USER-CENTRIC OPTIMIZATION FOR CONSTRAINT WEB SERVICE COMPOSITION USING A FUZZYGUIDED GENETIC ALGORITHM SYSTEM

International Journal on Web Service Computing (IJWSC), Vol.3, No.3, September 2012
DOI : 10.5121/ijwsc.2012.3301 1
USER-CENTRIC OPTIMIZATION FOR CONSTRAINT
WEB SERVICE COMPOSITION USING A FUZZY-
GUIDED GENETIC ALGORITHM SYSTEM
Mahdi Bakhshi1
and Dr. Seyyed Mohsen Hashemi2
1
Department of Computer Engineering, Islamic Azad University,
Shahrbabak Branch
Shahrbabak, Iran
mb@shahrbabakiau.ac.ir
2
Department of Computer Engineering, Islamic Azad University,
Science and Research Branch, Tehran, Iran
hashemi@isrup.com
ABSTRACT
Service-Oriented Applications (SOA) are being regarded as the main pragmatic solution for distributed
environments. In such systems, however each service responds the user request independently, it is
essential to compose them for delivering a compound value-added service. Since, there may be a number of
compositions to create the requested service, it is important to find one which its properties are close to
user’s desires and meet some non-functional constraints and optimize criteria such as overall cost or
response time. In this paper, a user-centric approach is presented for evaluating the service compositions
which attempts to obtain the user desires. This approach uses fuzzy logic in order to inference based on
quality criteria ranked by user and Genetic Algorithms to optimize the QoS-aware composition problem.
Results show that the Fuzzy-based Genetic algorithm system enables user to participate in the process of
web service composition easier and more efficient.
KEYWORDS
Web service, service composition, QoS, user preferences, fuzzy logic, genetic algorithms
1. INTRODUCTION
Service composition is a main problem in service based environment. Service composition means
how the simple services aggregate to construct a new compound service with more value. During
several years ago, many researchers have worked on this problem. Heretofore, the diverse
techniques have been presented based on different aspects for performing service composition
[2],[3],[4],[5]. From a business view, it is so important to find a composition whose cost is lower
than all other feasible compositions can be made up. In this paper we are going to find an
approach in order to select the optimal composition among different feasible compositions,
according to quality criteria of services by creation a Fuzzy-guided Genetic Algorithm System
(FGS).
One composite service performs specific functionalities which can be divided into some
component functions. Also, they can be accomplished by some component services respectively.
An example of a composite service is shown in Fig. 3 of [6]. The relations between component

2
functions are represented with the state chart in [3]. Some candidate services with same
functionality and different QoS values (non-functionality) are discovered for each task (abstract
service). Thus, there are various execution plans for each execution path in order to execute one
composite service. Furthermore, since the number of candidate services with same functionality
and different QoS values is increasing with the proliferation of web services, the composite size
should be larger and larger. For example, in one execution path, there are 10 component function
(task) and 20 candidate web services for each component function. In this composition scenario,
the size of composite service should be about 2010
. Since users who request web services usually
have both functional requirements and global QoS constraints, it is necessary to select candidate
services for a given task to achieve the best composite service and maximize user satisfaction
(here, the expression "the best" means the composite service which has the optimal QoS values).
Thus, web service selection with global QoS constraint satisfaction performs an important role in
the process of web service composition [7],[8].
There are several approaches for QoS-aware web service composition. But most approaches are
concerned about web service composition algorithm itself, while ignoring the flexibility for user
to set QoS and cost. Most of them require QoS constraints given in form of numbers. In reality, it
is difficult for users because they don’t know the exact value or range of QoS of composite web
services [25]. In this paper we try solve this problem through getting user constraints in form of
fuzzy constraints.
The remainder of this paper is organized as follows. After a review of performed works in section
2, we review the literature of QoS-aware web service composition using QoS computation in
Section 3. Section 4 presents modeling user preferences using quality driven fuzzy rules, designs
a Fuzzy-guided Genetic Algorithm System (FGS) in order to select the optimal web service
composition according to user preferences. Section 5 reports and discusses the results obtained
from the simulations. Finally, Section 6 concludes.
2. RELATED WORK
So far, different approaches are used for selecting the optimal composition of services from
quality properties point of view, like simple additive weighting technique (SAW) that stated in
[14]. But, the stated work in [3] is an effort based on using of linear programming (LP) technique
based on using constraints influence and introducing objective function for compositions
measurement. A survey of some nonlinear approaches is discussed in [15]. Also, genetic
algorithms are proposed for modeling composite services in the form of population members
without any supposition on linearity of problem contents [13].
The computation of QoS values based on QoS matrix is an appropriate solution. Web services
were ranked by normalizing QoS matrix in [16]. Anyway, it was only a local optimization
algorithm but not a global one for service selection problem. Other works in the area of QoS
computation include [3],[16], which proposed local optimization and global planning. The local
optimization technique could not take global QoS constraints into consideration. When the size of
composite service is very large, for example 2010
, the overhead of global planning is quite
enormous. Hereby, both had limitation to some extent. The mentioned techniques aren't able to
effectively solve the web service selection issue with global QoS constraints. This kind of issues
is NP-hard [16].
GA is a more suitable way in order to solve such issues. But GA performs an important role when
the size of composition is very large. In [13], some numerical simulations show that linear
Programming outperforms GA when the combinatorial size is small. Thus, GA should be

3
preferred instead of linear Programming in the case of widely used services. On the other hand,
linear Programming is to be preferred in the case of very specific services.
Generally, there are a few approaches that have investigated how various attributes of workflows
or composite web services can be aggregated [12],[17]. The proposed approach in this paper
performs selecting of optimal composition by added intuition of these works. Also, in this paper
we have shown, that ranking and thus plan selection is a possible use case for aggregation of web
service attributes, and we have also shown that how ranking can be calculated and plan selection
can be automated.
Therefore, there are some techniques for selecting the optimal service from the point view of the
user, based on giving high score to the service such as, whatever is proposed in [18] that includes
some approaches for generating suitable fuzzy rules for the service selection problem. But,
whatever is proposed in [19] tries to model user preferences by fuzzy rules in different strategies
in order to selecting the optimal service according to user preferences. Most of the existing
approaches for automatic selection of services, either consider only atomic services or they are
not based on user preferences.
For independent global constraint web services composition problem, [26] presents an
optimization method of web service composition with constraints using fuzzy Petri net (FPN),
which can transform solving the optimal service composition problem into locating the largest
trust value of legal firing sequences in the FPN model. Also To solve non-clarity and diversity of
user’s QoS requirements, a multi-strategic approach of fast composition of web services [25] is
proposed. Finally, in [27] an improved version of the standard genetic algorithm approach by
using fuzzy logic during the stochastic genetic search process is proposed. The fuzzy component
dynamically adjusts the crossover and mutation evolution rates for each ten consecutive
generations.
3. COMPUTING THE QOS OF COMPOSITE SERVICES
Services are constitutive units of service oriented systems. The service is presented serve to the
service receptor by server and can be recalled by service receptor. These services can be
combined and produce a value-added service. A composite service is an umbrella structure
aggregating multiple other elementary and composite web services, which interact with each
other according to a process model [3]. The composition of services can lead to a predetermined
objective, while don’t become certain by elementary services.
According to Std. ISO 8402 [20] and ITU E.800 [21], QoS may include a number of non-
functional properties such as cost, response time, availability and reliability. Thus, QoS value of a
composite service can be computed by fair computation of QoS of each composite service.
Methods for compute of quality criteria values are different. These values are used for
computation of QoS of composite services. Here, there are some aggregation functions that are
used to compute the QoS value of each composite service. Table 1 provides some of these
aggregation functions for the execution plan p [13].

4
Table 1. Aggregation functions for computation of quality values.
LoopFlowSwitchSequenceQoSAttr.
)(tTk ∗})({ }...1{ piitTMax ∈∑=
n
i
ii tTp
1
)(*∑=
m
i
itT
1
)(Time (T)
)(tCk ∗∑=
p
i
itC
1
)(∑=
n
i
ii tCp
1
)(*∑=
m
i
itC
1
)(Cost (C)
k
tA )(∏=
p
i
itA
1
)(∑=
n
i
ii tAp
1
)(*∏=
m
i
itA
1
)(Availability (A)
k
tR )(∏=
p
i
itR
1
)(∑=
n
i
ii tRp
1
)(*∏=
m
i
itR
1
)(Reliability (R)
))(,( tFkfL
}...1{
))((
pi
tFf iF
∈}...1{
)))(,((
ni
tFpf iiB
∈}...1{
))((
mi
tFf is
∈
Custom Attr. (F)
Namely, for a Sequence construct of tasks {t1,…, tm}, the Time and Cost functions are additive
while Availability and Reliability are multiplicative. The Switch construct of Cases 1,…,n, with
probabilities p1,…,pn such that 11
=∑
=
=
ni
i ip , and tasks {t1,…, tn} respectively, is always evaluated as
a sum of the attribute value of each task, times the probability of the Case to which it belongs.
The aggregation functions for the Flow construct are essentially the same as those for the
Sequence construct, except for the Time attribute where this is the maximum time of the parallel
tasks{t1,…,tp}[11]. Finally, a Loop construct with k iterations of task t is equivalent to a Sequence
construct of k copies of t [13]. Of course, this table includes a lot of quality attributes. As
mentioned in the last line, other features are definable by user.
4. A FUZZY-GUIDED GA APPROACH ACCORDING TO USER PREFERENCES
By moving toward the age of information, a hypothesis can formulate the human knowledge in
the systematic form, and introduce an approximate description that is reliable and analyzable.
This important subject is applicable by a fuzzy system [1].
User’s need to use considered services with different quality properties cause to user have a
determinative role in the process of service composition. For example, the cost criterion may be
the first grade importance for a user, but his need can be provide with a medium response time,
and for other users these preferences are vice versa. The main problem is providing an approach
for selecting the optimal composition of services according to user preferences and quality criteria
of services and the aggregated values for each quality criterion. Our work is an approach that
relies on the concept of domain ontology for description of services by specifying valid
vocabulary and adding semantic concepts for description of services. These vague semantic
descriptions located in the form of fuzzy rules and create a criterion to measurement of composite
services, and then determine and measure the importance of each rule according to user’s clear
point of views. In fact, we provide an approach for giving score to composite services by entering
the user’s point of views in the process of fuzzy inference.
4.1. Definition of Variables and Membership Functions of the System
In many application domains, the transition between the memberships of an individual from one
set to another is smooth. Consider, For example, height of a human. Small children grow, but
when do they stop to be small? Such kinds of knowledge can be encoded using techniques such as
fuzzy logic [19].

5
Vague knowledge, i.e. rules based on fuzzy logic, are also important from the aspect of evaluating
values of attributes that have very complex dependencies with other attribute values [19]. The
vague membership functions can be modeled in the form of some fuzzy sets. On the other hand,
in simplest form, a domain ontology would specifies the valid vocabulary of describing (naming)
functional and nonfunctional properties that are allowed to occur in service descriptions, but we
need a domain ontology that be able to define categories of linguistic variables. For example, the
response time could be described with the terms fast, normal, slow, very slow [18].
After the complete knowledge about linguistic variables, we can define the membership
functions. In our work, we use the triangular and trapezoidal shapes for defining membership
functions. According to expressed quality criteria, we define linguistic variables in the form of
fuzzy sets based on domain ontology to describe web services, as defined in Figure 1. The reason
of using triangular shapes for defining input variables and defining variable terms as a
symmetrical form is permanent change at input membership functions and the distinction between
different quality vectors.
v e ry c h e a p
c h e a p
m o d e ra te
e x p e n s iv e
0
0 .2
0 .4
0 .6
0 .8
1
0 2 0 4 0 6 0 8 0 1 0 0
C o s t (d o lla r )
Membership
v e ry e x p e n s iv e
v e ry fa s t
fa s t
m o d e ra te
s lo w
0
0 .2
0 .4
0 .6
0 .8
1
0 1 0 2 0 3 0 4 0 5 0
R esp o n se tim e (m sec)
Membership
v e ry s lo w
v e ry lo w
lo w
m o d e ra te
h ig h
0
0 .2
0 .4
0 .6
0 .8
1
-1 4 9 1 4 1 9 2 4
Availab ility (h o u r)
Membership
v e ry h ig h

6
v e ry lo w
lo w
m o d e ra te
h ig h
0
0 .2
0 .4
0 .6
0 .8
1
0 2 0 4 0 6 0 8 0 1 0 0
R e lia b ility (p e r c e n t)
Membership
v e ry h ig h
v e ry lo w m o d e ra te
h ig h
0
0 .2
0 .4
0 .6
0 .8
1
0 2 0 4 0 6 0 8 0 1 0 0
R a n k
Membership
v e ry h ig h
lo w
Figure 1. Membership functions for defining linguistic variables of the system.
In order to define membership functions, we use equal terms in definition of system’s linguistic
variables. The importance of this issue is because of logical relationship between the input and
output variable terms in the formation of system's fuzzy rules.
4.2. Modeling User Preferences Based on Weighting the Rules
We regard preferences as the information that describes the constraints on the properties of an
individual in order to be acceptable for further consideration. We specify different levels of
acceptance by definition of fuzzy membership functions [19].
We model user preferences with fuzzy IF-THEN rules. Fuzzy IF-THEN rules allow to evaluate
good approximations of desired QoS values in a very effective way [22],[23]. The IF part consists
of membership function of various properties of an individual, and the THEN part is one of the
membership functions of a special concept called Rank. Intuitively, a fuzzy rule describes which
composition of attribute values a user is willing to accept to which degree, where attribute values
and degree of acceptance are fuzzy sets, i.e. vague. An example of fuzzy rule can be:
IF Cost = Cheep and Response Time = Fast THEN
Rank = High
If all inputs classify into fuzzy sets viz. Very Low, Low, Moderate, High and Very High and The
output Rank classifies as Very High, High, Moderate, Low and Very Low, then all possible
combinations (55
i.e. 3125) of inputs are considered to design the rule base. Each rule corresponds
to one of the five outputs based on the expert opinions. But modeling all of the preferences by
users on fuzzy rules is very time consuming and maybe impossible.
Our approach with assumption existence of fuzzy rules that can be criteria for ranking quality
vectors related to feasible execution plans, gives more weight to the rules that are more important
from user’s point of view.

7
The confidence factor (CF) of each rule which is a number between 0 and 1, can express the
importance of a rule to obtain the final result. Equation (1) expresses the effect of this factor in
the process of computing the result [24].
iipremiseicon CFMembershipMembership ×= ,, (1)
This equation shows that the membership function of conclusion part in each rule i, is the result
of multiplying membership function of premise part by confidence factor of the relevant rule.
We can provide the preliminary of fuzzy system with complete knowledge about the quality
criteria and definition of input and output linguistic variables with equal terms. After that, we
obtain some category of fuzzy rules for each quality criterion, which in each category there are
some fuzzy rules that their number is equal to the number of input variable terms. In order to
express fuzzy rules, we create one logical mapping between input variable terms in premise part
and output variable in conclusion part for each category of rules. The effect of each rule in the
ranking process should be distinct by user. This work is done by means of getting the importance
grade of each quality criterion and located it as a confidence factor related to the one category of
rules. Therefore we define the importance grade as a number from 0 to 100 and by conversion of
distance is used as confidence factor.
In order to introduce fuzzy rules, we must create a logical mapping according to this point that
whether low or high value of variable is considerable for user. The fuzzy rules for cost variable
that low value of this variable is considerable for user can be expressed as follow:
CFcost IF Cost=very cheap THEN
Rank=very high
CFcost IF Cost=cheap THEN
Rank=high
CFcost IF Cost=moderate THEN
Rank=moderate
CFcost IF Cost=expensive THEN
Rank=low
CFcost IF Cost=very expensive THEN
Rank=very low
While, we express the fuzzy rules for availability variable that high value of this variable is
considerable for user as follow:
CFav IF Availability=very high THEN
Rank=very high
CFav IF Availability=high THEN
Rank=high
CFav IF Availability=moderate THEN
Rank=moderate
CFav IF Availability=low THEN
Rank=low
CFav IF Availability=very low THEN
Rank=very low

8
As pointed out above, we express (N=20) fuzzy rules for the fuzzy system that are according to
the numbers of system linguistic variables and variable terms, the number of these rules are
variable. These rules are criterion for evaluating different composite services.
4.3. A Genetic Algorithm based Optimization
By applying a GA-based approach the optimal solution (represented by its genotype)is
determined by simulating the evolution of an initial population (through generation)until survival
of best fitted individuals (here compositions) satisfying some constraints. The survivors are
obtained by crossover, mutation, selection of compositions from previous generations. Details of
GA parameterization follow:
• Genotype: it is defined by an array of integer. The number of items is equal to the
number tasks involved in the composition. Each item, in turn, contains an index to an array of
candidate services matching that task. Each composition, as a potential solution of the
optimization problem, can be encoded using this genotype (e.g., Figure 2 is encoding the
genotype of one composition).
Figure 2. Genotype encoding for service composition.
• Enhanced Initial Population: The value of every task in every chromosome is set
according to a local optimized method. The value of every task is QoS value of selected candidate
service. The larger QoS value of a candidate service is, the larger the probability to be selected is.
The probability of one candidate to be selected is the result of its QoS value divided by the sum of
QoS values of all candidates of same task.
• Fuzzy Constraints have to be met by compositions c e.g., Cost have to be cheap,
whereas the domain ontology it means Qco(c)<50 and Qco(c)>0 or response time at least have to
be moderate that it means Qrt(c)<38 and Qrt(c)>10.
• Fitness Function: Now, the problem can be modeled by means of a fitness function and
eventually, some constraints. The fitness function needs to maximize some QoS attributes (e.g.,
reliability), while minimizing others (e.g., cost). In our approach we define a new fitness
function f based defined fuzzy system.
),()( Rankinputfitcf = (2)
Where input is quality vector of a composition c and Rank is output result of fuzzy system. ∈lw
[0,1] is the weight assigned to the th
l quality criterion and ∑ ∈ },,,{ reavrtcol lw contrary to [10]

9
doesn’t have to be 1. Also QoS attribute factors ( },,,{ reavrtcolQ ∈ don't have to be normalized in the
interval [0,1]).
In addition f must drive the evolution towards constraint satisfaction. To this end compositions
that do not meet the constraints are penalized by (3).
2
},,,{
minmax
'
)
)()(
()()( ∑∈ −
∆
−=
avrertcol ll
l
pe
cQcQ
Q
wcfcf (3)
Where max
lQ , min
lQ are respectively the maximum and minimal value of the th
l quality
constraint, pew weights the penalty factor and },,,{ avrertcolQ ∈∆ is defined by:





<−
<<
<−
=∆
llll
lll
llll
l
QQifQQ
QQQif
QQifQQ
Q
minmin
maxmin
maxmax
0 (4)
Contrary to [5], compositions that violate constraints do not receive the same penalty. Indeed the
factor pew is further penalized in (3). This function avoids local optimal by considering also
compositions that disobey constraints. Unfortunately, (3) contains a penalty for candidate
compositions, which is the same at each generation. If, as usual, the weight pew for this penalty
factor is high, there is a risk that also candidate composition violating the constraints but "close"
to a good solution could be discarded.
The alternative is to adopt a dynamic penalty, i.e., a penalty having a weight that increases with
the number of generations. This allows, for the early generations, to also consider some
individuals violating the constraints. After a number of generations, the population should be able
to meet the constraints, and the evolution will try to improve only the rest of the fitness.
gen is the current generation, while maxgen is the maximum number of generations.
• Operators on Genotypes: they define authorized alterations on genotypes not only to
ensure evolution of compositions’ population along generations but also to prevent convergence
to local optimum. We use: i) composition mutation i.e., random selection of a task (i.e., a position
in the genotype) in a candidate composition and replacing its service with another one among
those available, ii) the standard two-points crossover i.e., randomly combination of two
compositions and iii) selection of compositions which is fitness-based i.e., compositions
disobeying the constraints are selected proportionally from previous generations.
• Stopping Criterion: it enables to stop the evolution of a population. First of all we
iterate until the constraints are met (i.e., 0=∆ lQ }),,,{ avrertcol ∈∀ within a maximum
number of generations. Once the latter constraints are satisfied we iterate until the best fitness
composition remains unchanged for a given number of generations.
4.4. Design of System
Figure 3 shows an aspect of the system. The system has several components which are described
below. The complete knowledge about quality criteria and then defining linguistic variables and
membership functions has a determinative role in fuzzification process of composite services. The

10
set of fuzzy rules which are proposed based on the logical mapping between the terms of
linguistic variables, are the criteria for evaluating the different composite services. But, these
rules are completely neutral against previous approaches of selecting suitable composition.
Therefore, the user’s role for preferring the rules that express his needs increases. As observed in
the figure, the received user preferences are based on the importance grade that is given to each
quality criterion. Then by changing distance, this numbers stated as confidence factors or weight
of each category of rules.
The plan generation unit produces all feasible plans based on workflow and presents services for
doing tasks. These plans can be limited by user constraints. For example, at composite service of
travel planner user can determine the maximum cost that he can pay for hotel or car rent and so,
infeasible execution plans will be omitted. In our system (FGS) these end compositions that do
not meet the constraints are penalized, but because they may be close to a good solution, they are
not discarded. On the other hand, aggregation functions for computing QoS of execution plans
formed quality gathering unit which create quality vector of each execution plan.
Finally, there is optimization of composition unit which works based on GA. Fuzzy rules, which
are created based on user preferences together with user constraints constitute one fitness
function. GA parameters are defined for the system. Quality vectors related to different
compositions are evaluated and one optimized solution is selected in accordance with user need
and convenience.
Figure 3. The general view of designed system.
5. EMPIRICAL STUDY
We implemented this approach by designing the FGS in MATLAB application and then run this
at XP operating system. The optimal compositions are computed by using an elitist GA where the
best 2 compositions were kept alive across generations, with a crossover probability of 0.7, a
mutation probability of 0.1, a population of 200 compositions. The roulette wheel selection has
been adopted as selection mechanism. We consider a simple stopping criterion i.e., up to 400

11
generations. We conducted experiments on Intel(R) Core(TM)2 CPU, 2.4GHz with 2GB RAM.
We conducted experiments on Intel(R) Core(TM)2 CPU, 2.4GHz with 2GB RAM.
5.1. Evaluation of Fitness Function
At first, we set confidence factor of each quality criterion equal to 1 and draw charts related to
changes of system variables. Figure 4 shows the changes of cost variable and results of these
changes on rank of composite services. The output level from the changes of input variables,
show the logical changes on rank of composite services.
Then, we decrease confidence factor that is related to this criterion (cost) and fix other criteria.
Now, we can observe that the chart gradient and width of ranking scores in each step of
confidence factor’s reduction, is lessened. This subject is true for other criteria and is a reason for
correct functionality of system. Figure 5 shows difference of maximum and minimum ranking
scores belong to composite services against changes of confidence factor related to one quality
criterion and fixing the other criteria in 1.
As observed here, the user’s point of view has direct effect on computed score for a composite
service. In fact, user can select a suitable composition through his point of view.
38
43
48
53
58
0 20 40 60 80 100
Cost
Rank
Figure 4. Effect of cost variable changing on rank variable.
0
5
10
15
20
25
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
confide nce factor
DifferenceofMax&Min
rankingscores
Figure 5. Effect of confidence factor changing on width of ranking scores.
5.2. Evolution of the Composition Quality
Figure 6 reports the evolution of the composition quality over the GA generations, by varying the
number of tasks. For each task, we considered 30 available candidate services. This illustrates
different levels of convergence to a composition that meets some constraints and optimizes its

12
different quality criteria by maximizing the availability and reliability while keeping low cost and
response time. For better evaluation, equal weights are assigned to the different quality criteria.
Figure 6. Evolution of the composition quality.
Table 2 and Figure 6 present the computation costs and the number of generations required to
obtain the maximal fitness value. The more the number of tasks, the more the amount of time it
takes to converge to the optimum. Obviously, the population size and the number of generations
should be extended to reach the optimum of more complex compositions.
Table 2. Overview of computation costs.
Tasks
Num.
Max. Fitness
(%)
Generation
Num.
Time (ms)
10 99 110 1230
20 97.5 267 2560
30 96 342 5540
5.3. Towards Large Scale Based Compositions
In this experiment we suggest to study the behavior of our approach regarding the optimization
with a large number of tasks (up to 500 tasks) and candidate services (500). To this end we focus
on its scalability and the impact of the number of generations as well as the population size on the
GA success.
Table 3. Large scale compositions.
Tasks
Num.
Max. Fitness
(%)
Generation Num./
Population size
Time (ms)
100
87
98
400/200
700/400
4350
9543
300
52
96
400/200
1500/500
5836
20568
500
27
95
400/200
3000/1000
8056
55655
As illustrated in Table 3, increasing both the number of generations and the population size does
actually result in better fitness values for problems with a larger number of tasks and candidate
services. For example, regarding the optimization of a composition of 500 tasks with 500
candidate services, a number of generations of 400 and a population size of 200 do result in a low
fitness value of 27% of the maximum, whereas considering a number of generations of 3000 and

13
a population size of 1000 achieve 95% of the maximum. Note that better fitness values can be
reached by further increasing the sizes of generations and populations.
5.4. Convergence of GA-based approaches
In this experiment, we compare the convergence of FGS with the main alternative at present [10].
Also for each task, there is 30 available candidate services.
Table 4. Comparing GA-based approaches (population size of 200).
Tasks
Num.
Approach Max.
Fitness (%)
Generation
Num.
Time
(ms)
10
FGS
[10]
99
98
110
156
1230
1350
20
FGS
[10]
97.5
93
267
425
2560
2865
30
FGS
[10]
96
84
342
596
5540
6570
According to Table 4, the advantage of FGS is twofold. Firstly we obtain better fitness values for
the optimal composition than the approach of [10]. Secondly, our approach converges faster than
the approach of [10]. In addition FGS avoids getting trapped by local optimums by i) further
penalizing compositions that disobey constraints (the factor of in (3) and (5)) and ii)
suggesting a dynamic penalty, i.e., a penalty having a weight that increases with the number of
generations. These results support the adoption of FGS in the cases where a large number of tasks
and services are considered.
A linear increase in time for increasing numbers of iterations for the GA which is proposed in
[10] and FGS are shown in Figure 7. Due to the additional time needed for the fuzzy component,
FGS shows the larger increase, but because of need to less iteration it is more valuable.
Figure 7. Execution times obtained using GA and FGS.
6. CONCLUSIONS
This paper proposed a hybrid Fuzzy-guided Genetic Algorithm system for QoS-aware service
composition, i.e., to determine a set of candidate services to be bound to abstract services
contained in a composition to meet a set of fuzzy constraints and to optimize a fitness criterion on

14
QoS attributes. In the GA optimization, the fitness function is a fuzzy system that is constructed
based on user preferences.
Several advantages can be stated for this approach. This approach emphasizes on accordance to
the user preferences and quality properties of composite service. The user clearly states his
preferences on the other hand he states many fuzzy rules. Also, in addition to high care in
expression of preferences, for modeling different user preferences there is no need to restate the
rules. User presents fuzzy constraints and don’t have to know details of information about QoS
values. Also, this system is extensible against increasing the quality criteria. Compared with GA,
this approach has better fitness values and faster convergence and more accuracy.
Finally, in future work which is user-centric in order to select the optimal web service
composition, we will consider composition of one technique such as simulated annealing or
migrating birds with fuzzy logic and survey their results through further experiments.
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Authors
M. Bakhshi received his B.Sc in computer engineering from Shahid Bahonar University of
Kerman, Iran, his M.Sc degree in software engineering from Islamic Azad University of
Najaf Abad, Iran. Currently he is faculty of Islamic Azad University, shahrbabak branch.
His interests include Web Service technology and coordination problem. He is working on
dynamic choreography models for Web services in B2B Corporation.
Dr. S. M. Hashemi received his M.S. degree in Computer Science from Amirkabir
University of Technology in 2003, and his PhD degree in Computer Science from the Azad
University in 2009. Moreover, he is currently a faculty member at Science and Research
Branch, Azad University, Tehran. His current research interests include Software Intensive
Systems, Global Village Services, Grid Computing, Agile Enterprise Architecting through
ISRUP, and Globalization Governance through IT/IS Services.

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