JOURNAL OF NETWORKS, VOL. 8, NO. 9, SEPTEMBER 2013 2143 Web Servce Composto Optmzato Based o Improved Artfcal Bee Coloy Algorthm Ju He The key laboratory, The Academy of Equpmet, Beg, Cha Emal: heu0123@sa.com Lag Che 1, Xaolog Wag 1, ad Yoggag L 2 1. Compay of Postgraduate Maagemet, the Academy of Equpmet, Beg, Cha 2. Malbox No. 103, Jagy of Jagsu Provce, Cha Emal: sky35022123@gmal.com Abstract Web servce composto s a hot ad actve research area servce oreted archtecture (SOA). Wth hghly developg of Web servce, users pay more ad more atteto to qualty of servce (QoS). I order to obta hgh comprehesve qualty composte servces, a optmzg method was preseted ths paper. Frstly, buld represetatve QoS propertes quattatve models ad QoS aggregato models, the proposed the mathematcal model of Web servce composto optmzg problem. Secodly, preseted a mproved artfcal bee coloy algorthm (I- ABC) ad appled to solvg Web servce composto optmzg problem, I-ABC performace was ehaced by mportg taboo strategy ad chaos. Fally, some smulato expermets are gve, ad the results proved I- ABC has perfect performace Web servce composto optmzg tha ABC. Idex Terms Web Servce, Qualty of Servce (QoS), Improved Artfcal Bee Coloy (I-ABC), Composto Optmzato, Modelg I. INTRODUCTION Web servces have bee wdely used the dustral ad commercal feld. Wth the sharply creasg the umber of Web servces, there wll bee a large umber of servce provders ad servce cosumers, ad thus evtably lots of Web servces wth the same fucto ad dfferet qualty of servce (QoS) wll appear. Accordg to user s QoS requremets, to select atomc servces from the Web servce wth same fucto ad dfferet qualty of servce (QoS) plays a very mportat role servce composto process [1]. Hgh qualty servce composto has become a mportat of Web servce developg. I recetly research result of servce composto optmzato algorthm, Zeg [2] preseted QoS-drve servce selecto problem ad buld model of servce composto optmzato usg mult-obectve optmzato. So that t could be solved usg the method of mult-obectve decso makg [1-4]. However, servces composto optmzato, lots of problems stll eed further study, ad may mperfect exst curret optmzato method. There are three key pots web servces composto optmzg problem: (1) Modelg of Web servce QoS propertes; (2) Modelg of QoS propertes aggregato; (3) Composto optmzato algorthm. I order to solve these problems, we presetg a ovel composto optmzato method based o prevous works ths paper. The followg works were doe: Frstly, we establsh four maor QoS propertes calculato models that user cocered, QoS aggregato models s also gve based o composte servces topology. The QoS comprehesve evaluato fucto (optmzg target) s proposed based o these models. Secodly, a mproved artfcal bee coloy algorthm s proposed to solve Web servces composto optmzato problem. The mproved algorthm combg tuba searchg ad chaos has perfect performace composto optmzg.. II. RELATED WORK I Web servce QoS modelg research, Zhag et el. [1] presets a ovel hybrd data type (cludg real umbers, terval umbers, tragular fuzzy umbers ad tutostc fuzzy umbers) QoS model, ad preseted a dyamc composto algorthm based o TOPSIS (DWSCA_TOPSIS) s to evaluate mult-perod hybrd QoS data. A tutostc fuzzy set-based QoS model was preseted Ref. [6]. A QoS model based o radom umbers was reseted Ref. [8]. There are lots of papers take Web servce composto optmzato as a costrat mult-obectve optmal problem. Zheg [7] proposed a collaboratve flterg approach for predctg QoS values by takg advatages of past usage expereces of servce users, ad preseted a QoS-aware mddleware platform ad a pla selecto algorthm based o smple addtve weghtg method for the purpose of servce composto. Xa [9] modeled o dyamc compostg optmzato problems based o QoS propertes, ad proposed a mproved at coloy algorthm to solve the optmzg model. Ths method adapts the dyamc ad stable characterstcs of Web servces. Zhag [10] proposed a QoS oreted ad treebased approach to mplemet servce composto. To do:10.4304/w.8.9.2143-2149
2144 JOURNAL OF NETWORKS, VOL. 8, NO. 9, SEPTEMBER 2013 solve dstrbuted servce composto ope dstrbute evromet, Su S et al. [11] proposed a algorthms that ca support multple QoS regstry ceters servce composto. Recet research shows heurstcs algorthms have advatages solvg composto optmal problems, thus lots of heurstcs algorthms, such as greedy algorthm[12], quatum geetc algorthm [13], mmue geetc algorthm [14], at coloy algorthm [15][16] ad partcle swarm optmzato[17]. Besdes, there are may research focus o automatc tellget servces selecto ad composto [18][19], real tme QoS propertes are maor depedece servces selecto ad composto. I recet years, the popularty of Artfcal Bee Coloy (ABC) algorthm has grow sgfcatly as a ew global search techque. It was (ABC) preseted by Dervs Karaboga [20] ad further developed by Karaboga ad Basturk [21]-[22]. Al et.al [23] preseted a ovel hybrd optmzato method (HRABC) based o artfcal bee coloy algorthm ad Taguch method ad appled t to structural desg optmzato. Artfcal Bee Coloy algorthm ad ts mproved verso have bee troduced ad appled for effcetly solvg fucto optmzato [20], costraed optmzato [22], ad systems egeerg [25]. III. MODELING OF QOS PROPERTIES A. QoS Propertes of Web Servce Amog all dfferet QoS propertes, some of whose values are offered by servce provders, whle some others values are user depedet, whch have dfferet values for dfferet users, such as respose tme, resource costs, relablty, avalablty, packet loss rate ad so o. Ths paper selected four represetatve QoS propertes to evaluate Web servce [3]. 1) Respose tme T: It s the measured tme betwee user sedg vocato ad gettg respose. The Respose tme cluds the servce processg tme ad etwork trasmsso tme, whch s dvded to vocato trasmsso tme T tral ad respose trasmsso tme T res, T prc stads for servce processg tme. So the respose tme ca be calculated by equato (1). T Ttra Tprc Tres (1) 2) Servce costs C: Servce costs are prce of servce or local computg resource cosumg, whose value are ofte offered by servce provder. 3) Relablty R: It s the percetage of servce successfully respodg vocato wth a ut of tme. Relablty s the overall measure of a web servce to mata ts servce qualty ad the base formato derved from the user s feedbacks [1]. N s s the umber of successful resposes, whle N sum s the total umber of vocatos, Relablty ca be calculated by R = N s /N sum. 4) Avalablty A: It s the probablty of servce respodg correctly after recevg a vocato deftve tme. Avalablty s usually descrbed by mea tme to falure (MTTF) ad mea tme to repar (MTTR). It ca be calculated by equato (2). A MTTF MTTR MTTF Dfferet QoS propertes are qute dfferet, ot oly the dmesoless magtude, but also the way of descrbg qualty, some eve ust atagostc. For example, favorable servces ofte eed lttle respose tme T but bg R. Cosequetly, t s prmary to classfy ad ormalze QoS propertes for comprehesve evaluatg servce qualty. Ordarly, QoS propertes was dvded to postve ad egatve. The less Respose tme T ad servce costs C s, the better servce s. So they are egatve ad ormalzed by equato (3). V m max Q Qm max m, 0 max m Q Q Q Q max m 1, Q Q 0 O the cotrary, the bgger Relablty R ad avalablty A are, the better servces are, So they are postve ad ormalzed by equato (4). V m Qm Q max Q Q m max m, Q 0 m Q max m 1, Q Q 0 where m (1,2,, N) represets umber of servces, (1,2,3,4) represets umber of propertes, Q, Q max (2) (3) (4) m represets the maxmum ad mmum value of the -th property, ad Q represets the -th property of servce m. m Accordgly, the qualty of Web servce ca be descrbed by a vector V [ V1, V2, V3, V4 ], where V1, V2, V3, V4 are the ormalzato value of correspodg QoS propertes T, C, R, A. QoS comprehesve evaluato value Q could be calculated by equato (5). where 4 Q V 1 4 1, 且 1 0 1 represets weght of the -th property. B. QoS Model of Composte Web Servces Web servce composto tegrates the exstg atom servces accordg to certa logcal order, by whch greater fucto composte servce could be provded as a whole. There are three frequetly-used dyamc servce compostg methods: compostg based o real-tme commucato, UMI modelg ad terface matchg. Where complex composte servce are abstractly descrbed by chart of busess process executo, whch s the logcal work flow. Just as show Fgure 1. (5)
JOURNAL OF NETWORKS, VOL. 8, NO. 9, SEPTEMBER 2013 2145 Start WS1 WS2 S1 S2 WS3 WS4 WS5 S3 S4 S5 WS6 WS7 WS8 S6 S7 WS9 WS10 Fgure 1. Chart of busess process executo Ed where S s a matchg ode of servce terface, arrow WS s a atomc servce, Start ad Ed are vrtual servce codto odes. Ay path betwee Start ad Ed s oe realzg scheme of servce composto. Atom servce, whch s autoomous ad has depedet fucto o some degree, s basc costructoal elemet of Composte servce. There are four kds of basc compostg structures betwee amog atom servces: sequece, cocurret, selecto ad loop. Accordg to the result of paper [6], t s easy to trasform the other three basc compostg structures to sequece, whch s show fgure 2. Therefore, t s eough to smply buld QoS propertes calculato models of sequece structure. What have bee show Table I. TABLE I. Propertes Equato Sart WS1 S1 WS2 WSN SN WSN+1 Fgure 2. Sequece busess process executo Ed QOS PROPERTIES MODELS OF SEQUENCE COMPOSITE SERVICE Respose tme T T T 1 Servce costs C C C 1 Relablty R R R 1 Avalablty A A 1 A 1 I table 1, T, C, R ad A are QoS propertes of costructoal servce, s the total umber of costructoal servces. Composte servce s QoS propertes ormalzed vector Vcom [ V1, V2, V3, V4 ] ad comprehesve evaluato value Q accordace wth (1) (2) ad (3) calculato. IV. OPTIMIZATION BASED ON IMPROVED ABC A. Basc Prcples of Artfcal Bee Coloy Algorthm Artfcal bee coloy algorthm (ABC) s a swarm tellgece optmzato algorthm proposed by Karaboga 2005 [20]. ABC s bee swarm cludes leadg bees ad followg bees. I optmzg, ABC takes optmal soluto as the bggest food source. The process of optmzg s the process of bee swarm searchg for food. Frstly, ABC radomly geerates tal populato cludg SN leadg bees, whch locate SN food sources (a food source meas a soluto) correspodgly. The, every food source eeds be searched by bee swarm for T tmes. Of course, the T s set artfcally. The detaled searchg operato s that: each leadg bee locally search correspodg food source oe tme at frst, f a bgger source s fd, replacg old source wth ew source, or else persstg old; the, all leadg bees retur back to dacg zoe ad sed hoey quatty formato of food source to followg bees, whch radomly select food sources accordg to the formato, of course, bgger source s more possbly be selected; at last, followg bees locally searchg selected sources oe tme, persstg bgger food sources (perfect solutos) ust lke frst step. We loop do t search ths way utl fd the bggest food sources (global optmal soluto) or satsfy the termato requremet. Assumg posto of the -th curret food source s X ( x 1, x 2,, xd ), leadg bees ad followg bees locally search aroud accordg to equato (6). y x rad ( x - x ) (6) k where : k (1,2,, SN), (1,2,, d) ; rad s a radom umber wth (-1,1), whch cotrols the geerato rag of the ew food source (soluto), ad also represets bees comparg of two sources wth the vsual scope. The geerato rag progressvely become smaller wth searchg for ear-optmal solutos. Followg bees udged expectat proft ad selected food source by observg leadg bees wagglg dace, whch cotas hoey qualty formato. Proft s represeted by ftess of food source. Probablty of selecto s determed by equato (7). P ftess (7) SN 1 ftess where ftess s ftess value of the -th soluto, ad SN s the umber of solutos. If a soluto ( ot curret global optmal soluto ) has ot be mproved after lmt (1,2, N) tmes loop searchg, what possbly meas the soluto s a local optmal oe, so that t should be qut ad replaced by ew radom oe. Assumg the abadoed soluto s X ( x 1, x 2,, xd ), ew soluto ca be geerated accordg to equato (8) x x rad ( x x ) (8) m max m where : xm ad xmax respectvely represet the mmum ad maxmum value of -th dmeso curret all solutos, rad s a radom umber wth (0,1). A. Algorthm Improvemet As a recetly emerget tellget optmzg algorthm, ABC s applcato ad mprovemet are prmary stage, so t has commoly dsadvatage, such as low search effcecy complex problem ad easy fallg to local optmal soluto. I order to ehace searchg capacty, we mposed taboo strategy ad chaos optmzato to mprove ABC. Taboo strategy
2146 JOURNAL OF NETWORKS, VOL. 8, NO. 9, SEPTEMBER 2013 Itroducg taboo strategy to ABC to avod followg bees excessvely repeated search for the same soluto, ad mprove the global searchg effcecy. The selecto strategy of followg bees s that f a soluto updates three tmes cotuously, forbd selectg t; else f a soluto has t bee updated over two tmes cotuously, crease selectg probablty ad get to premature checkg process prevously. Detaled selecto probablty s determed accordg to equato (9). SN P ftess / ftess (9) 1 where : s taboo strategy weght, whch s determate by equato (10). 0 Z [1,1,1] 1.5 Z [1,0,0] or[0,0,0] 1 else (10) where : Z () s the update record table, whch s realtme updated accordg to last three tmes soluto selecto. As show Fgure 3, the former d bts structure data of soluto s the posto vector, d+1 to d+3 bts are update records of local searchg last three tmes, 1 represets posto s updated whle 0 represets o. Update record table updatg automatcally after each searchg. d+4 bt s taboo strategy weght, whose value s automatcally updated accordg to the update records table. Fgure 3. Data structure of the soluto What should be oted s the value of taboo strategy weght. Whe the food source are costatly updatg (.e. Z [1,1,1] ), the soluto s ftess s creasg ad o eed be locally searched, so set the weght 0 order to forbd beg selected. Whle soluto has t bee updated over two tmes cotuously (.e. Z [0,0,0] or[1,0,0] ), t s probably that the soluto fall to local optmal soluto. It should crease the selecto probablty (.e. 1.5 ) ad the get to premature checkg process prevously whe Z [0,0,0] after local searchg. Addtoally, other states set 1. Thus, the local optmal soluto could be qut early ad ehace global searchg effcecy. Of course, f the soluto s global optmal soluto, searchg process could also termate earler. Chaos Optmzato ABC premature problem s mproved by replacg abadoed soluto wth ew soluto geerated usg chaos. Chaos traverses all states o ther ow laws, characterzed wth radomess ad regularty. Improved ABC takes advatage of chaos search ergodcty ump out of local optmal soluto. Assumg the curret stagat soluto s X ( x 1, x 2,, xd ), takg Logstc equato [24] as chaotc terato equato (equato (12)) to search the premature soluto by chaos searchg, whose ma steps are show as follows: step1. Mappg xl [ u, v ] to the doma of Logstc equato, t s assumed that u ad v are the upper ad lower of x l, the mappg fucto s: 0 ( xl - u ) t l ( l 0,1,, N ; 1,2,, d ) v - u (11) step2. Geeratg chaotc sequece t teratvely by usg Logstc equato, maxmum teratos tmes s N, ad settg 4, t [0,1], 0,1,, N ; ( 1) t t (1- t ) (12) step3. Chaos sequece t substtutes to equato (11) to solve x l, Obtag Xl ( xl1, xl 2,, xld ) ; ad the calculatg the ftess value, comparg wth the orgal soluto ad persstg more perfect solutos; step4. If terato tmes arrval maxmum N, optmzato process go to the ed, or else retur to step 2. B. Optmzato Problem Solvg Accordg to the composte servce QoS model bult Part 2.2, Web servce composto optmzg problem based o QoS ca be descrbed as selectg approprate servce from atom servces lbrares orderly ad combg to fuctoal requremets satsfed composte servce, whch has the hghest value of Q. As show Fgure 4, S s mddle matchg ode, block represets lbrary cludg the same fucto atom servces. A arrow represets a atomc servce stace WS ( T, C, R, A ). Modelg servce compostg as a fte-dmesoal vector X [ x1, x2,, x m ] though umbered each servce the lbrares. For example, the composte servces WS 13 -WS 21 -WS 48 -WS 57 ad WS 19 - WS 34 -WS 45 -WS 52 ca be descrbed as X [3,1,0, 8,7] ad X [9,0,4, 5,2]. Each bt value of X represets the umber of atomc servce stace. Takg comprehesve evaluato value Q ad composte servce model X as ftess ad soluto space to buld the fucto model of Web servce composto optmzg. As show follows: 4 ftess( X ) Q( X ) V( X ) 1 st. x 0, 1,, m V1( X ) v1 V2( X ) v2 V3( X ) v3 V4( X ) v4 (13)
JOURNAL OF NETWORKS, VOL. 8, NO. 9, SEPTEMBER 2013 2147 Fgure 4. Process of Web servce composto optmzg where : V ( X) s the ormalzato vector of composte servce s QoS propertes (T, C, R, A), whch s determed by X ; v 1, v 2, v 3, v 4 are lowest costrat offered by customers; m s the dmeso of X ; costrat of x s determed by the total umber of staces the -th lbrares. Gve model s soluto X s teger vector, mproved ABC does t apply to solve Web servce composto optmzg model utl soluto space reformed. Therefore, t s eed to scatter soluto space, the detal process s replacg equato (4) wth equato (11) y x rad ( x - xk ) 0.5 (14) where : [] s takg teger operato. There are two ways costrats processg: oe s a rule-based approach; the other s based o pealty fucto. For coveece of solvg, usg death pealty fucto to dear wth each QoS property costrat, ad the ftess value could be calculated by equato (12). ftess( X ) 0,( V( X ) v, 1,2,3,4) 4 ftess( X ) Q( X ) V( X ), else 1 (15) Specfc operato steps of solvg composto optmzg model usg mproved ABC are as follows: step1. Italg leadg bees umber SN, total teratos tmes T max, maxmum loop search tmes lmt ad maxmum chaos terato tmes N; step2. Radomly geeratg SN tal solutos X ad calculatg ther ftess (.e. ftess( X ) ) accordg to composte map; step3. Geeratg ew soluto Y ad calculatg ts ftess accordg to equato (11) ad (12), f ftess(y )> ftess(x ), replacg X wth Y, or else persstg X. step4. Usg equato (8) ad (9) to calculate selecto probablty P, followg bees selects soluto ad does local searchg accordg wheel strategy; step5. Judgg whether to qut soluto, f t exsts, usg chaos search to geerate ew soluto ad go o searchg utl ump out local optmal soluto, ad recordg the best soluto X best by far; step6. Judgg whether to meet loop termato codto, f do, output the optmal soluto X best, otherwse returs step3. V. SIMULATION AND ANALYSIS There s o stadard test platform ad measure data by far. I ths paper, a great qualty QoS data were radomly geerated for smulato, fve mddle matchg odes were set,.e. sx atomc servce lbrares, each of whch cotaed 100 staces, ad each stace had four QoS propertes value T, C, R, A radomly geerated by computer. I mproved ABC, total terato tmes Tmax 1200, loop tmes lmt = 5, Chaos maxmum teratos tmes N=10, leadg bees umber SN were orderly set to 10, 15, 20. The smulato results of three dfferet SN settgs have bee show Fgure 5. Fgure 5. Average covergece terato Fgure 6. Success rate of composto optmzg It s easy kow from Fgure 5, average covergece terato T decreases as SN creases, what demostrates the more leadg bees the greater search capacty, however, some relevat research [9] proved that the amout of calculato every terato wll crease wth SN growg, ad so SN growg wll crease
2148 JOURNAL OF NETWORKS, VOL. 8, NO. 9, SEPTEMBER 2013 covergece tme, the slow dow search speed some complex optmal problems. Whe the matchg odes umber respectvely rose to 11 ad 17, SN set for 20, ad other parameters costat. Smulato for 30 tmes each settg. Success rate of obtag the optmal soluto s show as follows Fgure 6 shows that the success rate of composto optmzg decreases wth the crease of the matchg odes, because of "curse of dmesoalty" problem. Guarateeg success rate of optmzg over 60% the premse, mproved ABC could fsh composto optmzg task cludg 40 matchg odes o persoal computer (Itel core 5-2400 CPU, 3.1 GHz, 2.0 GB RAM). To prove mproved ABC s superorty performace, usg stadard ABC do the same smulatos. Performace comparso s show Fgure 7 ad 8. model solvg. Fally, comparg smulato results proved the effectveess ad feasblty of the model ad mproved algorthm. However, the composto optmzg model s ot comprehesve ad eeds be further mproved, whle the mproved ABC also eeds ehace performace, what wll be the focus of further research. ACKNOWLEDGMEN The authors apprecate the revewers for ther extesve ad formatve commets for the mprovemet of ths mauscrpt. At the same tme, the authors apprecate the team of Servces Securty the key laboratory of the academy of Equpmet. They provded valuable advce that sgfcatly helped mprove ths paper. REFERENCES Fgure 7. Comparso of average covergece terato Fgure 8. Comparso of optmzg success rate Fgure 7 ad 8 show that mproved ABC crease success rate ad ehace searchg effcecy, whch demostrate the mproved algorthm has more perfect performace tha stadard oe. VI. CONCLUSION I ths paper, QoS propertes quattatve models are bult based o prevous studes, ad a mathematcal model of Web servce composto optmzg problem was preseted. The artfcal bee coloy algorthm (ABC) s mproved by mposg taboo strategy ad chaos, ad the appled to Web servce composto optmzg [1] Logchag Zhag, Hua Zou ad Fagchu Yag. A Dyamc Web Servce Composto Algorthm Based o TOPSIS. Joural of etworks, 2011, 6(9) pp. 1294-1304. [2] Zeg LZ, Beatallah B, Ngu AHH. QoS-Aware mddleware for Web servces composto. IEEE Tras. o Software Egeerg, 2004, 30(5) pp. 311-327 [3] Fa Gusheg, LIU Dog, Che Lqog. A coordato strategy for relable servce composto ad ts aalyss. Chese Joural of Computers, 2008, 31 (8) pp. l445-1457. [4] T Sheghu Zhao, Guox Wu, Gul Che, Habao Che. Reputato-aware Servce Selecto based o QoS Smlarty. Joural of etworks, 2011, 6(7) pp. 950-957. [5] Chaxue Xa, Ma Xuese, Zhou Le, Tag Hao. Approach to Web servce composto optmzato based o SMD model. Hefe Uversty of Techology (Natural Scece), 2011, 34 (10) pp. 1496-1500. [6] Pg Wag. QoS-aware web servces selecto wth tutostc fuzzy set uder cosumer s vague percepto. Expert Systems wth Applcatos, 2009, 36(3) pp. 4460-4466. [7] Zb Zheg, Hao Ma, Mchael R. Lyu, Irw Kg. QoS- Aware Web Servce Recommedato by Collaboratve Flterg. Proc. IEEE Trasactos O Servces Computg, 2011:140-152. [8] FAN Xao-Q, JIANG Chag-Ju, WANG Ju-L. Radom-QoS-Aware Relable Web Servce Composto. Joural of Software. 2009, 20(3) pp. 546-556. [9] Xa Yame, Cheg Bo, Che Julag, Megxag Wu, Lu Dog. Optmzg servces composto based o mproved at coloy algorthm. Chese Joural of Computers, 2012, 35 (2) pp. 270-281. [10] Zhag Yg, Lu Xaomg, Wag Zhxue, Che L. QoSoreted servce composto based o mappg relato tree. J. Cet. South Uv. 2012 (19) pp. 2194-2202. [11] Su S, L F, Yag F C. Iteratve selecto algorthm for servce composto dstrbuted evromets. Scece Cha seres F-formato Scece, 2008, 51(11) pp. 1841-1856. [12] Xu B, Luo Se, Ya Yx. Effcet composto of sematc Web servceswth ed toed QoS optmzato. Tsghua Scece & Techology, 2010,15 (6) pp. 678-686. [13] Lu Feg, Le Zhemg. Research o user-awear QoS based Webservces composto. The Joural of Cha Uverctes of Posts ad Telecommucatos, 2009, 16(6) pp. 125-130.
JOURNAL OF NETWORKS, VOL. 8, NO. 9, SEPTEMBER 2013 2149 [14] Che Lag, Su M. Web servces composto method based o mmue geetc algorthm. Computer Egeerg, 2010, 36 (10) pp. 226-228. [15] Lu B, Zhag Re. Web servces composto method based o QoS by multple obectve optmzato. Computer Egeerg ad Desg, 2012 33 (3) pp. 885-889. [16] Yag Yahog, Wu Gulg, Che Japg,at al. multobectve optmzato based o at coloy optmzato grd over optcal burst swtchg etworks. Experts Systems wth Applcatos,2010, 37 (2) pp. 1769-1775 [17] Fe Tao, Dogmg Zhao, Hu Yefa, Zude Zhou. Correlato-aware resource servce composto ad optmal-selecto maufactu rg grd. Europea Joural of Operatoal Research, 2010, 201(1) pp. 129-143. [18] Z. Zheg ad M.R. Lyu, Collaboratve Relablty Predcto for Servce-Oreted Systems Proc. IEEE/ACM 32d It l Cof. Software Eg. (ICSE 10), 2010. [19] Cardoso J, Sheth A, Mller J, e a1. Qualty of servce for workflow ad Web servce processes. Web Sematcs: Scece, Servces ad Agets o the World Wde Web, 2004, 1 (3) pp. 281-308. [20] D. Karaboga, B. Basturk. A powerful ad effcet algorthm for umercal fucto optmzato: artfcal bee coloy (ABC) algorthm. Joural of Global Optmzato. 2003, vol.39: 459 471. [21] D. Karaboga. A dea based o hoey bee swam for umercal optmzato Techcal Report-TR06. Kayser Ercyes Uversty Egeerg Faculty Computer Egeerg Departmet 2005. [22] D. Karaboga, B. Basturk. Artfcal bee coloy (ABC) optmzato algorthm for solvg costraed optmzato problems. Lecture Notes Artfcal Itellgece 4529, Sprger-Verlag, Berl, 2007. [23] Al R, Yldz. A ew hybrd artfcal bee coloy algorthm for robust optmal desg ad maufacturg. Appled Soft Computg, 2012, 1569-1575. [24] L we. Research o hybrd optmzato algorthm based o chaos. Chagsha: Cetral South Uversty master degree thess, 2004. [25] Lag Che, Peg Zou, Lyu Hao, Roghua Yu. Researchg o optmze embattle of radar etwork based o mproved artfcal bee coloy algorthm. Proceedgs of 2011 Asa-Pacfc Youth Coferece o Commucato, 2011 pp. 269-273. Ju He receved the Bachelor degree Computer Scece ad Techology from Natoal Defese Scece ad Techology Uversty, Chagsha, Cha, 1982. He s a seor egeer ad professor etwork ad formato securty. Now he works the key laboratory of The Academy of equpmet, Beg, Cha. I 30-year research work, he publshed a moograph ad 30 papers key ourals, wo several scetfc ad techologcal progress awards. Hs research terests clude Web servce, etwork ad formato securty, software desg ad egeerg. Lag Che receved the Bachelor degree Electroc ad Iformato Egeerg from Beg Uversty of Aeroautcs ad Astroautcs ad the M.S. degree Systems Scece from The Academy of equpmet, Beg, Cha. He s curretly workg toward the PhD degree the key laboratory, the Academy of equpmet. Hs research terests clude SOA, servce securty, tellget optmzato ad servce composto optmzato. Xaolog Wag receved the Bachelor degree Computer Scece ad Techology from Tsghua Uversty, Beg, Cha. 2011. Now he s studyg for M.S. degree Systems Scece The Academy of equpmet, Beg, Cha. Hs research terests clude servce composto optmzato ad tellget algorthm. Yoggag L receved the M.S. degree Computatoal mathematcs ad ts applcato software from Northwester Uversty, X a, Cha. Now he s a seor egeer ad works Departmet of Cha satellte martme trackg ad cotrol, Jagy, Cha. Hs research terests clude computatoal mathematcs ad ts applcato software, computer software ad theory.