng optmzaton algorthm of moble Ad-Hoc networks based on Genetc Algorthm Amercan Journal of Engneerng and Technology Research, Vol. 11, No.9, [21] L.J. Eshelman, R.A. Caruana, and J.D. Schaer, Bases n the crossover landscape In Proc. of the 3rd Int. Conf. on Genetc Algorthms, Morgan Kaufmann, [22] G. Syswerda, Unform crossover n genetc algorthms, In Proc. of the 3rd Int. Conf. on Genetc Algorthms, 2-9, Morgan Kaufmann, Rakesh Kumar obtaned hs B.Sc. Degree, Master s degree Gold Medalst (Master of Computer Applcatons) and PhD (Computer Scence & Applcatons) from Kurukshetra Unversty, Kurukshetra. Currently, he s Assocate Professor n the Department of Computer Scence and Applcatons, Kurukshetra Unversty, Kurukshetra, Haryana, Inda. Hs research Copyrght (c) 2012 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

7 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 1, No 3, January 2012 ISSN (Onlne): nterests are n Genetc Algorthm, Software Testng, Artfcal Intellgence, and Networkng. He s a senor member of Internatonal Assocaton of Computer Scence and Informaton Technology (IACSIT). Mahesh Kumar obtaned hs B.Sc. Degree, Master s degree n Scence (IT) and Master s degree n Engneerng (Computer Scence & Engneerng) from Kurukshetra Unversty, Kurukshetra. Currently, he s a research scholar n the Department of Computer Scence and Applcatons, Kurukshetra Unversty, Kurukshetra, Haryana, Inda..Hs research nterests are n Computer Networks, Database systems and Genetc Algorthms. Copyrght (c) 2012 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.