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Book ChapterDOI

Optimization of Location Allocation of Web Services Using a Modified Non-dominated Sorting Genetic Algorithm

02 Feb 2016-pp 246-257

TL;DR: A multi-objective optimization algorithm based on NSGA-II based algorithm can provide a set of best solutions that outperforms genetic algorithm to solve the service location-allocation problem.

AbstractIn recent years, Web services technology is becoming increasingly popular because of the convenience, low cost and capacity to be composed into high-level business processes. The service location-allocation problem for a Web service provider is critical and urgent, because some factors such as network latency can make serious effect on the quality of service QoS. This paper presents a multi-objective optimization algorithm based on NSGA-II to solve the service location-allocation problem. A stimulated experiment is conducted using the WS-DREAM dataset. The results are compared with a single objective genetic algorithm GA. It shows NSGA-II based algorithm can provide a set of best solutions that outperforms genetic algorithm.

Topics: Web service (60%), Genetic algorithm (56%), Service (business) (56%), Quality of service (55%), Multi-objective optimization (53%)

Summary (3 min read)

1 Introduction

  • Web Services are considered as self-contained, self-describing, modular applications that can be published, located, and invoked across the Web [25].
  • With the ever increasing number of functional similar web services being available on the Internet, the web service providers (WSPs) are trying to improve the quality of service (QoS) to become competitive in the market.
  • This algorithm solves the problem with one objective and one constraint.
  • Among numerous EMO algorithms, Non-dominated sorting GA (NSGA-II) [4], Strength Pareto Evolutionary Algorithm 2 (SPEA-2) [5] have become standard approaches.
  • – To model the web service location-allocation problem so that it can be tackled by NSGA-II, also known as The main objectives are.

2 Background

  • In a GA, a population of strings (called chromosomes or the genotype of the genome), which are encoded as candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem, evolves towards better solutions.
  • Since a chromosome from the population represents a solution, when the algorithm starts, the whole population moves like one group towards an optimal area so the GA searches from a population of solutions rather than a single solution.
  • Integer scalarization technique [3] is used to solve multi-objective problems with GA.
  • When used for problems with only two objectives, NSGA-II performs relatively well in both convergence and computing speed.
  • Parents are selected from the population by using tournament selection based on the rank and the crowding distance.

3.1 Problem Description and Assumptions

  • Web service location-allocation problem is to determine reasonable locations for web services so that the deployment cost of WSP can be minimized while service performance can be optimized.
  • In addition to deciding locations to of the services, information about network latency between user centers and candidate locations are needed.
  • Therefore, the average network latency for a period of time should be representative.
  • The details of these input data and modeling are introduced in Section 3.2.
  • The authors made an assumption that WSPs periodicaly change the web service deployment since the user centers are changing over time.

3.2 Model Formulation

  • For service location-allocation problem, the authors need information of service usage, network latency, and service deployment cost to decide service location-allocation so that the overall network latency can be minimized with minimal deployment cost and within constraints.
  • The authors will use the following matrices.
  • The network latency between user center i2 with candidate location j1 is 5.776s.
  • These data could be collected by monitoring network latencies [31] [32].
  • If there is more than one location, then the smallest latency is selected.

4.1 Chromosome Representation and Constraints

  • The constraint setting is based on service providers’ needs.
  • One can set multiply constraints to the problem to narrow the potential searching space.
  • In their case, the authors set two basic constraints.
  • The first constraint service number constraint requires that each service is deployed in at least one location.
  • ∑ x∈S axj ≥ 1 (2) The second constraint, is cost constraint, which sets up the upper boundary of the total cost.

4.2 Genetic Operators

  • The original NSGA-II uses a simulated binary crossover (SBX) [2] and polynomial mutation [24] to cope with continuous problems.
  • The crossover point is created randomly within the length of the chromosome.
  • In order to accomplish these two objectives, the authors design two fitness functions to evaluate how good each chromosome meets the objectives.
  • The authors assume the latency is symmetrical between user center and candidate location.
  • CostF itness′ = CostF itness− Costmin Costmax − Costmin (6) LatencyF itness′ = LatencyF itness− Latencymin Latencymax − Latencymin (7).

4.3 NSGA-II based algorithm for service location-allocation

  • Comparing with the original NSGA-II their proposed algorithm has two new features.the authors.
  • Firstly, in order to avoid repeatly evaluating the fitness of chromosomes, after the first generation is initialized, it storing the Pareto front in the memory.
  • The reason for setting a memory pool is that, after a number of iterations, the population start converging.
  • Secondly, it uses general mutation and crossover operation instead of polynomial mutation and simulated binary crossover.
  • Therefore, repair operators are needed to try to maintain feasible solutions.

5 GA for Web Service Location Allocation

  • In order to show the advantages of their multi-objective NSGA-II based approach, the authors extend the single-objective GA based approach in [13] to consider two objectives.
  • The authors employ integer scalarization technique [9] to transform the multi-objective problem into a single objective problem.
  • In their approach, the authors define the weight equals 0.5 since they consider both objectives equally important.
  • Crossover and mutation operators are same as defined in Section 4.2.
  • Note that CostFitness and LatencyFitness are calculated using Formula 6 and 7 in section 4.2.

6 Experiment Evaluation

  • To evaluate the effectiveness and efficiency of their proposed NSGA-II based approach to service location-allocation.
  • The authors compare their approach with the GA-based single objective approach in Section 5 using an existing dataset, WS-DREAM [31] [32], which is a historical dataset on QoS of Web services from different locations.
  • In each dataset, algorithms are run under four different levels of cost constraints: Sufficient condition (indicating 100% the expected total cost), good condition (70%), pool condition (40%) and minimum budget condition (0%).
  • In addition to the comparison between NSGA-II based algorithm and GA based algorithm, the authors conducted full experiments on NSGA-II with an initialisation and GA with an initialisation.
  • The authors initialized the population with 49 random chromosomes and a chromosome which leading to minimum cost.

6.1 Effectiveness comparison

  • The authors conducted experiments on NSGA-II, GA, NSGA-II with initialisation and GA with initialisation respectively.
  • The authors goal is to minimize both cost and latency.
  • The authors also notice that even though the population size is small as 50, including a chromosome of optimal cost can help to narrow down searching space and to converge to optimal solution faster.
  • The results from initialized algorithms are similar with uninitialized algorithms, therefore the authors only present the uninitialized results.

7 Conclusion

  • The authors proposed a NSGA-II based approach to web service location-allocation problem.
  • The authors approach consider two objectives, minimizing cost and minimizing network latency at the same time.
  • The authors have conducted a full experimental evaluation using the public WS-DREAM dataset to compare their approach to single-objective GA-based approach.
  • The experimental results shows the NSGA-II based approach is effective to produce a set near-optima solutions for the web service location-allocation problem.

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Optimization of Location Allocation of Web
Services Using A Modified Non-dominated
Sorting Genetic Algorithm
Boxiong Tan, Hui Ma, Mengjie Zhang
School of Engineering and Computer Science,
Victoria University of Wellington, New Zealand
{Boxiong.Tan, Hui.Ma, Mengjie.Zhang}@ecs.vuw.ac.nz
Abstract. In recent years, web services technology is becoming increas-
ingly popular because of the convenience, low cost and capacity to be
composed into high-level business processes. The service location-allocation
problem for a web service provider is critical and urgent, because some
factors such as network latency can make serious effect on the quality of
service (QoS). This paper presents a multi-objective optimization algo-
rithm based on NSGA-II to solve the service location-allocation problem.
A stimulated experiment is conducted using the WS-DREAM dataset.
The results are compared with a single objective genetic algorithm (GA).
It shows NSGA-II based algorithm can provide a set of best solutions
that outperforms genetic algorithm.
1 Introduction
Web Services are considered as self-contained, self-describing, modular appli-
cations that can be published, located, and invoked across the Web [25]. In recent
years, web services technology is becoming increasingly popular because of the
convenience, low cost [1] and capacity to be composed into high-level business
processes.
With the ever increasing number of functional similar web services being avail-
able on the Internet, the web service providers (WSPs) are trying to improve the
quality of service (QoS) to become competitive in the market. QoS, also known
as non-functional requirements to web services, is the degree to which a service
meets specified requirements or web service users’ needs [33], such as response
time, security and availability. Among numerous QoS measurements, service re-
sponse time is a critical factor for many real-time services, e.g. traffic service or
finance service. Service response time has two components: transmission time
(variable with message size) and network latency [18]. Study [17] shows that
network latency is a significant component of service response delay. Ignoring
network latency will underestimate response time by more than 80 percent [26],
since network latency is related to network topology as well as physical distance
[14]. To reduce the network latency WSPs need to allocate services location
where has the lower latency to the user center that access the services. User cen-
ter denotes a geometric location (e.g., a city) that is encompassed by a service

area. Service area, also known as trade area, is a region where web service users
concentrated at. There are many ways to identify service areas such as using
IP information or user generated location data [23]. Ideally, WSPs could deploy
their services to each user center in order to provide the best quality. However,
the more services deployed, the better the quality and the higher cost.
The Web service location-allocation problem is essentially a multi-objective
optimization problem [3], for which there are two conflict objectives, to provide
optimal QoS to web service users and to consume minimal deployment cost. This
problem can be classified as a multidimensional knapsack problem (MKP), there-
fore, it is considered NP-hard due to the fact that the combinatorial explosion
of the search space [27].
Very few researches have studied the service location-allocation problem and
most of the researchers treat this problem as a single objective problem. [1] [26]
try to solve the problem by using integer linear programming techniques. In par-
ticular, [26] solved this problem by employing greedy and linear relaxations of
Integer transpotation problem. However, integer programming (IP) is very effec-
tive for small-scale or mid-scale MKP but suffers from large memory requirement
for large-scale MKP [15]. Huang [12] proposed an enhanced genetic algorithm
(GA)-based approach, which make use of the integer scalarization technique to
solve this problem. This algorithm solves the problem with one objective and
one constraint. However there are some deficiencies in the integer scalarization
techniques [3]. Firstly, decision makers need to choose appropriate weights for
the objectives to retrieve a satisfactorily solution. Secondly, non-convex parts
of the Pareto set cannot be reached by minimizing convex combinations of the
object functions.
So far, to the best of our knowledge, there is no research has considered the
service location-allocation problem as a multi-objective problem. Therefore, this
paper we will treat service location-allocation problem as a multi-objective prob-
lem. Evolutionary multi-objective optimization (EMO) methodologies is ideal for
solving multi-objective optimization problems [7], since EMO works with a pop-
ulation of solutions and a simple EMO can be extended to maintain a diverse
set of solutions. With an emphasis for moving toward the true Pareto-optimal
region, an EMO can be used to find multiple Pareto-optimal solutions in one
single simulation run [19]. Among numerous EMO algorithms, Non-dominated
sorting GA (NSGA-II) [4], Strength Pareto Evolutionary Algorithm 2 (SPEA-2)
[5] have become standard approaches. Some schemes based on particle swarm
optimization (PSO) approaches are also proposed [10] [13]. NSGA-II is one of
the most widely used methods for generating the Pareto frontier, because it can
keep diversity without specifying any additional parameters [6]. In this paper,
we propose to use NSGA-II to solve the web service location-allocation problem,
which has two objectives, to minimize cost and deployment network latency.
The aim of this project is to propose a NSGA-II based approach to produce
a set of near optimal solutions of service location-allocation, so that cost and
overall network latency are close to minimum. Then, the WSPs could use the

algorithm which proposed by this paper, to select an optimal plan based on their
funds. The main objectives are:
To model the web service location-allocation problem so that it can be tack-
led by NSGA-II.
To develop a NSGA-II based approach to the web service location-allocation
problem.
To evaluate our proposed approach using some existing datasets.
In Section 2 we introduce the background of NSGA-II and GA. In Section 3
we provide models of the service location allocation problems. Section 4 develops
a NSGA-II based algorithm. The experimental design and results evaluation are
shown in Section 6. Section 7 provides a brief summary.
2 Background
GA [21] is a powerful tool to solve combinatorial optimization problems. It is
an iterative procedure based on a constant-size population. In a GA, a popula-
tion of strings (called chromosomes or the genotype of the genome), which are
encoded as candidate solutions (called individuals, creatures, or phenotypes) to
an optimization problem, evolves towards better solutions. Each genome is as-
sociated with a fitness value based on a fitness function that indicates how close
it comes to meeting the overall specification, when compared to other genomes
in the population. The fitness value of an individual is also an indication of its
chances of survival and reproduction in the next generation. A typical genetic
algorithm requires a genetic representation of the solution domain and a fitness
function to evaluate the solution domain. Since a chromosome from the pop-
ulation represents a solution, when the algorithm starts, the whole population
moves like one group towards an optimal area so the GA searches from a popu-
lation of solutions rather than a single solution. Integer scalarization technique
[3] is used to solve multi-objective problems with GA. It predefines a weight for
each objective.
NSGA-II is a multi-objective algorithm based on GA. When used for problems
with only two objectives, NSGA-II performs relatively well in both convergence
and computing speed. However, NSGA-II has been criticized for its high compu-
tational cost and bad performance on applications with more than two objectives
[8].
NSGA-II permits a remarkable level of flexibility with regard to performance
assessment and design specification. NSGA-II assumes that every chromosome in
the population has two attributes: a non-domination rank in the population and
a local crowding distance in the population. The goal of NSGA-II is to converge
to the Pareto front as possible and with even spread of the solutions on the front
by controlling the two attributes.
The algorithm starts with a random initialization population. Once the pop-
ulation is sorted based on non-domination sorting, a rank is assigned to each
chromosome. Then, a parameter called crowding distance is calculated for each
individual. The crowding distance is a measure of how close an individual is to

its neighbors. A large average crowding distance will result in better diversity in
the population.
Parents are selected from the population by using tournament selection based
on the rank and the crowding distance. An individual is selected in the rank if it
is smaller than the other or if the crowding distance is greater than the other. The
selected population generates offsprings using crossover and mutation operators.
The population with the current population and current offsprings is sorted
again based on non-domination and only the best N individuals are selected,
where N is the population size. The selection is based on rank and the on crowd-
ing distance on the last front.
3 Problem Description and Modeling
In this section, we first describe the service location-allocation problem, then
we will present models for the services location allocation problem.
3.1 Problem Description and Assumptions
Web service location-allocation problem is to determine reasonable locations for
web services so that the deployment cost of WSP can be minimized while service
performance can be optimized. In this paper, to optimize service performance
we consider to minimize network latency.
The task of service location allocation has two objectives:
To minimize the total cost of the services.
To minimize the total network latency of the services.
To model the service location-allocation problem, we consider the following
assumptions:
Stakeholder Web Service Providers We consider the problem faced by a
WSP who has existing facilities but wishes to use the collected data to re-allocate
their services in order to maximum their profit.
The WSP must decide on facility locations from a finite set of possible loca-
tions. In order to make a decision, the WSP must first analyze the data collected
from current use of services. The collected data should include the records of in-
vocations from each unique IP address. Therefore, based on these data, the WSP
could summarize several customer demands concentrated on n discrete nodes [1],
namely user centers. We assume that the WSP has already done this step and a
list of user centers and candidate locations are given. Candidate location is the
geometric location that is suitable to deploy services. Candidate locations are
selected based on other criterions such as facilities or deployment cost. User cen-
ters and candidate locations can be overlapping, in fact, since web serivce users
receive best QoS services if the web serivces are deployed locally. Therefore, the
WSPs would like to choose user centers as candidate locations. In addition to
deciding locations to of the services, information about network latency between
user centers and candidate locations are needed.
The list below shows some critical information that should be providered by
the WSPs.
1. A list of user centers

2. A list of candidate locations
3. Service invocation frequency from user centers to services
4. Average network latency from user centers to candidate locations
5. Web service deployment cost for each candidate location
Worth nothing that service invocation frequency are changing over time. For
example, a service was popular in some regions may be unfrequented after a few
months. That’s the main reason for WSPs re-allocate their services. Network
latency highly depends on the network traffic and may be very different during
periods of a day. However, as long as there is no significant changes in the
network topology, the average network latency remain stable. Therefore, the
average network latency for a period of time should be representative.
These are the main input data that the decision making is dependent on. The
details of these input data and modeling are introduced in Section 3.2. Section
6 discussed the data that we used in the experiment.
Static deployment vs. Dynamic deployment As virtual machine technol-
ogy and infrastracture-as-a-service (IaaS) are becoming more and more popular.
Dynamic web service deployment become possible [20]. On the other hand, static
deployment is still the mainstream because of a majority of web serivce are de-
ployed on local infrastracture [11]. In this paper, we made an assumption that
WSPs periodicaly change the web service deployment since the user centers are
changing over time.
3.2 Model Formulation
To model service location-allocation problem, we need to make use of a set of
matrices, to present input information and output solutions.
For service location-allocation problem, we need information of service usage,
network latency, and service deployment cost to decide service location-allocation
so that the overall network latency can be minimized with minimal deployment
cost and within constraints. Assume a set of S = {s
1
, s
2
, ...s
s
, s
x
} services are re-
quested from a set of locations I = {i
1
, i
2
, ...i
i
, i
y
}. The service providers allocate
services to a set of candidate facility locations J = {j
1
, j
2
, ...j
j
, j
z
}.
In this paper, we will use the following matrices.
Matrices
L server network latency matrix L = {l
ij
}
A service location matrix A = {a
sj
}
F service invocation frequency matrix F = {f
is
}
C cost matrix C = {c
sj
}
R user response time matrix R = {r
is
}
service invocation frequency matrix, F = [f
is
], is used to record services invo-
cation frequencies from user centers, where f
is
is an integer that indicates the
number of invocations in a period of time from a user center to a service. For
example, f
13
= 85 denotes service s
1
is called 85 times in a predefined period of
time.

Citations
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Journal ArticleDOI
TL;DR: This paper develops a new PSO-based algorithm to provide a set of trade-off solutions for Web Service Location Allocation Problem and shows that the new algorithm can provide a more diverse range of solutions than the compared three well known multi-objective optimization algorithms.
Abstract: With the ever increasing number of functionally similar web services being available on the Internet, the market competition is becoming intense. Web service providers (WSPs) realize that good Quality of Service (QoS) is a key of business success and low network latency is a critical measurement of good QoS. Because network latency is related to location, a straightforward way to reduce network latency is to allocate services to proper locations. However, Web Service Location Allocation Problem (WSLAP) is a challenging task since there are multiple objectives potentially conflicting with each other and the solution search space has a combinatorial nature. In this paper, we consider minimizing the network latency and total cost simultaneously and model the WSLAP as a multi-objective optimization problem. We develop a new PSO-based algorithm to provide a set of trade-off solutions. The results show that the new algorithm can provide a more diverse range of solutions than the compared three well known multi-objective optimization algorithms. Moreover, the new algorithm performs better especially on large problems.

16 citations


Cites methods or result from "Optimization of Location Allocation..."

  • ...Lastly, we conduct an experiment considering the overall performance of a BMOPSOCDwith a dynamic rounding function in comparison with three other algorithms: PSO, BNSPSO andNSGA-II (see [12])....

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  • ...compare its performance with BNSPSO, BPSO from our previous research [11], and NSGA-II from [12]....

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  • ...linear normalization method described in [12]....

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  • ...Therefore, we further investigated a Pareto front approach [12] with Sorting Genetic Algorithm-II (NSGA-II)....

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TL;DR: This paper considers container consolidation as one multi-objective optimization problem with the objectives of minimizing the total energy consumption and minimize the total number of container migrations within a certain period of time and presents an NSGA-II based algorithm to find solutions for the container consolidation problem.
Abstract: In recent years, container-based clouds are becoming increasingly popular for their lightweight nature. Existing works on container consolidation mainly focus on reducing the energy consumption of cloud data centers. However, reducing energy consumption often results in container migrations which have big impact on the performance (i.e. availability) of applications in the containers. In this paper, we consider container consolidation as one multi-objective optimization problem with the objectives of minimizing the total energy consumption and minimizing the total number of container migrations within the certain period of time and present an NSGA-II based algorithm to find solutions for the container consolidation problem. Our experimental evaluation based on the real-world workload demonstrates that our proposed approach can lead to further energy saving and significant reduction of container migrations at the same time compared with some existing approaches.

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Proceedings ArticleDOI
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Cites background or methods from "Optimization of Location Allocation..."

  • ...The network latency matrix [7] L = [luv] is used to model the round trip latency between each user group location and the location of a candidate VM, where luv is a float that indicates the number of seconds of the network latency....

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  • ...An NSGAII based approach is proposed in [7]....

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  • ...[7], [5] treat the problem of application deployment as location allocation problem with an aggregated objective that minimises the total cost and latency....

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  • ...Previous works have employed these techniques in the context of cloud deployment, however they either fail to consider multiple clouds [6] or neglect the use of local search to further optimise solutions [5], [7]....

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  • ...Due to its powerfulness of finding wide-spread solutions and implementation simplicity [13], NSGA-II has been successfully applied in many real-world multi-objective combinatorial problems such as web service allocation [14], service composition [15], [16] and resource allocation in clouds [17], [18]....

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References
More filters

Journal ArticleDOI
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Abstract: Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN/sup 3/) computational complexity (where M is the number of objectives and N is the population size); (2) their non-elitism approach; and (3) the need to specify a sharing parameter. In this paper, we suggest a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN/sup 2/) computational complexity is presented. Also, a selection operator is presented that creates a mating pool by combining the parent and offspring populations and selecting the best N solutions (with respect to fitness and spread). Simulation results on difficult test problems show that NSGA-II is able, for most problems, to find a much better spread of solutions and better convergence near the true Pareto-optimal front compared to the Pareto-archived evolution strategy and the strength-Pareto evolutionary algorithm - two other elitist MOEAs that pay special attention to creating a diverse Pareto-optimal front. Moreover, we modify the definition of dominance in order to solve constrained multi-objective problems efficiently. Simulation results of the constrained NSGA-II on a number of test problems, including a five-objective, seven-constraint nonlinear problem, are compared with another constrained multi-objective optimizer, and the much better performance of NSGA-II is observed.

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"Optimization of Location Allocation..." refers methods in this paper

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Book
01 Jan 1996
TL;DR: An Introduction to Genetic Algorithms focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.
Abstract: From the Publisher: "This is the best general book on Genetic Algorithms written to date. It covers background, history, and motivation; it selects important, informative examples of applications and discusses the use of Genetic Algorithms in scientific models; and it gives a good account of the status of the theory of Genetic Algorithms. Best of all the book presents its material in clear, straightforward, felicitous prose, accessible to anyone with a college-level scientific background. If you want a broad, solid understanding of Genetic Algorithms -- where they came from, what's being done with them, and where they are going -- this is the book. -- John H. Holland, Professor, Computer Science and Engineering, and Professor of Psychology, The University of Michigan; External Professor, the Santa Fe Institute. Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

9,645 citations


"Optimization of Location Allocation..." refers methods in this paper

  • ...Our problem is discretized, therefore we use the binary GA mutation and crossover operations [18]....

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Journal ArticleDOI
TL;DR: A new Web services discovery model is proposed in which the functional and non-functional requirements are taken into account for the service discovery and should give Web services consumers some confidence about the quality of service of the discovered Web services.
Abstract: Web services technology has generated a lot interest, but its adoption rate has been slow. This paper discusses issues related to this slow take up and argues that quality of services is one of the contributing factors. The paper proposes a new Web services discovery model in which the functional and non-functional requirements (i.e. quality of services) are taken into account for the service discovery. The proposed model should give Web services consumers some confidence about the quality of service of the discovered Web services.

1,068 citations


"Optimization of Location Allocation..." refers background in this paper

  • ...Web service deployment cost for each candidate location Worth nothing that service invocation frequency are changing over time....

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  • ...Web Services are considered as self-contained, self-describing, modular applications that can be published, located, and invoked across the Web [25]....

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  • ...We compare our approach with the GA-based single objective approach in Section 5 using an existing dataset, WS-DREAM [31] [32], which is a historical dataset on QoS of Web services from different locations....

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  • ...Web service location-allocation problem is to determine reasonable locations for web services so that the deployment cost of WSP can be minimized while service performance can be optimized....

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  • ...The Web service location-allocation problem is essentially a multi-objective optimization problem [3], for which there are two conflict objectives, to provide optimal QoS to web service users and to consume minimal deployment cost....

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Journal ArticleDOI
Abstract: This paper introduces genetic algorithms (GA) as a complete entity, in which knowledge of this emerging technology can be integrated together to form the framework of a design tool for industrial engineers. An attempt has also been made to explain "why" and "when" GA should be used as an optimization tool.

795 citations


01 Jan 1996

773 citations


"Optimization of Location Allocation..." refers methods in this paper

  • ...GA [21] is a powerful tool to solve combinatorial optimization problems....

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Frequently Asked Questions (2)
Q1. What are the contributions mentioned in the paper "Optimization of location allocation of web services using a modified non-dominated sorting genetic algorithm" ?

The service location-allocation problem for a web service provider is critical and urgent, because some factors such as network latency can make serious effect on the quality of service ( QoS ). This paper presents a multi-objective optimization algorithm based on NSGA-II to solve the service location-allocation problem. 

Future work will investigate the scalability of their proposed approaches for big datasets.