Research Article A New Manufacturing Service Selection and Composition Method Using Improved Flower Pollination Algorithm

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1 Mathematcal Problems n Engneerng Volume 2016, Artcle ID , 12 pages Research Artcle A New Manufacturng Servce Selecton and Composton Method Usng Improved Flower Pollnaton Algorthm Wenyu Zhang, Yushu Yang, Shua Zhang, Dejan Yu, and Yangbng Xu School of Informaton, Zhejang Unversty of Fnance and Economcs, Hangzhou , Chna Correspondence should be addressed to Shua Zhang; zs760914@sna.com Receved 12 August 2016; Revsed 16 November 2016; Accepted 22 November 2016 Academc Edtor: Eusebo Valero Copyrght 2016 Wenyu Zhang et al. Ths s an open access artcle dstrbuted under the Creatve Commons Attrbuton Lcense, whch permts unrestrcted use, dstrbuton, and reproducton n any medum, provded the orgnal work s properly cted. Wth an ncreasng number of manufacturng servces, the means by whch to select and compose these manufacturng servces have become a challengng problem. It can be regarded as a multobjectve optmzaton problem that nvolves a varety of conflctng qualty of servce (QoS) attrbutes. In ths study, a multobjectve optmzaton model of manufacturng servce composton s presented that s based on QoS and an envronmental ndex. Next, the skylne operator s appled to reduce the soluton space. And then a new method called mproved Flower Pollnaton Algorthm () s proposed for solvng the problem of manufacturng servce selecton and composton. The mproved enhances the performance of basc by combnng the latter wth crossover and mutaton operators of the Dfferental Evoluton () algorthm. Fnally, a case study s conducted to compare the proposed method wth other evolutonary algorthms, ncludng the Genetc Algorthm,, basc, and extended. The expermental results reveal that the proposed method performs best at solvng the problem of manufacturng servce selecton and composton. 1. Introducton Wth the rapd development of the Internet and assocated nformaton technology, many tradtonal manufacturng ndustres have changed the ways by whch they provde products and servces. Supplers ncreasngly encapsulate ther producton capactes nto servces and upload them to shared network platforms. In general, user requrements are extremely complex and cannot be satsfed by a sngle manufacturng servce. Therefore, to meet such ncreasngly complex requrements, approprate canddate servces must be chosen from a servce set for each subtask and combned to form a composte servce. The optmal composte manufacturngservcesthendentfedfromallthepossble solutons and used to perform the multobjectve task [1]. As an ncreasng number of manufacturng servces become avalable, the manner n whch to select and compose them approprately s a challengng problem. Ths s referred to as a multobjectve manufacturng grd resource servce composton and optmal-selecton problem [1]. As for the background of manufacturng servce selecton and composton, the concept of cloud manufacturng, as a new manufacturng paradgm, was proposed by Zhang et al. [2] and has attracted consderable attenton from experts and scholars n the feld of manufacturng servce. Tao et al. [3] presented the classfcaton of manufacturng resource and servce and proposed a fve-layered structure (.e., resource layer, percepton layer, network layer, servce layer, and applcaton layer) toward cloud manufacturng. Wth the rapd ncrease n the number of web servces havngthesamefunctonalattrbutes,selectnganappropratecanddateservcebasedonnonfunctonalattrbutes such as qualty of servce (QoS) has become an equally challengngtaskthathasalsoattractedconsderableattenton [4]. As wth web servces, each manufacturng servce has functonal and nonfunctonal attrbutes [5]. Regardng nonfunctonal attrbutes, QoS has become crucal n evaluatng the overall performance of manufacturng servce composton. Tme, cost, relablty, and avalablty are four representatve QoS propertes that are frequently employed by researchers. Tao et al. [6] presented a QoS descrpton supportng resource servce correlaton. They also descrbed the basc resource servce composte modes (RSCM) for composte resource servce and the methods to translate a complcated RSCM nto a smple sequence RSCM.

2 2 Mathematcal Problems n Engneerng However, t s worth notng that, unlke typcal web servce composton, addtonal restrctons on manufacturng servce composton exst [7]. For example, manufacturng enterprses belong to the entty enterprses that often produce consderable amount of contamnaton n the course of provdng servces. Therefore, from the perspectve of green manufacturng [7], an envronmental ndex (.e., carbon emsson) must be factored nto the evaluaton system to measure the overall performance of a manufacturng servce composton. In addton, as the number of canddate servces ncreases, the soluton space becomes so large that fndng the optmal soluton s dffcult. Thus, some researchers have proposed usng the skylne operator [8] for servce composton andhaveadvancedtheconceptofskylneservce[9,10]to lmt the sze of the soluton space and mprove the combned utlty of composte solutons. Even after applyng of the skylne operator, selectng an optmal composte soluton remans tme-consumng. Although many evolutonary algorthms, lke Genetc Algorthm (), Partcle Swarm Optmzaton (PSO) algorthm, and Dfferental Evoluton () algorthm, have been proposed to solve ths problem, room remans for mprovement. The Flower Pollnaton Algorthm () was frst proposed by Yang [11] n 2012 as a new metaheurstc algorthm. It has been appled to many felds and has been shown to perform well, especally at solvng optmzaton problems. In ths study, a novel and effcent method s proposed that employs not only the skylne operator but also the mproved (I) to solve the problem of QoS-based manufacturng servce selecton and composton. The use of the skylne operator means that only those servces that belong to the skylne servce set are vald canddate servces [9]. In ths study, the dfferences between our proposed I and basc are the followng three ponts. Frst, a groupng strategy and elte replacement strategy are used to mprove the convergence speed of. Second, we not only use the adaptve parameter to adjust dynamcally the swtch probablty between global and local pollnaton but also employ an adaptve scalng factor to control the step sze of the global search. Thrd, we ntroduce crossover and mutaton operators of the algorthm nto the local search of to mprove ts performance. The rest of ths paper s organzed as follows. Related studes about manufacturng servce composton and the development of relevant evolutonary algorthms are dscussed n Secton 2. Secton 3 provdes a descrpton of the model that s establshed n ths study and defntons of skylne servce. The basc and ts mprovements arentroducednsecton4.secton5offersacasestudy and several comparatve experments used to llustrate the practcalty and effectveness of the proposed method. A concluson that outlnes the authors man contrbutons and that descrbes future research s provded n Secton 6. of bg data, the problem of optmzng composte servces s causng ncreasng concern n the manufacturng ndustry. A composte servce s the result of aggregatng multple servces nto one, and ts am s to execute functons of greater complexty than those that can be performed by a sngle servce [12]. To evaluate the overall performance of servce composton, Zeng et al. [13] set QoS attrbutes as the man measurement, ncludng executon prce, executon tme, reputaton, relablty, and avalablty, whch were wdely accepted and appled. To obtan hgh qualty of servce composton, they also ntroduced a mddleware platform to express user satsfacton as utlty functons. Tao et al. [14] presented dfferent aggregaton formulatons for four dfferent structures of servce composton, ncludng sequence, parallel, selectve and cycle. In addton, the correlaton of servce composton was taken nto consderaton. To mnmze mplementaton tme and cost and to maxmze relablty, an mproved Partcle Swarm Optmzaton (PSO) algorthm was ntroduced as the key technology. Consderng the heterogeneous QoS values and homogeneous resources n manufacturng servce, Zheng et al. [15] took the dynamc customer requrements nto account by ntroducng the fuzzy theory and presented the way of calculatng fuzzy QoS value and selectng the best servces consderng desgn preference. Hao et al. [16] ponted out that, n order to obtan the best servce composton soluton, user QoS constrants need to be relaxed rather than overlooked, so they proposed a novel QoS model and computatonal framework. Tao et al. [17] made an overvew about manufacturng servce management and analyzed the future research drectons of manufacturng servce composton and optmzaton. However, most of the aforementoned studes only focus on the general QoS attrbutes and gnore the mportance of envronmental protecton, whch has become one of the most mportant attrbutes n the process of selectng manufacturng servce [7, 17]. Therefore, an envronmental ndex such as carbon emsson s added n ths paper as a soft constrant to measure the overall performance of manufacturng servce composton. As a multobjectve optmzaton problem, ts soluton space s very large and the objectves of manufacturng servce composton are n conflct wth each other. Thus, Alrfa et al. [18] frst ntroduced the skylne operator [8] nto servce composton to reduce the number of canddate servces. They also proposed the defnton of skylne servce to mprove the effcency of servce selecton. Skylne servce has beenwdelyusedbyscholarsandbecomeanactveresearch area [10, 12]. In vew of ts good performance, the proposed method n ths paper ncorporates the skylne servce concept n the model to lmt the sze of the soluton space and mprove the overall qualty of the optmal manufacturng servce composton soluton. 2. Related Work 2.1. Manufacturng Servce Selecton and Composton. Wth the rapd development of cloud computng and the advent 2.2. The Development of Evolutonary Algorthms n Manufacturng Servce Composton. In order to solve the problem of servce composton, experts and scholars have used a varety of evolutonary algorthms. For example, n the authors

3 Mathematcal Problems n Engneerng 3 prevous work [19], the extended was employed to solve the problem of manufacturng resource composton, treatng the overall utlty of multple objectves as the metrc of the composte soluton. L et al. [4] employed a multpopulaton to solve the problem of QoS-orented web servce composton for the Internet of Thngs. Tao et al. [1] presented the concept of multobjectve manufacturng grd resource servce composton and optmzaton problem and used the PSO algorthm to solve t. They not only ntroduced the basc resource servce composte modes (RSCM) and the prncples to translate a complcated RSCM nto a smple sequence but also used the extended PSO algorthm to mprove the qualty of the optmal soluton. Pop et al. [20] ntroduced a modfed algorthm wth a new genome-encodng method to solvetheproblemofservcecomposton.weandlu[21] ntroduced the Ant Colony Optmzaton (ACO) algorthm nto the cloud manufacturng resource allocaton model. Wang et al. [22] proposed a new optmzaton algorthm called the culture max-mn ant system, whch not only establshed a novel comprehensve model ncludng both generc and doman QoS but also combned the Culture Algorthm (CA) and the ACO algorthm nto a new approach. Lartgauetal.[7]appledanadaptedArtfcalBeeColony (ABC) optmzaton algorthm to the cloud manufacturng servce composton model, whch not only ncluded QoS attrbutes but also took the geoperspectve transportaton nto consderaton. Although many conventonal evolutonary algorthms have been used n servce composton, each has ts own defcences, for example, convergng too easly to local optmum or premature convergence. In order to obtan better optmzaton solutons, a varety of emergng algorthms have been proposed. For example, nspred by the bologcal process of flower pollnaton, Yang [11] frst proposed n2012andprovedthattwasmoreeffcentnsolvng optmzaton problems than both the and PSO. As a novel and effectve metaheurstc algorthm, has been used n many felds. Abdelazz and Al [23] used t n a multmachne power system to seek the optmal parameters of statc VAR compensator dampng controller. Abdelazz et al. [24] also used to solve the problem of economc load dspatch and combned economc emsson dspatch problems. To solve szng optmzaton problem of truss structures, Bekdaş et al. [25] used to mnmze the weght of truss structures. To obtan feasble optmzed desgns, they added an teratve constrant handlng strategy nto the search engne. El-henawy and Ismal [26] extended the basc by combnng t wth chaotc search to solve large nteger programmng problems. Wang et al. [27] appled wth bee pollnator n the feld of cluster analyss to make up for defcences n the k-means method. By comparng t wth other algorthms such as, they proved the better accuracy andhgherlevelofstabltyoftherproposedmethod.in order to solve the problem of multlevel mage thresholdng, Ouadfel and Taleb-Ahmed [28] appled to search for thebestvaluesfromagvennumberofthresholdswththe purpose of maxmzng the objectve functon. To apply the to solve the antenna postonng problem, Dah et al. [29] proposed four bnary varants of the by applyng the prncpal mappng technques. In addton, to study the applcablty of ths new metaheurstc algorthm n realworld applcatons, Draa [30] revewed the performance of n terms of both qualtatve and quanttatve aspects, andthenhesummarzedthedefectsofthebascby comparng t wth state-of-the-art algorthms and ponted out the extenson and applcaton drectons of. However, even though research s expandng, to the best of our knowledge, t has seldom been used to solve the optmzaton problem of servce composton, let alone be appled n the feld of manufacturng servce selecton and composton. Therefore, n ths study, s appled to solve the manufacturng servce selecton and composton problem by mprovng the standard n combnaton wth. The expermental results show that the proposed I canobtanbetteroptmalsolutonsthanotheralgorthms such as [4], [31], basc [11], and even extended () [32]. 3. Model of Manufacturng Servce Composton Problem 3.1. Notatons n Manufacturng Servce Composton Model. To solve the manufacturng servce composton problem and translatetntoamathematcalmodel,weusethefollowng notatons to better llustrate the model of manufacturng servce composton: T: executon tme of the canddate servce C: executon cost of the canddate servce Ava: avalablty of the canddate servce Rel: relablty of the canddate servce m: the number of subtasks n each basc structure MS : the selected manufacturng servce realzng th subtask, =1,2,...,m T(seq): the total executon tme n the sequence structure C(seq): the total executon cost n the sequence structure Ava(seq): the total avalablty n the sequence structure Rel(seq): the total relablty n the sequence structure Ec(MS ):theprocessedcarbonemssonvalueofth manufacturng servce Ec : a soft constrant about carbon emsson that s provded by users θ :realcarbonemssonvalueofth canddate servce The Calculaton of QoS Propertes. In ths study, the four representatve QoS propertes of executon cost (C), executon tme (T),relablty(Rel),andavalablty(Ava) are employed as crtera for manufacturng servce performance evaluaton [13, 33]. As there are varous metrcs for dfferent QoS attrbutes, n order to ensure the accuracy of assessment results, all the

4 4 Mathematcal Problems n Engneerng QoS attrbutes need to be normalzed and converted nto the same order of magntude before they are employed n the fnal QoS aggregaton formula. It has been well recognzed that servce qualty s negatvely correlated wth cost and tme, but t has a postve correlaton wth relablty and avalablty. Therefore, each QoS attrbute s classfed as beng ether postve attrbute or negatve attrbute. The correspondng calculaton formulas are as follows [5]: q q mn q nor = { f q { q max q max =q mn mn { 1 f q max =q mn q nor = { q max q f q { q max q max mn { 1 f q max =q mn =q mn postve attrbute, negatve attrbute. Here, q s kth true attrbute value of a manufacturng servce and q nor represents the correspondng normalzed value. q max represents the maxmal value of kth QoS attrbute for manufacturng servces n the same canddate servce set, that s, q max = max{q 1,q 2,...}; q mn stands for the mnmal value of kth QoS attrbute for manufacturng servce, that s, q mn = mn{q 1,q 2,...}. In general, manufacturng servce composton ncludes four basc structures: sequence, parallel, selectve, and cycle structures. There are dfferent aggregaton methods for each structure. The aggregaton formulas are defned n [13]. For example, the followng are the calculaton formulas for the sequence structure: T(seq) = C(seq) = Rel (seq) = Ava (seq) = m =1 m =1 m =1 m =1 T(MS ), C(MS ), Rel (MS ), Ava (MS ). In order to use a unfed standard to measure the overall performance of manufacturng servce composton, the multple objectves need to be transformed nto a sngle one by assgnng dfferent weghts to each attrbute accordng to the dfferent preferences of users. The fnal QoS evaluaton valueofmanufacturngservcecompostoncanbecalculated accordng to the followng equaton: f (QoS) =w 1 T+w 2 C+w 3 Rel +w 4 Ava, (4) where w 1, w 2, w 3,andw 4 sumto1andrepresenttherespectve weghts of the four attrbutes, ncludng total tme (T), total cost (C), total relablty (Rel), and total avalablty (Ava), of manufacturng servce composton. (1) (2) (3) 3.3. The Calculaton of Envronmental Index. In addton to the basc QoS ndcators, the proposed model contans an envronmental ndcator. Customers generally consder the QoS performance of manufacturng servce composton to be more mportant than ts envronmental performance, and therefore they have dfferent expectatons of them. For that reason, the constrants proposed by customers can be dvded nto two types: rgd constrants and soft constrants. QoS constrants are defned as beng rgd ones that must be satsfed, whereas envronmental ndces are defned as soft constrants that do not have to be satsfed strctly [22]. In the present paper, the authors regard carbon emsson as an envronmental protecton ndex. As wth manufacturng tasks, customers mpose ther own soft constrants that should be satsfed as far as possble. For each canddate manufacturng servce MS, the devaton value between ts carbon emsson and soft constrant s calculated accordng to the followng equaton [22]: Ec (MS )= θ Ec Ec β, β= { 0, f θ satsfy Ec { 1, f θ { do not satsfy Ec, where Ec(MS ) s the devaton carbon emsson value, θ s the real value (.e., carbon emsson value) provded by supplers, and Ec s a soft constrant about carbon emsson that s provded by users. From the above equaton, t s clear that β s a bnary ndex. If carbon emsson meets the user requrement for envronmental protecton, then β=0; otherwse, β = 1. It s well known that the smaller the devaton value, the hgher the envronmental ndcator. Thus, the devaton value s a negatve attrbute, and ts standardzed formula s defned n (2) too. The aggregaton formulas of devaton value about carbon emsson are smlar to the aggregaton formula of executon cost, so we omtted them here The Comprehensve Utlty of a Manufacturng Servce Composton. Based on the above descrpton, the fnal objectve functon of manufacturng servce composton s defned as (5) max F (MSC) =λ 1 f (QoS) +λ 2 f (Ec), (6) where F(MSC) s the comprehensve utlty of a manufacturng servce composton, f(qos) represents the fnal QoS evaluaton, and f(ec) represents the fnal carbon emsson valueofmanufacturngservcecomposton.termsλ 1 and λ 2 sum to 1 and represent the weghts of QoS and envronmental ndex n a manufacturng servce composton soluton, respectvely. In ths model, the goal s to obtan the maxmum value of the comprehensve utlty, and ths comprehensve utlty be regarded as the ftness value n the experment secton of ths paper.

5 Mathematcal Problems n Engneerng The Introducton of Skylne Servce. Before the new evolutonary algorthm s carred out to solve the problem of manufacturng servce composton, the skylne servce s ntroduced to reduce the number of canddate manufacturng servcesandmprovethequaltyofoptmalsoluton.the concept of skylne servce, whch was based on domnance relatonshp, was frst proposed by Alrfa et al. [18]. The followng s a detaled descrpton of the related defntons [9]. Defnton 1 (domnance). One assumes that there are two vectors X 1 =(x 11,x 12,...,x 1d ) and X 2 =(x 21,x 22,...,x 2d ) n d-dmensonal space. If the followng two condtons are satsfed, one says that X 1 domnates X 2. Frstly, each dmenson n X 1 sgreaterthanorequaltothecorrespondng n X 2. Secondly, at least one dmenson n X 1 s strctly greater than the correspondng n X 2.Thetwocondtonscanbe expressed mathematcally as follows [34]: f {1,2,...,d}, x 1 x 2, (7) j {1,2,...,d}, x 1 >x 2. Otherwse, f ether of the two condtons s not satsfed, then X 1 does not domnate X 2. Defnton 2 (skylne servce). For two manufacturng servces MS 1 and MS 2,falltheQoSattrbutesofMS 1 are better than or equal to the correspondng ones of MS 2,and at least one attrbute of MS 1 performs strctly better than the correspondng attrbute of MS 2,thenMS 1 domnates MS 2, whch s denoted as MS 1 MS 2 [34]. That s to say, MS 1 s better than MS 2. A skylne servce refers to such a servce that s not domnated by any other servces. The applcaton of skylne operator can lmt the sze of the soluton space of manufacturng servce composton because the process of choosng skylne servce elmnates those other servces that could not possbly be selected as the fnal manufacturng servce composton. 4. Improved Flower Pollnaton Algorthm 4.1. Introducton to the Standard. s a novel bonc evolutonary algorthm that was frst proposed by Yang n 2012 [11]. The nspraton for comes from the natural pollnaton process that takes place n flowerng plants. In, each flower stands for a feasble soluton and the objectve functon value n Secton 3.3 s regarded as the ftness value. To mmc the pollnaton process, there are two dfferent pollnaton methods for each flower to choose: global or local pollnaton wth swtch probablty p. The specfc process s descrbed as follows. For each ndvdual, there s a random number rand. If rand <p, then global pollnaton should be carred out. In the global pollnaton process, each flower updates ts poston accordng to the followng equaton [11]: X t+1 =X t +γl(λ) (Xt best Xt ), (8) where X t and Xt+1 are the old and new postons of th flower, respectvely, X t best s the best flower at current teraton t, whch has the best ftness value n the whole populaton, and γ s the scalng factor that controls the step sze of global pollnaton. Parameter L, the Lévy flght, s used as the strength of pollnaton n the basc, and the step sze L(λ) obeys the Lévy dstrbuton: λγ (λ) sn (πλ/2) 1 L π s, (s s 1+λ 0 >0), (9) where Γ(λ) s the standard gamma functon and λ s from 0.3 to 1.99 [35]. In the basc, Yang [11] set the dstrbuton factor λ as 1.5. Ths dstrbuton s vald when large step s s bgger than 0. If rand > p, the local pollnaton process should be carred out. Flower X t obtans ts new poston X t+1 accordng to the dfference between ts old poston and the poston of two neghborng flowers X t p and Xt q.thsprocess s consdered as local search, and the updatng equaton [11] s defned as X t+1 =X t +r(xt p Xt q ), (10) where r sdrawnfromaunformdstrbuton[0, 1],andts consdered as a local random walk. After pollnaton s completed, the new ndvduals update ther postons by comparng ftness values. If the ftness of X t+1 s better than that of X t,thenewpostonofth.otherwse,th flower remans flower wll be replaced by X t+1 at X t Improvements of I Compared wth the Standard. Lke many other evolutonary algorthms, basc suffers from premature convergence and can easly fall nto local optmum. In order to overcome these shortcomngs, the authors propose an I to extend the standard n three aspects. Frstly, the whole populaton s dvded nto dfferent groups on average, and an elte replacement strategy s ntroduced to replace the worst ndvdual n each group. Secondly, the swtch probablty p and scalng parameter γ are adapted dynamcally wth the teratve process rather than remanng fxed lke the basc. Ths modfcaton ncreases not only the global search ablty but also the local search ablty of basc. Thrdly, n order to enhance ts localsearchabltyandobtanbetteroptmalsoluton,the localsearchmethodscombnedwththemutatonand crossover operators of Groupng Strategy and Elte Replacement Strategy. In the orgnal, flowers search for the optmal soluton n a sngle populaton only. Ths method may take a relatvely long tme and ts global search ablty s nsuffcent. Based on ths dea and nspred by [32], the authors ntroduce a parallel groupng strategy to solve ths problem effectvely. Before startng the teraton, the whole populaton s dvded nto three dfferent groups on average accordng to ther ftness values. The detaled operaton steps are descrbed as follows. Frstly, the ftness s calculated and ranked. Secondly, the whole populaton s dvded nto three groups on average

6 6 Mathematcal Problems n Engneerng accordng to ther ftness. The frst group conssts of ndvduals wth the best ftness, the thrd group conssts of ndvduals wth the worst ftness, and the remanng ndvduals belong tothesecondgroup.theteratveprocessscarredoutn each group concurrently. In addton, n order to mprove the convergence and the optmal soluton qualty, an elte replacement strategy s ntroduced. From each group, the best ndvdual s selected asaneltendvdual.thatstosay,afterglobalandlocal pollnaton, the worst ndvdual s replaced wth the elte ndvdual n each group. If there are duplcate ndvduals, then the duplcate one can be modfed by selectng one dmenson randomly to carry out mutaton before startng the next teraton. In each group, s carred out concurrently n the same way. Before executng the next generaton, these groups are combned nto a sngle populaton Adaptve Swtch Probablty and Scalng Factor. In standard, Yang [11] set the swtch probablty p as 0.8. However, a fxed swtch probablty s nsuffcent. In order to retan a balance between global and local pollnaton n, the swtch probablty s generated dynamcally accordng to the ftness value of the current generaton, whch s represented by the objectve functon. The adaptve swtch probablty s obtaned accordng to the followng equaton: p=e (max f mn f)/ max f, (11) where max f and mn f are the maxmum and mnmum ftness values, respectvely, of an ndvdual n the current teraton. For the same reason, n order to escape local optmum and avod premature convergence n global pollnaton, the scalng factor γ s also generated dynamcally accordng to the number of teratons. The calculaton of scalng factor depends on the followng formula: max ter t γ= , (12) max ter where max ter represents the maxmum teratons and t s the current teratons. Accordng to ths formula, the value of scalng factor γ ranges from 0.2 to 0.5, and ts value decreases wth the ncrease of teratons Modfed Local Pollnaton Process by Combnng wth Algorthm. In the local pollnaton process of X,the ndvduals X p and X q are selected randomly except for the rule that X p and X q should dffer from the objectve ndvdual X. However, ths method s not suffcently farsghted and there s no drecton of evoluton to follow, whch may lead the algorthm to fall nto local convergence. In order to avod ths, the local search method s modfed by combnng t wth. The detaled steps are as follows. Frstly, the mutaton operator [31] s appled n local search. For X t,anewmutatedndvdualvt wll be generated accordng to the followng equaton: V t =X t best +r(xt p Xt q ), (13) where X t best s the best ndvdual n current generaton t n the whole populaton, X t p and Xt q are dfferent from Xt and belong to the same group wth X,andrs a random number that s drawn from a unform dstrbuton [0, 1]. Secondly, the crossover operator [31] s also ntroduced after mutaton. The two-pont crossover operator can be carred out as: X t+1 V t,j,,j = { { X { t,j, f l j h else =1,2,...,n; j=1,2,...,d. (14) Here, X,j s jth dmenson of th ndvdual X ; l and h are generated randomly from [1, d]. ThemutatedndvdualV obtaned by mutaton operator changes ts locaton accordng the above equaton. Ths modfcaton not only ncreases the local search ablty but also mproves the populaton dversty to avod local optmal. The pseudocode of mproved s summarzed n Pseudocode RepresentatonSchemeofInMultobjectveManufacturng Servce Composton. In order to solve the problem of multobjectve manufacturng servce composton wth I, the representaton scheme should be solved frst. In ths paper, populaton ndvduals are referred to as flowers. Each flower represents one possble soluton of manufacturng servce composton; t has N dmensons that stand for the number of subtasks. We use real encodng n ths manuscrpt, wth all numbers as ntegers. For example, a flower X = (3, 4, 2, 5, 1, 7) that contans sx dmensons represents a manufacturng servce composton soluton that comprses sx subtasks. The frst dmenson (value = 3) nstructs the frst subtask to select ts thrd canddate servce. The second dmenson (value = 4) nstructs the secondsubtasktoselecttsfourthcanddateservceand so on for the remanng dmensons. In ths paper, we use manufacturng servce selecton (MSS) to represent the canddate servce n each servce set, and MSS nm stands for nth subtask selectng mthcanddateservce.thus,the correspondng composton soluton wth ths pollen can be expressed as (MSS 13, MSS 24, MSS 32, MSS 45, MSS 51, MSS 67 ) The Procedure of Usng I for Solvng Multobjectve Manufacturng Servce Composton Problems. The whole procedure of the proposed algorthm s descrbed as follows. Step 1 (selectng skylne servces). Frstly, specfc user requrements are dentfed, ncludng constrant condtons and preferences. Secondly, skylne servces are selected accordng to the aforementoned defntons n Secton 3.4 before I s carred out. Step 2 (ntalzng). Frstly, the parameters of the proposed method are set, ncludng ntal populaton sze, maxmum teratons, and number of groups. In addton, λ = 1.5 s set n the Lévy flght. Secondly, an orgnal populaton from the skylne servce set s ntalzed.

7 Mathematcal Problems n Engneerng 7 (1) Intalze (t = 0) Objectve max f(x) Set parameters: populaton sze N, maxmum teratons max ter and λ Randomly generate an ntal populaton of N flowers X(t) = (X t 1,Xt 2,...,Xt N ) (2) Repeat (t = 1 to max ter) whle (t < max ter) Fnd the best ndvdual X best n current populaton Dvde the whole populaton nto three grous (max f mn f)/ max f Defne a swtch probablty p va p=e for =1:N f rand <p Generate a step sze L(λ) Defne a scalng factor γ va γ=((max ter t)/ max ter) Generate a step sze L that obeys a Levy dstrbuton Do global pollnaton va X t+1 =X t + γl(λ)(xt best Xt ) else Do local pollnaton va X t+1 =X t +r(xt p Xt q ) Execute crossover operaton and fnd out X t+1 end f Evaluate new ndvdual If new ndvdual s better, update ts locaton end for Carry out elte replacement strategy Combne these three groups nto one populaton Update the best ndvdual X best end whle (3) Output Output the best ndvdual X best Pseudocode 1: Pseudocode of mproved. Step 3 (groupng strategy). Frstly, the ftness of each ndvdual s calculated and ranked, and the populaton s dvded nto three groups accordng to Secton Then, the global optmal ndvdual X best andtheeltendvdualneachgroup are selected. The teratve process s carred out n all groups concurrently. Step 4 (dentfyng the swtch probablty). For each generaton, ts swtch probablty p s calculated accordng to (11). A random number rand for each ndvdual s generated and then compared wth p to determne the pollnaton method. Step 5 (global pollnaton). If rand < p, then global pollnaton s chosen. The correspondng calculatons are gvenn(8),(9),and(12). Step 6 (local pollnaton). If rand >p, then local pollnaton s chosen. The correspondng calculatons are gven n (13) and (14). Step 7 (updatng locatons). After completon of global and local pollnaton, the locaton of each ndvdual s updated. If f(x t+1 )>f(x t ),thenth ndvdual updates ts locaton; otherwse, t remans at old locaton. Step 8 (elte replacement strategy). After locaton updatng s fnshed, the elte replacement strategy s employed to replace the worst ndvdual n each group accordng to Secton Step 9 (mergng). When all groups have fnshed the pollnaton process, the three groups are combned nto a sngle populaton, and then the current best soluton X best s updated. Step 10 (stoppng teraton). In general, there are two termnaton condtons for an evolutonary algorthm wth an optmzaton problem: maxmum teratons and ftness value. If ether of the condtons s satsfed, then the teraton termnates and the best soluton X best s outputted; otherwse return to Step 3 and repeat untl a termnaton condton s satsfed. Theflowchartoftheproposedmethodsshownn Fgure Expermental Smulaton and Comparson In order to verfy the practcalty and effectveness of the I, the proposed method s used to solve an llustrated problem n manufacturng servce composton. A varety of comparatve experments wth [4], [31], basc [11], and extended () [32] were carred out to llustrate the superorty of the proposed method. All the experments were carred out usng a personal computer wth Wndows 7, an Intel Core 3-GHz CPU, and 2 GB of memory. All of the algorthms were programmed n C#.

8 8 Mathematcal Problems n Engneerng Start Input user requrements and algorthm s parameters Select skylne servces Intalze a populaton of N flowers Dvde the populaton nto three groups Fnd the current best soluton X best Identfy the swtch probablty p usng (11) Yes Global pollnaton Generate a step sze L usng (9) Defne a scalng factor γ usng (12) If rand < p No Local pollnaton Carry out mutaton operator usng (13) Carry out crossover operator usng (14) Update locatons of new solutons Carry out elte replacement strategy Combne the three groups nto one populaton Fnd the current best soluton X best Is termnaton condton satsfed? End Yes Output the current best soluton X best Fgure1:Flowchartofproposedmethod Expermental Desgn. A case study of manufacturng servce composton wth sx subtasks was desgned to llustrate the practcalty and effectveness of the proposed method. Although the example s relatvely smple, t ncludes all the four structures of manufacturng servce composton, as shownnfgure2.thefnalftnessfunctonofthsmodelcan be referred to Secton 3. The selecton probabltes of subtasks 2 and 3 are 0.4 and0.6,respectvely.thecycletmeofsubtask6sfve. The weght of each QoS attrbute s set as 0.25, and the weghts of QoS and Ec are 0.6 and 0.4, respectvely. Each manufacturng servce has four QoS attrbutes (tme, cost, relablty, and avalablty) and one envronmental ndex No (carbon emsson). The maxmum teratons are 50. There are two termnaton condtons: ether t reaches the maxmum number of teratons or t converges such that the successve dfference n fnal average ftness s less than for three consecutve generatons of ndvduals [19] The Practcalty of I. In order to verfy the practcalty of I, the llustratve example shown n Secton 5.3 s assgned wth the practcal manufacturng data. The partal canddate manufacturng servce and correspondng attrbute values are shown n Table 1. The fnal constrant condtons of the whole servce composton are Total Tme < 32 hrs, Total Cost < $210, and Total Ec <3.Thereare15canddate servces n each manufacturng servce set for each subtask. As for the proposed I, the ntal populaton sze s 20. Fgure 3 records the evolutonary tracks of the proposed method, ncludng the best and average ftness values n each generaton. Here, the horzontal axs represents teraton number and the vertcal axs denotes the ftness of each generaton. As shown n Fgure 3, the I stopped at the maxmum teratons. And the optmal ftness value at the 50th teraton was 5.81; the best ndvdual was X=(7,1,7,1,2,5). Ths means that the optmal servce composton soluton s (MSS 17, MSS 21, MSS 37, MSS 41, MSS 52, MSS 65 ).Thus,tcanbe concluded that the proposed approach can fnd the relatve optmal soluton and converges acceptably well. In order to test the effcency and superorty of the proposed method, I s compared wth other algorthms, ncludng,, basc, and, to solve the same problem descrbed above. Fgure 4 shows the dfferent evolutonary tracks of these algorthms, and the orange dots represent the teratve termnaton ponts. In order to ensure the objectvty of the experment, dfferent weght combnatons are taken nto account, ncludng not only the weght of cost, tme, relablty, and avalablty but also the weght of QoS and envronment ndex. From ths fgure, two results can be obtaned. Frstly, the ftness values obtaned from the proposed method are hgher than those obtaned by the other algorthms n most cases. In other words, the proposed method can obtan better solutons even n comparson wth. Secondly, the proposed method converges faster than the other algorthms. It llustrates that the proposed algorthm has a strong search ablty and fast convergence speed.hence,tsaneffectvemethodtosolvetheproblem of manufacturng servce composton even under dfferent weght combnatons n the fnal objectve functon The Effectveness of I. In order to test the effectveness and stablty of the proposed method, the ftness values of the best manufacturng servce composton soluton n dfferent algorthms are compared by changng the values of QoS propertes and envronmental ndex of canddate servces, the number of canddate servces n each servce set, and the ntal populaton sze. In these experments, the value ranges of QoS propertes and envronmental ndex are Cost = 0 50, Tme=0 10,Rel=0.6 1,Ava=0.4 1,andθ = 0 5. The fnal constrant condtons of the whole servce composton are thesameassecton5.2.

9 Mathematcal Problems n Engneerng 9 Table 1: The partal canddate manufacturng servces and correspondng attrbute values. Subtask Canddate manufacturng servce Cost (0, 50) Tme (0, 10) Relablty (0.6, 1) Avalablty (0.4, 1) Carbon emsson (0, 5) Subtask 1 MSS MSS MSS Subtask 2 MSS MSS MSS Subtask 3 MSS MSS MSS Subtask 4 MSS MSS MSS Subtask 5 MSS MSS MSS Subtask 6 MSS MSS MSS S 0.4 Subtask 2 Subtask Subtask 3 Subtask 4 Subtask 5 Subtask 6 5 E Fgure 2: Case study of manufacturng servce composton. Ftness Best Average Iteratons Fgure 3: Evolutonary tracks of the proposed method. TheresultsshownnFgure5ndcatethatmostoftenthe proposed method obtans the best solutons wth the hghest ftness values under dfferent number of canddate servces n each servce set. In the same way, Fgure 6 shows that when the ntal populaton sze changes, the results obtaned by the proposed method are better than those of the other methods n most cases. In order to ensure the objectvty of the experment, dfferent weght combnatons are taken nto account, ncludng not only the weght of executon cost, executon tme, relablty, and avalablty but also the weght of QoS and envronment ndex. Based on all the expermental results, t s concluded that the proposed method searches better and converges faster than the other algorthms evaluated n ths paper, even under dfferent weght combnatons n the fnal objectve functon. The proposed method performs better n terms of both effectveness and effcency than the baselne algorthms of,,, and n solvng the problem of manufacturng servce selecton and composton. 6. Conclusons In ths study, a new method called I was proposed to solve the problem of manufacturng servce selecton and composton based on QoS and an envronmental ndex. The man contrbutons of ths paper are as follows: () The concept of skylne servce s ntroduced n the manufacturng servce composton model. The skylne operator can not only reduce the search space but also mprove the qualty of fnal optmal solutons. () Three mprovements were made to the basc to produce the I. Frst, a groupng strategy and elte replacement strategy were employed to mprove the convergence speed of. Second, two adaptve parameters were used to adjust the swtch probablty andcontrolthestepszeoftheglobalsearch.thrd, the mutaton and crossover operators of the algorthmwerentegratedwththelocalsearchof to mprove the performance of.

10 10 Mathematcal Problems n Engneerng Ftness Iteratons Ftness Iteratons I (a) w 1 =0.25, w 2 =0.25, w 3 =0.25, w 4 =0.25,andλ 1 =0.6, λ 2 =0.4 I (b) w 1 =0.3, w 2 =0.3, w 3 =0.2, w 4 =0.2,andλ 1 =0.7, λ 2 =0.3 Ftness Iteratons I (c) w 1 =0.4, w 2 =0.1, w 3 =0.3, w 4 =0.2,andλ 1 =0.8, λ 2 =0.2 Fgure 4: Evolutonary tracks of dfferent algorthms wth dfferent weght combnatons. Ftness Canddate servces Ftness Canddate servces I (a) w 1 =0.25, w 2 =0.25, w 3 =0.25, w 4 =0.25,andλ 1 =0.6, λ 2 =0.4 I (b) w 1 =0.3, w 2 =0.3, w 3 =0.2, w 4 =0.2,andλ 1 =0.7, λ 2 =0.3 Ftness Canddate servces I (c) w 1 =0.4, w 2 =0.1, w 3 =0.3, w 4 =0.2,andλ 1 =0.8, λ 2 =0.2 Fgure 5: Performance of dfferent algorthms under dfferent number of canddate servces wth dfferent weght combnatons.

11 Mathematcal Problems n Engneerng 11 Ftness Populaton sze Ftness Populaton sze I (a) w 1 =0.25, w 2 =0.25, w 3 =0.25, w 4 =0.25,andλ 1 =0.6, λ 2 =0.4 I (b) w 1 =0.3, w 2 =0.3, w 3 =0.2, w 4 =0.2,andλ 1 =0.7, λ 2 =0.3 Ftness Populaton sze I (c) w 1 =0.4, w 2 =0.1, w 3 =0.3, w 4 =0.2,andλ 1 =0.8, λ 2 =0.2 Fgure 6: Performance of dfferent algorthms under dfferent ntal populaton szes wth dfferent weght combnatons. In ths study, a multobjectve problem was transformed nto a sngle objectve problem. Nevertheless defcences exst that must be addressed. In a future study, the Paretooptmal method can be consdered to solve a real multobjectve problem nstead of smply transformng t nto a sngle objectve problem. In addton, the authors wll attempt to combne wth other evolutonary algorthms to mprove the performance of basc n order to solve the problem of manufacturng servce composton. Competng Interests The authors declare that there are no competng nterests regardng the publcaton of ths paper. Acknowledgments The work has been supported by Chna Natonal Natural Scence Foundaton (no and no ), Zhejang Natural Scence Foundaton of Chna (no. LY17E050010), and Humantes and Socal Scences Research Project of Mnstry of Educaton, Chna (no. 15YJC870008). References [1] F. Tao, D. Zhao, Y. Hu, and Z. Zhou, Resource servce composton and ts optmal-selecton based on partcle swarm optmzaton n manufacturng grd system, IEEE Transactons on Industral Informatcs,vol.4,no.4,pp ,2008. [2]L.Zhang,Y.Luo,F.Taoetal., Cloudmanufacturng:anew manufacturng paradgm, Enterprse Informaton Systems, vol. 8, no. 2, pp , [3]F.Tao,Y.Zuo,L.D.Xu,andL.Zhang, IoT-basedntellgent percepton and access of manufacturng resource toward cloud manufacturng, IEEE Transactons on Industral Informatcs, vol. 10, no. 2, pp , [4] Q. L, R. Dou, F. Chen, and G. Nan, A QoS-orented Web servce composton approach based on mult-populaton genetc algorthm for Internet of thngs, Internatonal Computatonal Intellgence Systems, vol.7,supplement2,pp.26 34, [5] A.F.M.Huang,C.-W.Lan,andS.J.H.Yang, AnoptmalQoSbased Web servce selecton scheme, Informaton Scences,vol. 179, no. 19, pp , [6]F.Tao,D.Zhao,H.Yefa,andZ.Zhou, Correlaton-aware resource servce composton and optmal-selecton n manufacturng grd, European Operatonal Research, vol. 201,no.1,pp ,2010. [7] J.Lartgau,X.Xu,L.Ne,andD.Zhan, Cloudmanufacturng servce composton based on QoS wth geo-perspectve transportaton usng an mproved Artfcal Bee Colony optmsaton algorthm, Internatonal Producton Research,vol.53, no.14,pp ,2015. [8] S. Borzsony, D. Kossmann, and K. Stocker, The skylne operator, n Proceedngs of the 17th Internatonal Conference on Data Engneerng, pp , Hedelberg, Germany, Aprl 2001.

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