Research Article A New Manufacturing Service …downloads.hindawi.com/journals/mpe/2016/7343794.pdfResearch Article A New Manufacturing Service Selection and Composition Method Using

Research ArticleA New Manufacturing Service Selection and CompositionMethod Using Improved Flower Pollination Algorithm

Wenyu Zhang, Yushu Yang, Shuai Zhang, Dejian Yu, and Yangbing Xu

School of Information, Zhejiang University of Finance and Economics, Hangzhou 310018, China

Correspondence should be addressed to Shuai Zhang; [email protected]

Received 12 August 2016; Revised 16 November 2016; Accepted 22 November 2016

Academic Editor: Eusebio Valero

Copyright © 2016 Wenyu Zhang et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

With an increasing number ofmanufacturing services, themeans bywhich to select and compose thesemanufacturing services havebecome a challenging problem. It can be regarded as a multiobjective optimization problem that involves a variety of conflictingquality of service (QoS) attributes. In this study, a multiobjective optimization model of manufacturing service composition ispresented that is based on QoS and an environmental index. Next, the skyline operator is applied to reduce the solution space. Andthen a new method called improved Flower Pollination Algorithm (FPA) is proposed for solving the problem of manufacturingservice selection and composition. The improved FPA enhances the performance of basic FPA by combining the latter withcrossover and mutation operators of the Differential Evolution (DE) algorithm. Finally, a case study is conducted to compare theproposed method with other evolutionary algorithms, including the Genetic Algorithm, DE, basic FPA, and extended FPA. Theexperimental results reveal that the proposed method performs best at solving the problem of manufacturing service selection andcomposition.

1. Introduction

With the rapid development of the Internet and associatedinformation technology, many traditional manufacturingindustries have changed the ways by which they provideproducts and services. Suppliers increasingly encapsulatetheir production capacities into services and upload themto shared network platforms. In general, user requirementsare extremely complex and cannot be satisfied by a singlemanufacturing service. Therefore, to meet such increasinglycomplex requirements, appropriate candidate services mustbe chosen from a service set for each subtask and combinedto form a composite service. The optimal composite man-ufacturing service is then identified from all the possiblesolutions and used to perform the multiobjective task [1].As an increasing number of manufacturing services becomeavailable, the manner in which to select and compose themappropriately is a challenging problem. This is referred toas a multiobjective manufacturing grid resource servicecomposition and optimal-selection problem [1].

As for the background ofmanufacturing service selectionand composition, the concept of cloud manufacturing, as anew manufacturing paradigm, was proposed by Zhang et al.

[2] and has attracted considerable attention from experts andscholars in the field of manufacturing service. Tao et al. [3]presented the classification of manufacturing resource andservice and proposed a five-layered structure (i.e., resourcelayer, perception layer, network layer, service layer, andapplication layer) toward cloud manufacturing.

With the rapid increase in the number of web serviceshaving the same functional attributes, selecting an appro-priate candidate service based on nonfunctional attributessuch as quality of service (QoS) has become an equallychallenging task that has also attracted considerable atten-tion [4]. As with web services, each manufacturing servicehas functional and nonfunctional attributes [5]. Regard-ing nonfunctional attributes, QoS has become crucial inevaluating the overall performance of manufacturing ser-vice composition. Time, cost, reliability, and availabilityare four representative QoS properties that are frequentlyemployed by researchers. Tao et al. [6] presented a QoSdescription supporting resource service correlation. Theyalso described the basic resource service composite modes(RSCM) for composite resource service and the methodsto translate a complicated RSCM into a simple sequenceRSCM.

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2016, Article ID 7343794, 12 pageshttp://dx.doi.org/10.1155/2016/7343794

2 Mathematical Problems in Engineering

However, it is worth noting that, unlike typical web ser-vice composition, additional restrictions on manufacturingservice composition exist [7]. For example, manufacturingenterprises belong to the entity enterprises that often produceconsiderable amount of contamination in the course ofproviding services. Therefore, from the perspective of greenmanufacturing [7], an environmental index (i.e., carbonemission) must be factored into the evaluation system tomeasure the overall performance of a manufacturing servicecomposition. In addition, as the number of candidate servicesincreases, the solution space becomes so large that finding theoptimal solution is difficult.Thus, some researchers have pro-posed using the skyline operator [8] for service compositionand have advanced the concept of skyline service [9, 10] tolimit the size of the solution space and improve the combinedutility of composite solutions.

Even after applying of the skyline operator, selectingan optimal composite solution remains time-consuming.Although many evolutionary algorithms, like Genetic Algo-rithm (GA), Particle Swarm Optimization (PSO) algorithm,and Differential Evolution (DE) algorithm, have been pro-posed to solve this problem, room remains for improvement.The Flower Pollination Algorithm (FPA) was first proposedby Yang [11] in 2012 as a new metaheuristic algorithm. It hasbeen applied to many fields and has been shown to performwell, especially at solving optimization problems.

In this study, a novel and efficient method is proposedthat employs not only the skyline operator but also theimproved FPA (IFPA) to solve the problem of QoS-basedmanufacturing service selection and composition. The useof the skyline operator means that only those services thatbelong to the skyline service set are valid candidate services[9]. In this study, the differences between our proposedIFPA and basic FPA are the following three points. First,a grouping strategy and elite replacement strategy are usedto improve the convergence speed of FPA. Second, we notonly use the adaptive parameter to adjust dynamically theswitch probability between global and local pollination butalso employ an adaptive scaling factor to control the stepsize of the global search. Third, we introduce crossover andmutation operators of the DE algorithm into the local searchof FPA to improve its performance.

The rest of this paper is organized as follows. Relatedstudies about manufacturing service composition and thedevelopment of relevant evolutionary algorithms are dis-cussed in Section 2. Section 3 provides a description ofthe model that is established in this study and definitionsof skyline service. The basic FPA and its improvementsare introduced in Section 4. Section 5 offers a case studyand several comparative experiments used to illustrate thepracticality and effectiveness of the proposed method. Aconclusion that outlines the authors’ main contributions andthat describes future research is provided in Section 6.

2. Related Work

2.1. Manufacturing Service Selection and Composition. Withthe rapid development of cloud computing and the advent

of big data, the problem of optimizing composite services iscausing increasing concern in the manufacturing industry.A composite service is the result of aggregating multipleservices into one, and its aim is to execute functions of greatercomplexity than those that can be performed by a singleservice [12].

To evaluate the overall performance of service com-position, Zeng et al. [13] set QoS attributes as the mainmeasurement, including execution price, execution time,reputation, reliability, and availability, which were widelyaccepted and applied. To obtain high quality of servicecomposition, they also introduced a middleware platform toexpress user satisfaction as utility functions. Tao et al. [14]presented different aggregation formulations for four differ-ent structures of service composition, including sequence,parallel, selective and cycle. In addition, the correlationof service composition was taken into consideration. Tominimize implementation time and cost and to maximizereliability, an improved Particle Swarm Optimization (PSO)algorithmwas introduced as the key technology. Consideringthe heterogeneous QoS values and homogeneous resourcesin manufacturing service, Zheng et al. [15] took the dynamiccustomer requirements into account by introducing the fuzzytheory and presented the way of calculating fuzzy QoS valueand selecting the best services considering design preference.Hao et al. [16] pointed out that, in order to obtain the bestservice composition solution, user QoS constraints need tobe relaxed rather than overlooked, so they proposed a novelQoS model and computational framework. Tao et al. [17]made an overview aboutmanufacturing service managementand analyzed the future research directions of manufacturingservice composition and optimization.

However, most of the aforementioned studies only focuson the general QoS attributes and ignore the importance ofenvironmental protection, which has become one of themostimportant attributes in the process of selecting manufactur-ing service [7, 17]. Therefore, an environmental index suchas carbon emission is added in this paper as a soft constraintto measure the overall performance of manufacturing servicecomposition.

As a multiobjective optimization problem, its solutionspace is very large and the objectives ofmanufacturing servicecomposition are in conflict with each other. Thus, Alrifai etal. [18] first introduced the skyline operator [8] into servicecomposition to reduce the number of candidate services.They also proposed the definition of skyline service toimprove the efficiency of service selection. Skyline service hasbeen widely used by scholars and become an active researcharea [10, 12]. In view of its good performance, the proposedmethod in this paper incorporates the skyline service conceptin the model to limit the size of the solution space andimprove the overall quality of the optimal manufacturingservice composition solution.

2.2. The Development of Evolutionary Algorithms in Manufac-turing Service Composition. In order to solve the problem ofservice composition, experts and scholars have used a varietyof evolutionary algorithms. For example, in the authors’

Mathematical Problems in Engineering 3

previous work [19], the extended GA was employed to solvethe problemofmanufacturing resource composition, treatingthe overall utility of multiple objectives as the metric of thecomposite solution. Li et al. [4] employed a multipopulationGA to solve the problem of QoS-oriented web servicecomposition for the Internet ofThings. Tao et al. [1] presentedthe concept of multiobjective manufacturing grid resourceservice composition and optimization problem and used thePSO algorithm to solve it.They not only introduced the basicresource service compositemodes (RSCM) and the principlesto translate a complicated RSCM into a simple sequence butalso used the extended PSO algorithm to improve the qualityof the optimal solution. Pop et al. [20] introduced a modifiedDE algorithm with a new genome-encoding method tosolve the problem of service composition. Wei and Liu [21]introduced the Ant Colony Optimization (ACO) algorithminto the cloud manufacturing resource allocation model.Wang et al. [22] proposed a new optimization algorithmcalled the culture max-min ant system, which not onlyestablished a novel comprehensive model including bothgeneric and domain QoS but also combined the CultureAlgorithm (CA) and theACOalgorithm into a new approach.Lartigau et al. [7] applied an adapted Artificial Bee Colony(ABC) optimization algorithm to the cloud manufacturingservice composition model, which not only included QoSattributes but also took the geoperspective transportationinto consideration.

Although many conventional evolutionary algorithmshave been used in service composition, each has its owndeficiencies, for example, converging too easily to localoptimumor premature convergence. In order to obtain betteroptimization solutions, a variety of emerging algorithmshave been proposed. For example, inspired by the biologicalprocess of flower pollination, Yang [11] first proposed FPAin 2012 and proved that it was more efficient in solvingoptimization problems than both the GA and PSO.

As a novel and effectivemetaheuristic algorithm, FPA hasbeen used in many fields. Abdelaziz and Ali [23] used it in amultimachine power system to seek the optimal parametersof static VAR compensator damping controller. Abdelaziz etal. [24] also used FPA to solve the problem of economicload dispatch and combined economic emission dispatchproblems. To solve sizing optimization problem of trussstructures, Bekdas et al. [25] used FPA tominimize theweightof truss structures. To obtain feasible optimized designs, theyadded an iterative constraint handling strategy into the FPAsearch engine. El-henawy and Ismail [26] extended the basicFPA by combining it with chaotic search to solve large integerprogramming problems. Wang et al. [27] applied FPA withbee pollinator in the field of cluster analysis to make up fordeficiencies in the 𝑘-means method. By comparing it withother algorithms such as GA, they proved the better accuracyand higher level of stability of their proposed method. Inorder to solve the problem of multilevel image thresholding,Ouadfel and Taleb-Ahmed [28] applied FPA to search forthe best values from a given number of thresholds with thepurpose of maximizing the objective function. To apply theFPA to solve the antenna positioning problem, Dahi et al.[29] proposed four binary variants of the FPA by applying

the principal mapping techniques. In addition, to study theapplicability of this new metaheuristic algorithm in real-world applications, Draa [30] reviewed the performance ofFPA in terms of both qualitative and quantitative aspects,and then he summarized the defects of the basic FPA bycomparing it with state-of-the-art algorithms and pointed outthe extension and application directions of FPA.

However, even though FPA research is expanding, to thebest of our knowledge, it has seldom been used to solvethe optimization problem of service composition, let alonebe applied in the field of manufacturing service selectionand composition. Therefore, in this study, FPA is applied tosolve the manufacturing service selection and compositionproblem by improving the standard FPA in combination withDE. The experimental results show that the proposed IFPAcan obtain better optimal solutions than other algorithmssuch as GA [4], DE [31], basic FPA [11], and even extendedFPA (EFPA) [32].

3. Model of Manufacturing ServiceComposition Problem

3.1. Notations in Manufacturing Service Composition Model.To solve themanufacturing service composition problem andtranslate it into a mathematical model, we use the followingnotations to better illustrate the model of manufacturingservice composition:

𝑇: execution time of the candidate service𝐶: execution cost of the candidate serviceAva: availability of the candidate serviceRel: reliability of the candidate service𝑚: the number of subtasks in each basic structureMS𝑖: the selected manufacturing service realizing 𝑖thsubtask, 𝑖 = 1, 2, . . . , 𝑚𝑇(seq): the total execution time in the sequencestructure𝐶(seq): the total execution cost in the sequencestructureAva(seq): the total availability in the sequence struc-tureRel(seq): the total reliability in the sequence structureEc(MS𝑖): the processed carbon emission value of 𝑖thmanufacturing serviceEc∗: a soft constraint about carbon emission that isprovided by users𝜃𝑖: real carbon emission value of 𝑖th candidate service.

3.2. The Calculation of QoS Properties. In this study, thefour representative QoS properties of execution cost (𝐶),execution time (𝑇), reliability (Rel), and availability (Ava) areemployed as criteria for manufacturing service performanceevaluation [13, 33].

As there are various metrics for different QoS attributes,in order to ensure the accuracy of assessment results, all the


QoS attributes need to be normalized and converted into thesame order ofmagnitude before they are employed in the finalQoS aggregation formula. It has been well recognized thatservice quality is negatively correlated with cost and time, butit has a positive correlation with reliability and availability.Therefore, each QoS attribute is classified as being eitherpositive attribute or negative attribute. The correspondingcalculation formulas are as follows [5]:

𝑞nor ={{{

𝑞 − 𝑞min𝑞max − 𝑞min

if 𝑞max = 𝑞min

1 if 𝑞max = 𝑞min

positive attribute,

(1)

𝑞nor ={{{

𝑞max − 𝑞𝑞max − 𝑞min

if 𝑞max = 𝑞min

1 if 𝑞max = 𝑞min

negative attribute.

(2)

Here, 𝑞 is 𝑘th true attribute value of a manufacturingservice and 𝑞nor represents the corresponding normalizedvalue. 𝑞max represents the maximal value of 𝑘th QoS attributefor manufacturing services in the same candidate service set,that is, 𝑞max = max{𝑞1, 𝑞2, . . .}; 𝑞min stands for the minimalvalue of 𝑘th QoS attribute for manufacturing service, that is,𝑞min = min{𝑞1, 𝑞2, . . .}.

In general, manufacturing service composition includesfour basic structures: sequence, parallel, selective, and cyclestructures. There are different aggregation methods for eachstructure. The aggregation formulas are defined in [13]. Forexample, the following are the calculation formulas for thesequence structure:

𝑇 (seq) =𝑚

∑𝑖=1

𝑇 (MS𝑖) ,

𝐶 (seq) =𝑚

∑𝑖=1

𝐶 (MS𝑖) ,

Rel (seq) =𝑚

∏𝑖=1

Rel (MS𝑖) ,

Ava (seq) =𝑚

∏𝑖=1

Ava (MS𝑖) .

(3)

In order to use a unified standard to measure theoverall performance of manufacturing service composition,the multiple objectives need to be transformed into a singleone by assigning different weights to each attribute accordingto the different preferences of users.The final QoS evaluationvalue ofmanufacturing service composition can be calculatedaccording to the following equation:

𝑓 (QoS) = 𝑤1𝑇 + 𝑤2𝐶 + 𝑤3Rel + 𝑤4Ava, (4)

where𝑤1,𝑤2,𝑤3, and𝑤4 sum to 1 and represent the respectiveweights of the four attributes, including total time (𝑇), totalcost (𝐶), total reliability (Rel), and total availability (Ava), ofmanufacturing service composition.

3.3. The Calculation of Environmental Index. In addition tothe basic QoS indicators, the proposed model contains anenvironmental indicator. Customers generally consider theQoS performance of manufacturing service composition tobe more important than its environmental performance, andtherefore they have different expectations of them. For thatreason, the constraints proposed by customers can be dividedinto two types: rigid constraints and soft constraints. QoSconstraints are defined as being rigid ones that must besatisfied, whereas environmental indices are defined as softconstraints that do not have to be satisfied strictly [22].

In the present paper, the authors regard carbon emissionas an environmental protection index.Aswithmanufacturingtasks, customers impose their own soft constraints thatshould be satisfied as far as possible. For each candidatemanufacturing service MS𝑖, the deviation value between itscarbon emission and soft constraint is calculated accordingto the following equation [22]:

Ec (MS𝑖) =𝜃𝑖 − Ec∗

Ec∗× 𝛽,

𝛽 ={{{

0, if 𝜃𝑖 satisfy Ec∗

1, if 𝜃𝑖 do not satisfy Ec∗,(5)

where Ec(MS𝑖) is the deviation carbon emission value, 𝜃𝑖is the real value (i.e., carbon emission value) provided bysuppliers, and Ec∗ is a soft constraint about carbon emissionthat is provided by users. From the above equation, it isclear that 𝛽 is a binary index. If carbon emission meets theuser requirement for environmental protection, then 𝛽 = 0;otherwise, 𝛽 = 1. It is well known that the smaller thedeviation value, the higher the environmental indicator.Thus,the deviation value is a negative attribute, and its standardizedformula is defined in (2) too. The aggregation formulas ofdeviation value about carbon emission are similar to theaggregation formula of execution cost, so we omitted themhere.

3.4. The Comprehensive Utility of a Manufacturing ServiceComposition. Based on the above description, the final objec-tive function ofmanufacturing service composition is definedas

max𝐹 (MSC) = 𝜆1𝑓 (QoS) + 𝜆2𝑓 (Ec) , (6)

where 𝐹(MSC) is the comprehensive utility of a manufac-turing service composition, 𝑓(QoS) represents the final QoSevaluation, and 𝑓(Ec) represents the final carbon emissionvalue ofmanufacturing service composition. Terms𝜆1 and𝜆2sum to 1 and represent the weights of QoS and environmentalindex in a manufacturing service composition solution,respectively. In this model, the goal is to obtain themaximumvalue of the comprehensive utility, and this comprehensiveutility be regarded as the fitness value in the experimentsection of this paper.


3.5. The Introduction of Skyline Service. Before the newevolutionary algorithm is carried out to solve the problemof manufacturing service composition, the skyline service isintroduced to reduce the number of candidatemanufacturingservices and improve the quality of optimal solution. Theconcept of skyline service, which was based on dominancerelationship, was first proposed by Alrifai et al. [18]. Thefollowing is a detailed description of the related definitions[9].

Definition 1 (dominance). One assumes that there are twovectors 𝑋1 = (𝑥11, 𝑥12, . . . , 𝑥1𝑑) and 𝑋2 = (𝑥21, 𝑥22, . . . , 𝑥2𝑑)in 𝑑-dimensional space. If the following two conditionsare satisfied, one says that 𝑋1 dominates 𝑋2. Firstly, eachdimension in𝑋1 is greater than or equal to the correspondingin𝑋2. Secondly, at least one dimension in𝑋1 is strictly greaterthan the corresponding in 𝑋2. The two conditions can beexpressed mathematically as follows [34]:

if ∀𝑖 ∈ {1, 2, . . . , 𝑑} , 𝑥1𝑖 ≥ 𝑥2𝑖,∃𝑗 ∈ {1, 2, . . . , 𝑑} , 𝑥1𝑖 > 𝑥2𝑖.

(7)

Otherwise, if either of the two conditions is not satisfied, then𝑋1 does not dominate𝑋2.

Definition 2 (skyline service). For two manufacturing ser-vices MS1 and MS2, if all the QoS attributes of MS1 arebetter than or equal to the corresponding ones of MS2, andat least one attribute of MS1 performs strictly better than thecorresponding attribute of MS2, then MS1 dominates MS2,which is denoted as MS1 ≺ MS2 [34]. That is to say, MS1 isbetter thanMS2. A skyline service refers to such a service thatis not dominated by any other services.

The application of skyline operator can limit the sizeof the solution space of manufacturing service compositionbecause the process of choosing skyline service eliminatesthose other services that could not possibly be selected as thefinal manufacturing service composition.

4. Improved Flower Pollination Algorithm

4.1. Introduction to the Standard FPA. FPA is a novel bionicevolutionary algorithm that was first proposed by Yang in2012 [11]. The inspiration for FPA comes from the naturalpollination process that takes place in flowering plants. InFPA, each flower stands for a feasible solution and theobjective function value in Section 3.3 is regarded as thefitness value. To mimic the pollination process, there aretwo different pollination methods for each flower to choose:global or local pollination with switch probability 𝑝. Thespecific process is described as follows.

For each individual, there is a random number rand. Ifrand < 𝑝, then global pollination should be carried out. In theglobal pollination process, each flower updates its positionaccording to the following equation [11]:

𝑋𝑡+1𝑖 = 𝑋𝑡𝑖 + 𝛾𝐿 (𝜆) (𝑋𝑡best − 𝑋𝑡𝑖) , (8)

where𝑋𝑡𝑖 and𝑋𝑡+1𝑖 are the old and new positions of 𝑖th flower,respectively, 𝑋𝑡best is the best flower at current iteration 𝑡,

which has the best fitness value in the whole population,and 𝛾 is the scaling factor that controls the step size ofglobal pollination. Parameter 𝐿, the Levy flight, is used as thestrength of pollination in the basic FPA, and the step size 𝐿(𝜆)obeys the Levy distribution:

𝐿 ∼ 𝜆Γ (𝜆) sin (𝜋𝜆/2)𝜋

1𝑠1+𝜆 , (𝑠 ≫ 𝑠0 > 0) , (9)

where Γ(𝜆) is the standard gamma function and 𝜆 is from 0.3to 1.99 [35]. In the basic FPA, Yang [11] set the distributionfactor 𝜆 as 1.5. This distribution is valid when large step 𝑠 isbigger than 0.

If rand > 𝑝, the local pollination process shouldbe carried out. Flower 𝑋𝑡𝑖 obtains its new position 𝑋𝑡+1𝑖according to the difference between its old position and theposition of two neighboring flowers𝑋𝑡𝑝 and𝑋𝑡𝑞. This processis considered as local search, and the updating equation [11]is defined as

𝑋𝑡+1𝑖 = 𝑋𝑡𝑖 + 𝑟 (𝑋𝑡𝑝 − 𝑋𝑡𝑞) , (10)

where 𝑟 is drawn from a uniform distribution [0, 1], and it isconsidered as a local random walk.

After pollination is completed, the new individualsupdate their positions by comparing fitness values. If thefitness of𝑋𝑡+1𝑖 is better than that of𝑋𝑡𝑖 , the new position of 𝑖thflower will be replaced by𝑋𝑡+1𝑖 . Otherwise, 𝑖th flower remainsat𝑋𝑡𝑖 .

4.2. Improvements of IFPA Compared with the Standard FPA.Like many other evolutionary algorithms, basic FPA suffersfrom premature convergence and can easily fall into localoptimum. In order to overcome these shortcomings, theauthors propose an IFPA to extend the standard FPA inthree aspects. Firstly, the whole population is divided intodifferent groups on average, and an elite replacement strategyis introduced to replace the worst individual in each group.Secondly, the switch probability 𝑝 and scaling parameter𝛾 are adapted dynamically with the iterative process ratherthan remaining fixed like the basic FPA. This modificationincreases not only the global search ability but also the localsearch ability of basic FPA. Thirdly, in order to enhance itslocal search ability and obtain better optimal solution, thelocal search method is combined with the mutation andcrossover operators of DE.

4.2.1. Grouping Strategy and Elite Replacement Strategy. Inthe original FPA, flowers search for the optimal solution ina single population only. This method may take a relativelylong time and its global search ability is insufficient. Based onthis idea and inspired by [32], the authors introduce a parallelgrouping strategy to solve this problem effectively. Beforestarting the iteration, the whole population is divided intothree different groups on average according to their fitnessvalues. The detailed operation steps are described as follows.Firstly, the fitness is calculated and ranked. Secondly, thewhole population is divided into three groups on average


according to their fitness.The first group consists of individu-als with the best fitness, the third group consists of individualswith the worst fitness, and the remaining individuals belongto the second group. The iterative process is carried out ineach group concurrently.

In addition, in order to improve the FPA convergence andthe optimal solution quality, an elite replacement strategy isintroduced. From each group, the best individual is selectedas an elite individual. That is to say, after global and localpollination, the worst individual is replaced with the eliteindividual in each group. If there are duplicate individuals,then the duplicate one can be modified by selecting onedimension randomly to carry out mutation before startingthe next iteration. In each group, FPA is carried out concur-rently in the same way. Before executing the next generation,these groups are combined into a single population.

4.2.2. Adaptive Switch Probability and Scaling Factor. Instandard FPA, Yang [11] set the switch probability 𝑝 as 0.8.However, a fixed switch probability is insufficient. In order toretain a balance between global and local pollination in FPA,the switch probability is generated dynamically accordingto the fitness value of the current generation, which isrepresented by the objective function. The adaptive switchprobability is obtained according to the following equation:

𝑝 = 𝑒(max𝑓−min𝑓)/max𝑓, (11)

where max𝑓 and min𝑓 are the maximum and minimumfitness values, respectively, of an individual in the currentiteration.

For the same reason, in order to escape local optimumand avoid premature convergence in global pollination, thescaling factor 𝛾 is also generated dynamically according tothe number of iterations. The calculation of scaling factordepends on the following formula:

𝛾 = max iter − 𝑡max iter

× 0.3 + 0.2, (12)

where max iter represents the maximum iterations and 𝑡 isthe current iterations. According to this formula, the value ofscaling factor 𝛾 ranges from 0.2 to 0.5, and its value decreaseswith the increase of iterations.

4.2.3. Modified Local Pollination Process by Combining withDE Algorithm. In the local pollination process of 𝑋𝑖, theindividuals 𝑋𝑝 and 𝑋𝑞 are selected randomly except forthe rule that 𝑋𝑝 and 𝑋𝑞 should differ from the objectiveindividual 𝑋𝑖. However, this method is not sufficiently far-sighted and there is no direction of evolution to follow, whichmay lead the algorithm to fall into local convergence. Inorder to avoid this, the local search method is modified bycombining it with DE. The detailed steps are as follows.

Firstly, the DE mutation operator [31] is applied in localsearch. For𝑋𝑡𝑖 , a newmutated individual𝑉𝑡𝑖 will be generatedaccording to the following equation:

𝑉𝑡𝑖 = 𝑋𝑡best + 𝑟 (𝑋𝑡𝑝 − 𝑋𝑡𝑞) , (13)

where 𝑋𝑡best is the best individual in current generation 𝑡 inthe whole population, 𝑋𝑡𝑝 and 𝑋𝑡𝑞 are different from 𝑋𝑡𝑖 andbelong to the same group with𝑋𝑖, and 𝑟 is a random numberthat is drawn from a uniform distribution [0, 1].

Secondly, the crossover operator [31] is also introducedafter mutation. The two-point crossover operator can becarried out as:

𝑋𝑡+1𝑖,𝑗 ={{{

𝑉𝑡𝑖,𝑗, if 𝑙 ≤ 𝑗 ≤ ℎ𝑋𝑡𝑖,𝑗, else

𝑖 = 1, 2, . . . , 𝑛; 𝑗 = 1, 2, . . . , 𝑑.

(14)

Here, 𝑋𝑖,𝑗 is 𝑗th dimension of 𝑖th individual 𝑋𝑖; 𝑙 and ℎ aregenerated randomly from [1, 𝑑]. The mutated individual 𝑉𝑖obtained bymutation operator changes its location accordingthe above equation. This modification not only increases thelocal search ability but also improves the population diversityto avoid local optimal.

The pseudocode of improved FPA is summarized inPseudocode 1.

4.3. Representation Scheme of IFPA in Multiobjective Manu-facturing Service Composition. In order to solve the problemof multiobjective manufacturing service composition withIFPA, the representation scheme should be solved first. Inthis paper, population individuals are referred to as flowers.Each flower represents one possible solution of manufac-turing service composition; it has 𝑁 dimensions that standfor the number of subtasks. We use real encoding in thismanuscript, with all numbers as integers. For example, aflower 𝑋 = (3, 4, 2, 5, 1, 7) that contains six dimensionsrepresents a manufacturing service composition solutionthat comprises six subtasks. The first dimension (value =3) instructs the first subtask to select its third candidateservice. The second dimension (value = 4) instructs thesecond subtask to select its fourth candidate service andso on for the remaining dimensions. In this paper, weuse manufacturing service selection (MSS) to represent thecandidate service in each service set, and MSS𝑛𝑚 standsfor 𝑛th subtask selecting 𝑚th candidate service. Thus, thecorresponding composition solution with this pollen can beexpressed as (MSS13,MSS24,MSS32,MSS45,MSS51,MSS67).

4.4. The Procedure of Using IFPA for Solving MultiobjectiveManufacturing Service Composition Problems. The wholeprocedure of the proposed algorithm is described as follows.

Step 1 (selecting skyline services). Firstly, specific userrequirements are identified, including constraint conditionsand preferences. Secondly, skyline services are selectedaccording to the aforementioned definitions in Section 3.4before IFPA is carried out.

Step 2 (initializing). Firstly, the parameters of the proposedmethod are set, including initial population size, maximumiterations, and number of groups. In addition, 𝜆 = 1.5 is setin the Levy flight. Secondly, an original population from theskyline service set is initialized.


(1) Initialize (𝑡 = 0)Objective max𝑓(𝑥)Set parameters: population size𝑁, maximum iterations max iter and 𝜆Randomly generate an initial population of𝑁 flowers𝑋(𝑡) = (𝑋𝑡1, 𝑋𝑡2, . . . , 𝑋𝑡𝑁)(2) Repeat (𝑡 = 1 to max iter)while (𝑡 < max iter)Find the best individual𝑋best in current populationDivide the whole population into three grousDefine a switch probability 𝑝 via 𝑝 = 𝑒(max𝑓−min𝑓)/max𝑓

for 𝑖 = 1 : 𝑁if rand < 𝑝

Generate a step size 𝐿(𝜆)Define a scaling factor 𝛾 via 𝛾 = ((max iter − 𝑡)/max iter) × 0.3 + 0.2Generate a step size 𝐿 that obeys a Levy distributionDo global pollination via𝑋𝑡+1𝑖 = 𝑋𝑡𝑖 + 𝛾𝐿(𝜆)(𝑋𝑡best − 𝑋𝑡𝑖 )

elseDo local pollination via 𝑋𝑡+1𝑖 = 𝑋𝑡𝑖 + 𝑟(𝑋𝑡𝑝 − 𝑋𝑡𝑞)Execute crossover operation and find out𝑋𝑡+1𝑖

end ifEvaluate new individualIf new individual is better, update its location

end forCarry out elite replacement strategyCombine these three groups into one populationUpdate the best individual𝑋bestend while(3)OutputOutput the best individual𝑋best

Pseudocode 1: Pseudocode of improved FPA.

Step 3 (grouping strategy). Firstly, the fitness of each individ-ual is calculated and ranked, and the population is dividedinto three groups according to Section 4.2.1. Then, the globaloptimal individual𝑋best and the elite individual in each groupare selected. The iterative process is carried out in all groupsconcurrently.

Step 4 (identifying the switch probability). For each genera-tion, its switch probability 𝑝 is calculated according to (11).A random number rand for each individual is generated andthen compared with 𝑝 to determine the pollination method.

Step 5 (global pollination). If rand < 𝑝, then globalpollination is chosen. The corresponding calculations aregiven in (8), (9), and (12).

Step 6 (local pollination). If rand > 𝑝, then local pollinationis chosen. The corresponding calculations are given in (13)and (14).

Step 7 (updating locations). After completion of global andlocal pollination, the location of each individual is updated.If 𝑓(𝑋𝑡+1𝑖 ) > 𝑓(𝑋𝑡𝑖), then 𝑖th individual updates its location;otherwise, it remains at old location.

Step 8 (elite replacement strategy). After location updating isfinished, the elite replacement strategy is employed to replacethe worst individual in each group according to Section 4.2.1.

Step 9 (merging). When all groups have finished the pol-lination process, the three groups are combined into asingle population, and then the current best solution 𝑋best isupdated.

Step 10 (stopping iteration). In general, there are two ter-mination conditions for an evolutionary algorithm with anoptimization problem:maximum iterations and fitness value.If either of the conditions is satisfied, then the iterationterminates and the best solution𝑋best is outputted; otherwisereturn to Step 3 and repeat until a termination condition issatisfied.

The flow chart of the proposed method is shown inFigure 1.

5. Experimental Simulation and Comparison

In order to verify the practicality and effectiveness of theIFPA, the proposed method is used to solve an illustratedproblem in manufacturing service composition. A variety ofcomparative experiments with GA [4], DE [31], basic FPA[11], and extended FPA (EFPA) [32] were carried out toillustrate the superiority of the proposed method. All theexperiments were carried out using a personal computer withWindows 7, an Intel Core 3-GHz CPU, and 2GB of memory.All of the algorithms were programmed in C#.


Input user requirements and algorithm’s parameters

Initialize N flowersofpopulationa

Start

Divide the population into three groups

If rand < pNo

Select skyline services

Identify the switch probability p using (11)

Yes

Generate Lsizestepausing (9)

Define a scaling factor

Carry out DE mutationoperator using (13)

Carry out DE crossoveroperator using (14)

Global pollination Local pollination

Update locations of new solutions

Carry out elite replacement strategy

Combine the three groups into one population

Is termination condition satisfied?

Yes

No

End

Find the current best solution Xbest

Output the current best solution Xbest

Find the current best solution Xbest

𝛾 using (12)

Figure 1: Flow chart of proposed method.

5.1. Experimental Design. A case study of manufacturing ser-vice composition with six subtasks was designed to illustratethe practicality and effectiveness of the proposed method.Although the example is relatively simple, it includes allthe four structures of manufacturing service composition, asshown in Figure 2.The final fitness function of thismodel canbe referred to Section 3.

The selection probabilities of subtasks 2 and 3 are 0.4and 0.6, respectively. The cycle time of subtask 6 is five.The weight of each QoS attribute is set as 0.25, and theweights of QoS and Ec are 0.6 and 0.4, respectively. Eachmanufacturing service has four QoS attributes (time, cost,reliability, and availability) and one environmental index

(carbon emission).Themaximum iterations are 50.There aretwo termination conditions: either it reaches the maximumnumber of iterations or it converges such that the successivedifference in final average fitness is less than 0.001 for threeconsecutive generations of individuals [19].

5.2.ThePracticality of IFPA. In order to verify the practicalityof IFPA, the illustrative example shown in Section 5.3 isassigned with the practical manufacturing data. The partialcandidatemanufacturing service and corresponding attributevalues are shown in Table 1. The final constraint conditionsof the whole service composition are Total Time < 32 hrs,Total Cost < $210, and Total Ec∗ < 3. There are 15 candidateservices in each manufacturing service set for each subtask.As for the proposed IFPA, the initial population size is 20.Figure 3 records the evolutionary tracks of the proposedmethod, including the best and average fitness values ineach generation. Here, the horizontal axis represents iterationnumber and the vertical axis denotes the fitness of eachgeneration. As shown in Figure 3, the IFPA stopped at themaximum iterations. And the optimal fitness value at the 50thiteration was 5.81; the best individual was𝑋 = (7, 1, 7, 1, 2, 5).This means that the optimal service composition solution is(MSS17,MSS21,MSS37,MSS41,MSS52,MSS65).Thus, it can beconcluded that the proposed approach can find the relativeoptimal solution and converges acceptably well.

In order to test the efficiency and superiority of theproposed method, IFPA is compared with other algorithms,including GA, DE, basic FPA, and EFPA, to solve thesame problem described above. Figure 4 shows the differentevolutionary tracks of these algorithms, and the orange dotsrepresent the iterative termination points. In order to ensurethe objectivity of the experiment, different weight combina-tions are taken into account, including not only the weightof cost, time, reliability, and availability but also the weightof QoS and environment index. From this figure, two resultscan be obtained. Firstly, the fitness values obtained fromthe proposed method are higher than those obtained by theother algorithms in most cases. In other words, the proposedmethod can obtain better solutions even in comparisonwith EFPA. Secondly, the proposed method converges fasterthan the other algorithms. It illustrates that the proposedalgorithm has a strong search ability and fast convergencespeed. Hence, it is an effective method to solve the problemof manufacturing service composition even under differentweight combinations in the final objective function.

5.3.The Effectiveness of IFPA. In order to test the effectivenessand stability of the proposed method, the fitness values of thebest manufacturing service composition solution in differentalgorithms are compared by changing the values of QoSproperties and environmental index of candidate services,the number of candidate services in each service set, and theinitial population size. In these experiments, the value rangesof QoS properties and environmental index are Cost = 0–50,Time = 0–10, Rel = 0.6–1, Ava = 0.4–1, and 𝜃 = 0–5. The finalconstraint conditions of the whole service composition arethe same as Section 5.2.


Table 1: The partial candidate manufacturing services and corresponding attribute values.

Subtask Candidate manufacturing service Cost (0, 50) Time (0, 10) Reliability (0.6, 1) Availability (0.4, 1) Carbon emission (0, 5)

Subtask1MSS11 33.00 2.99 0.99 0.48 3.57MSS12 7.75 6.19 0.64 0.42 2.86MSS13 32.51 9.39 0.70 0.95 1.54






0.4

0.6

5

S ESubtask1

Subtask2

Subtask3

Subtask4

Subtask5

Subtask6

Figure 2: Case study of manufacturing service composition.

Iterations

Fitn

ess

4.004.204.604.804.40

5.405.60

5.005.20

5.80

01 05 09 13 17 21 25 29 33 37 41 45 49

BestAverage

Figure 3: Evolutionary tracks of the proposed method.

The results shown in Figure 5 indicate that most often theproposed method obtains the best solutions with the highestfitness values under different number of candidate servicesin each service set. In the same way, Figure 6 shows thatwhen the initial population size changes, the results obtainedby the proposed method are better than those of the othermethods in most cases. In order to ensure the objectivityof the experiment, different weight combinations are takeninto account, including not only the weight of execution cost,execution time, reliability, and availability but also the weightof QoS and environment index.

Based on all the experimental results, it is concludedthat the proposed method searches better and converges

faster than the other algorithms evaluated in this paper, evenunder different weight combinations in the final objectivefunction. The proposed method performs better in terms ofboth effectiveness and efficiency than the baseline algorithmsof GA, DE, FPA, and EFPA in solving the problem ofmanufacturing service selection and composition.

6. Conclusions

In this study, a new method called IFPA was proposed tosolve the problem of manufacturing service selection andcomposition based on QoS and an environmental index. Themain contributions of this paper are as follows:

(i) The concept of skyline service is introduced in themanufacturing service composition model. The sky-line operator can not only reduce the search space butalso improve the quality of final optimal solutions.

(ii) Three improvements were made to the basic FPAto produce the IFPA. First, a grouping strategy andelite replacement strategy were employed to improvethe convergence speed of FPA. Second, two adaptiveparameters were used to adjust the switch probabilityand control the step size of the global search. Third,the mutation and crossover operators of the DEalgorithmwere integratedwith the local search of FPAto improve the performance of FPA.


Fitn

ess

4.804.955.255.405.10

5.856.00

5.555.70

6.15

Iterations01 05 09 13 17 21 25 29 33 37 41 45 49

IFPAFPAGA

DEEFPA

(a) 𝑤1 = 0.25, 𝑤2 = 0.25, 𝑤3 = 0.25, 𝑤4 = 0.25, and 𝜆1 = 0.6, 𝜆2 = 0.4

Fitn

ess

4.704.855.155.305.00

5.755.90

5.455.60

6.05

Iterations01 05 09 13 17 21 25 29 33 37 41 45 49

IFPAFPAGA

DEEFPA

(b) 𝑤1 = 0.3, 𝑤2 = 0.3, 𝑤3 = 0.2, 𝑤4 = 0.2, and 𝜆1 = 0.7, 𝜆2 = 0.3

Fitn

ess

3.904.054.354.504.20

4.955.10

4.654.80

5.25

IFPAFPAGA

DEEFPA

Iterations01 05 09 13 17 21 25 29 33 37 41 45 49

(c) 𝑤1 = 0.4, 𝑤2 = 0.1, 𝑤3 = 0.3, 𝑤4 = 0.2, and 𝜆1 = 0.8, 𝜆2 = 0.2

Figure 4: Evolutionary tracks of different algorithms with different weight combinations.

Fitn

ess

4.404.605.005.204.80

5.806.00

5.405.60

6.206.40

Candidate services05 09 13 17 21 25 29 33 37 41 45 49

IFPAFPAGA

DEEFPA

(a) 𝑤1 = 0.25, 𝑤2 = 0.25, 𝑤3 = 0.25, 𝑤4 = 0.25, and 𝜆1 = 0.6, 𝜆2 = 0.4

Fitn

ess

4.404.204.605.005.204.80

5.806.00

5.405.60

6.206.40


IFPAFPAGA

DEEFPA

(b) 𝑤1 = 0.3, 𝑤2 = 0.3, 𝑤3 = 0.2, 𝑤4 = 0.2, and 𝜆1 = 0.7, 𝜆2 = 0.3

Fitn

ess

4.404.204.60

3.803.604.00

5.005.204.80

5.405.60


IFPAFPAGA

DEEFPA

(c) 𝑤1 = 0.4, 𝑤2 = 0.1, 𝑤3 = 0.3, 𝑤4 = 0.2, and 𝜆1 = 0.8, 𝜆2 = 0.2

Figure 5: Performance of different algorithms under different number of candidate services with different weight combinations.


5.405.50

5.205.30

5.805.90

5.605.70

5.1011 15 19 23 27 31 35 39 43 4707

Population size

Fitn

ess

IFPAFPAGA

DEEFPA

(a) 𝑤1 = 0.25, 𝑤2 = 0.25, 𝑤3 = 0.25, 𝑤4 = 0.25, and 𝜆1 = 0.6, 𝜆2 = 0.4

11 15 19 23 27 31 35 39 43 4707Population size

Fitn

ess

IFPAFPAGA

DEEFPA

5.405.50

5.205.30

5.805.90

5.605.70

5.105.00

(b) 𝑤1 = 0.3, 𝑤2 = 0.3, 𝑤3 = 0.2, 𝑤4 = 0.2, and 𝜆1 = 0.7, 𝜆2 = 0.3

11 15 19 23 27 31 35 39 43 4707

4.504.60

4.304.40

4.90

4.704.80

4.20

Population size

Fitn

ess

IFPAFPAGA

DEEFPA

5.005.10

(c) 𝑤1 = 0.4, 𝑤2 = 0.1, 𝑤3 = 0.3, 𝑤4 = 0.2, and 𝜆1 = 0.8, 𝜆2 = 0.2

Figure 6: Performance of different algorithms under different initial population sizes with different weight combinations.

In this study, a multiobjective problem was transformedinto a single objective problem. Nevertheless deficienciesexist that must be addressed. In a future study, the Pareto-optimal method can be considered to solve a real multiobjec-tive problem instead of simply transforming it into a singleobjective problem. In addition, the authors will attempt tocombine FPA with other evolutionary algorithms to improvethe performance of basic FPA in order to solve the problemof manufacturing service composition.

Competing Interests

The authors declare that there are no competing interestsregarding the publication of this paper.

Acknowledgments

Thework has been supported by China National Natural Sci-ence Foundation (no. 51375429 and no. 51475410), ZhejiangNatural Science Foundation of China (no. LY17E050010), andHumanities and Social Sciences Research Project of Ministryof Education, China (no. 15YJC870008).

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