Optimization Design of Computer Network Reliability Based on Genetic Algorithms

Transcription

1 775 A publcaton of CHEMICAL ENGINEERING TRANSACTIONS VOL. 51, 2016 Guest Edtors: Tchun Wang, Hongyang Zhang, Le Tan Copyrght 2016, AIDIC Servz S.r.l., ISBN ; ISSN The Italan Assocaton of Chemcal Engneerng Onlne at DOI: /CET Optmzaton Desgn of Computer Network Relablty Based on Genetc Algorthms Lje Lu Jln Engneerng Vocatonal College, Spng, Jln , Chna lulje9957@126.com Wth the acceleraton of the process of nformaton socety, not only the user's computer communcaton networks s ncreasng, but also the rapd expanson of computer communcaton network connectng regonal scale and network connectons. Network relablty optmzaton of network desgn s a classc problem. Due to the complexty of the network relablty wth the number of network nodes ncreases exponentally, t takes too much tme to accurate calculaton takes. It appears genetc algorthms, neural network, fuzzy neural network ntellgent algorthm for solvng ths problem provdes a new deas and approaches. Due to the complexty of the network relablty wth the number of network nodes ncreases exponentally, we want accurate calculaton takes too much tme, not even the result. Meanwhle, n order to make a better search algorthm performance, the paper also ntroduces the concept of co-evoluton accordng to the schema theorem. The ntroducton of the test group, usng a test group to retan better pattern, whle the nteracton between test groups and reconclaton group, thereby acheve the common purpose of evoluton. Fnally, the proposed algorthm smulaton comparson, the results show that the algorthm has good convergence and search results. 1. Introducton Computer communcaton network relablty concepts appear n the 1970s due to the rapd development of communcatons technology (Balog, 2007). Optcal fber technology, computer network technology, makng the network functon of growng, promote research computer communcaton network relablty problems contnue depth drecton (Putha and Sahoo, 2012). Wth the acceleraton of the process of nformaton socety, not only the user's computer communcaton networks s ncreasng, but also the rapd expanson of computer communcaton network connectng regonal scale and network connectons (Bureerat, 2013). Due to a computer communcaton network s wdely used n enterprses and nsttutons (Lu, 2015). Important areas of bankng, transportaton, communcatons, ndustral, defense, computer communcaton network powerful complexty of ts structure or not s drectly proportonal (Al, 2013). In order to ensure the system work safely and relably, theoretcal research focused on the need for other ndcators to optmze the desgn of the network relablty and mprove network relablty based on the optmzaton of the system, such as the lnk cost, mantenance cost, delay, obstructon (Mohan, 2012). Rate and response tme n practcal applcaton, the desgner n the constructon of the network system (Karaboga and Zhang, 2014). The man consderaton s two aspects: performance and nvestment cost of the network due to network systems are generally costly, and therefore costs are often become a network topology desgn s a very mportant constrant (Gandom, 2013). Currently, for most networks, as long as the small mprovement of ts topology desgn, you can save a certan amount of cost, "by re-optmzng the system performance wthout reducng, the applcaton of new deas, new technology, you can get consderable cost savngs (Montalvo, 2014). Based on a comprehensve study t has been proposed based on genetc algorthm computer communcatons network relablty analyss and optmzaton. To dscuss the establshment of the relablty of the model and relablty analyss n theory, to provde the necessary engneerng applcatons securty servce strategy, and network relablty study mult-objectve optmzaton, n order to establsh optmzaton algorthm based on the Please cte ths artcle as: Lu L.J., 2016, Optmzaton desgn of computer network relablty based on genetc algorthms, Chemcal Engneerng Transactons, 51, DOI: /CET

2 776 method of constructon of ntellgent computer communcaton network relablty purposes, to gve a specfc mplementaton cases. Due to the complexty of the network relablty wth the number of network nodes ncreases exponentally, whch s a NP problem, you want accurate calculaton takes too much tme. Based on prevous research on genetc algorthm s proposed based on the mproved heurstc algorthm, genetc algorthm strong global search ablty, wth some of the local search strategy, to overcome the shortcomngs of tradtonal methods at the same tme has made good effect. 2. The related theory and method 2.1 Algorthm for computng network relablty Complete state enumeraton method s to calculate the relablty of the network the most common and easest way (Afonso and Gandom, 2013). The man dea s: accordng to the requrements lsted envoy network durng normal operaton of the S event occurs, all possble mutually exclusve events Bt, =1,2, n. Then S can be expressed as (Nguyen, 2014): AS k A A A (1) ( ) k Therefore, the network relablty: Pr n S Pr B (2) 1 For a gven graph G (V, E) on each sde there are two states: runnng or falure. If your network has old edges, then t would have 2E-1 states. For larger networks, wth the ncrease n the number of edges, the number of states ncreases exponentally, so complete state enumeraton method s only sutable for small computng network relablty (Yang, 2012). Such methods are manly seekng network n accordance wth the prncple of ncluson-excluson formula combnatoral mathematcs relablty. Let Am s the talk events. By the ncluson-excluson prncple known, A1, A2,... Am. The probablty that at least one event s: Pr G PrA A A m1 A A A A A A Pr Pr 1 Pr m The topology of the network and the prncple of ncluson-excluson formula combnng the ntroducton of the concept of free p crcle graphs prove the ncluson-excluson prncple of two-termnal relablty of the formula not just destructve tems and networks p crcle dagram No-one correspondence, gvng an ncluson-excluson prncple can be obtaned drectly n the formula s not destructve term formulas: (4) njv1 G Ga Pr 1 Pr Wheren, J represents a network of non-g crcle dagram, n, v denote the network G vertces and edges. Ths result makes the prncple of ncluson and excluson algorthm smplfed. 2.2 The basc prncples of genetc algorthms Genetc Algorthm (GA) s a draw on natural selecton and genetc mechansms of thnkng global random search algorthm. It s the soluton of the problem mght POPULATION, put every possble soluton seen as ndvduals wthn a populaton, runnng the algorthm n the whole populaton random search space, accordng to a certan assessment strateges for each ndvdual evaluaton, constantly usng the selecton, crossover and mutaton of these three genetc operators, the problem of evolvng soluton untl an optmal soluton.ga executon process was shown n Fgure 1. GA basc elements ncludng structure, populaton ntalzaton code, select Optons, genetc operatons (crossover and mutaton), the objectve functon and ftness functon desgn, the termnaton condton selecton. Among them, selecton, crossover and mutaton three operatons s the core of genetc algorthms, Optons are selected or elmnated based on ndvdual ftness functon value n the sze of the parent to ensure that the drecton of optmal search algorthm; crossover operator s the man method to produce new ndvduals, whch determnes the global search ablty of GA; mutaton operaton s to generate new ndvdual helper method, whch determnes the local search ablty of GA. m (3)

3 777 Start Parameter Set Determnaton Encode the Parameter Set Intalze Populatons 1.Decode Parameters 2.Caculate Objectve functon 3.Mapped to ftness value 4.Adapt ftness value Evaluate Populatons Meet the condtons Populaton P(t+1) Populaton P(t) Y End 1.Selceton 2.Cross 3.Mutate 4.Other Operator N Genetc Operator Fgure 1: GA executon process 3. Experments and results 3.1 The mpact of network topology analyss Congental factor programmng computer communcaton network topology belong to computer communcaton networks, but also affect the relablty of computer communcaton network practce shows that: dfferent applcaton areas, dfferent levels of scale computer communcaton networks must have dfferent network topologes, otherwse mprove computer communcaton network relablty s just empty talk. mpact analyss of computer communcaton network topology of the computer network relablty of ths chapter to be studed core nterconnecton network topology s the man connecton computer communcaton networks between the components, whch can be used dagram, graph theory s the study and therefore the most powerful mathematcal tool nterconnecton network performance. Connected graph any edge or a node falure wll brng down the network. Despte the low cost of such a computer communcaton network, but from the relablty pont of vew to consder ts small fault tolerance, relablty s poor. For more mportant computer communcatons networks, not the use of such a computer communcaton network topology network topology bus structure shown n Fgure 2 (a), the network topology star structure shown n Fgure 2 (b), n addton to there s a rng network topology, as shown n Fgure 2 (c). Termnal controller... Node 1 Node 2 Node n Node n+1 Node 1 (a)bus Node 1 Node 2... Termnal controller Node n+1 Node 2... Node n+1 Node n Node n (b)star (c)rng Fgure 2: Computer network topology of the communcaton network

4 Mult-objectve optmzaton Sgnfcance of mult-objectve optmzaton problem s to fnd problems one or more solutons to enable desgners to accept all of the target value. Therefore, t s consdered the sngle objectve optmzaton problem s a mult-objectve optmzaton problem n a specal case, the varous departments n the engneerng, producton management and natonal defense buldng socety the problem encountered by most multobjectve optmzaton problem. for example, n the desgn of computer communcaton network backbone, to consder how to make the general cost delay as small as possble, relablty and survvablty to be as large as possble, whch optmzaton s a three ndexes can be sad that mult-objectve optmzaton problem n real lfe s abundant, and even everywhere. General mathematcal form of mult-objectve optmzaton problem as follows: 1 2 m 0 0,1,2,,m 0,1,2,,m V mn F x mn f x, f x,, f x g x st.. h x R T Let optmzaton system parameters (varable) have n parameters for optmzaton consttute selected (varable) set x: X x, x,, x x R, 1,2,, n (6) n X s the soluton space optmzaton problem n whch a set of parameters: x k x k x k x k 1, 2,, n (7) Evaluaton of optmzaton problem set performance ndcators wth m consttutng performance ndcators set H: H h, h,, h h R, 1,2,,m (8) m k 3.3 Applcaton of genetc algorthms n computer communcatons network relablty optmzaton In ths paper, nclude a computer network relablty optmzaton smulaton example calculaton. Computer network nodes N = 6, the computer network node relablty constrant constant α = 2, ᵝ = 2, the number of teratons for the genetc manpulaton 100, computer network lnk cost matrx C, and computer network relablty matrx R, respectvely As follows: (5) C R (9)

5 Genetc algorthm accordng to flow and flow adjustment algorthm genetc algorthms, smulated gradually solved. When the genetc manpulaton of the number of teratons s 100 tmes, the smulaton process s termnated. The smulaton solved a mnmum cost of 45 computer network lnks, and to ensure that the computer maxmum network relablty s If the network s slghtly more expensve than the average delay, then Wc = 0.8, Wd = 0.2, Wr = 0, optmal results are shown n Table 1; the results showed that the central node: 1,2, converted nto the tree structure has three sdes, They are: (3,1), (1,2), (2,4), 3,1,3,3,1,2,2,2,2 workstaton. 779 Table 1: Performance Indces Under dfferent Weghts and Optmzed result by GA Weght Network Cost Relablty Cost Satsfacton Relablty Satsfacton Synthetcal Satsfacton Wc=Wr=Wd=1/ Wc=Wr=0.5,W d= Wc= Through the above analyss shows that under these condtons the relablty of dfferent weghts, can get a better satsfacton can be sad that these mult-objectve optmzaton algorthm combned genetc and after, n the shortest possble tme to fnd a satsfactory soluton can be successful hgh relablty and low cost to solve the NP problem, and resolve quckly mplement topology optmzaton computer communcaton network. 4. Conclusons Due to the complexty of the network relablty wth the number of network nodes ncreases exponentally, we want accurate calculaton takes too much tme, not even the result. It appears genetc algorthms, neural network, fuzzy neural network ntellgent algorthm for solvng ths problem provdes a new deas and approaches. Due to the complexty of the network relablty wth the number of network nodes ncreases exponentally, we want accurate calculaton takes too much tme, not even the result. It appears genetc algorthms, neural network, fuzzy neural network ntellgent algorthm for solvng ths problem provdes a new deas and approaches. Meanwhle, n order to make a better search algorthm performance, the paper also ntroduces the concept of co-evoluton, accordng to the schema theorem, the ntroducton of the test group, usng a test group to retan better pattern, whle the nteracton between test groups reconclaton group, thereby acheve the common purpose of evoluton. Acknowledgments Ths work s supported by Jln Provnce Department of Educaton teachng reform of vocatonal educaton and adult educaton research n 2015: The Research on the Development and Applcaton of Currculum Resource Lbrary of Hgher Vocatonal Computer Applcaton Foundaton.(No. 2015ZCY198). Reference Afonso L.D., Maran V. C., & dos Santos C.L., 2013, Modfed mperalst compettve algorthm based on attracton and repulson concepts for relablty-redundancy optmzaton. Expert Systems wth Applcatons,40(9), Al A.M., Zendehboud S., Loh A., Elkamel A., & Chatzs I., 2013, Reservor permeablty predcton by neural networks combned wth hybrd genetc algorthm and partcle swarm optmzaton. Geophyscal Prospectng,61(3),

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