Software testing is the primary phase, which is performed during software development and it is carried by a sequence of instructions of test inputs followed by expected output. Evolutionary algorithms are most popular in the computational field based on population. The test case generation process is used to identify
test cases with resources and also identifies critical domain requirements. The behavior of bees is based on
population and evolutionary method. Bee Colony algorithm (BCA) has gained superiority in comparison to other algorithms in the field of computation. The Harmony Search (HS) algorithm is based on the enhancement process of music. When musicians compose the harmony through different possible combinations of the music, at that time the pitches are stored in the harmony memory and the optimization
can be done by adjusting the input pitches and generate the perfect harmony. Particle Swarm Optimization (PSO) is an intelligence based meta-heuristic algorithm where each particle can locate their source of food at different position.. In this algorithm, the particles will search for a better food source position in the hope of getting a better result. In this paper, the role of Artificial Bee Colony, particle swarm optimization
and harmony search algorithms are analyzed in generating random test data and optimized those test data.
Test case generation and optimization through bee colony, PSO and harmony search (HS) algorithms which are applied through a case study, i.e., withdrawal operation in Bank ATM and it is observed that these algorithms are able to generate suitable automated test cases or test data in a client manner. This
section further gives the brief details and compares between HS, PSO, and Bee Colony (BC) Optimization
methods which are used for test case or test data generation and optimization.
EVOLUTIONARY COMPUTING TECHNIQUES FOR SOFTWARE EFFORT ESTIMATIONijcsit
Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
HYBRID DATA CLUSTERING APPROACH USING K-MEANS AND FLOWER POLLINATION ALGORITHMaciijournal
Data clustering is a technique for clustering set of objects into known number of groups. Several approaches are widely applied to data clustering so that objects within the clusters are similar and objects in different clusters are far away from each other. K-Means, is one of the familiar center based clustering algorithms since implementation is very easy and fast convergence. However, K-Means algorithm suffers from initialization, hence trapped in local optima. Flower Pollination Algorithm (FPA) is the global optimization technique, which avoids trapping in local optimum solution. In this paper, a novel hybrid data clustering approach using Flower Pollination Algorithm and K-Means (FPAKM) is proposed. The proposed algorithm results are compared with K-Means and FPA on eight datasets. From the experimental results, FPAKM is better than FPA and K-Means.
Artificial Bee Colony (ABC) is a swarm
optimization technique. This algorithm generally used to solve
nonlinear and complex problems. ABC is one of the simplest
and up to date population based probabilistic strategy for
global optimization. Analogous to other population based
algorithms, ABC also has some drawbacks computationally
pricey due to its sluggish temperament of search procedure.
The solution search equation of ABC is notably motivated by a
haphazard quantity which facilitates in exploration at the cost
of exploitation of the search space. Due to the large step size in
the solution search equation of ABC there are chances of
skipping the factual solution are higher. For that reason, this
paper introduces a new search strategy in order to balance the
diversity and convergence capability of the ABC. Both
employed bee phase and onlooker bee phase are improved
with help of a local search strategy stimulated by memetic
algorithm. This paper also proposes a new strategy for fitness
calculation and probability calculation. The proposed
algorithm is named as Improved Memetic Search in ABC
(IMeABC). It is tested over 13 impartial benchmark functions
of different complexities and two real word problems are also
considered to prove proposed algorithms superiority over
original ABC algorithm and its recent variants
Artificial Bee Colony (ABC) is a distinguished optimization strategy that can resolve nonlinear and multifaceted problems. It is comparatively a straightforward and modern population based probabilistic approach for comprehensive optimization. In the vein of the other population based algorithms, ABC is moreover computationally classy due to its slow nature of search procedure. The solution exploration equation of ABC is extensively influenced by a arbitrary quantity which helps in exploration at the cost of exploitation of the better search space. In the solution exploration equation of ABC due to the outsized step size the chance of skipping the factual solution is high. Therefore, here this paper improve onlooker bee phase with help of a local search strategy inspired by memetic algorithm to balance the diversity and convergence capability of the ABC. The proposed algorithm is named as Improved Onlooker Bee Phase in ABC (IoABC). It is tested over 12 well known un-biased test problems of diverse complexities and two engineering optimization problems; results show that the anticipated algorithm go one better than the basic ABC and its recent deviations in a good number of the experiments.
Artificial Bee Colony (ABC) algorithm is a Nature
Inspired Algorithm (NIA) which based on intelligent food
foraging behaviour of honey bee swarm. This paper introduces
a local search strategy that enhances exploration competence
of ABC and avoids the problem of stagnation. The proposed
strategy introduces two new local search phases in original
ABC. One just after onlooker bee phase and one after scout
bee phase. The newly introduced phases are inspired by
modified Golden Section Search (GSS) strategy. The proposed
strategy named as new local search strategy in ABC
(NLSSABC). The proposed NLSSABC algorithm applied over
thirteen standard benchmark functions in order to prove its
efficiency.
A Heuristic Approach for optimization of Non Linear process using Firefly Alg...IJERA Editor
A comparison study of Firefly Algorithm (FA) and Bacterial Foraging Algorithm (BFO) optimization is carried out by applying them to a Non Linear pH neutralization process. In process control engineering, the Proportional, Derivative, Integral controller tuning parameters are deciding the performance of the controller to ensure the good performance of the plant. The FA and BFO algorithms are applied to obtain the optimum values of controller parameters. The performance indicators such as servo response and regulatory response tests are carried out to evaluate the efficiency of the heuristic algorithm based controllers. The error minimization criterion such as Integral Absolute Error (IAE), Integral Square Error (ISE), Integral Time Square Error (ITSE), Integral Time Absolute Error (ITAE) and Time domain specifications – rise time, Peak Overshoot and settling time are considered for the study of the performance of the controllers. The study indicates that, FA tuned PID controller provides marginally better set point tracking, load disturbance rejection, time domain specifications and error minimization for the Non Linear pH neutralization process compared to BFO tuned PID controller.
The document discusses a proposed Randomized Memetic Artificial Bee Colony (RMABC) algorithm for optimization problems. RMABC incorporates local search techniques into the Artificial Bee Colony algorithm to improve exploitation of promising solutions. It randomizes the step size in the local search to balance diversification and intensification. Experimental results on benchmark problems show RMABC outperforms other ABC algorithm variants in finding optimal solutions. The document provides background on optimization problems, nature-inspired algorithms, Artificial Bee Colony algorithm, and Memetic algorithms.
Artificial Bee Colony (ABC) algorithm is a Nature Inspired Algorithm (NIA) which based in intelligent food foraging behaviour of honey bee swarm. ABC outperformed over other NIAs and other local search heuristics when tested for benchmark functions as well as factual world problems but occasionally it shows premature convergence and stagnation due to lack of balance between exploration and exploitation. This paper establishes a local search mechanism that enhances exploration capability of ABC and avoids the dilemma of stagnation. With help of recently introduces local search strategy it tries to balance intensification and diversification of search space. The anticipated algorithm named as Enhanced local search in ABC (EnABC) and tested over eleven benchmark functions. Results are evidence for its dominance over other competitive algorithms.
EVOLUTIONARY COMPUTING TECHNIQUES FOR SOFTWARE EFFORT ESTIMATIONijcsit
Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
HYBRID DATA CLUSTERING APPROACH USING K-MEANS AND FLOWER POLLINATION ALGORITHMaciijournal
Data clustering is a technique for clustering set of objects into known number of groups. Several approaches are widely applied to data clustering so that objects within the clusters are similar and objects in different clusters are far away from each other. K-Means, is one of the familiar center based clustering algorithms since implementation is very easy and fast convergence. However, K-Means algorithm suffers from initialization, hence trapped in local optima. Flower Pollination Algorithm (FPA) is the global optimization technique, which avoids trapping in local optimum solution. In this paper, a novel hybrid data clustering approach using Flower Pollination Algorithm and K-Means (FPAKM) is proposed. The proposed algorithm results are compared with K-Means and FPA on eight datasets. From the experimental results, FPAKM is better than FPA and K-Means.
Artificial Bee Colony (ABC) is a swarm
optimization technique. This algorithm generally used to solve
nonlinear and complex problems. ABC is one of the simplest
and up to date population based probabilistic strategy for
global optimization. Analogous to other population based
algorithms, ABC also has some drawbacks computationally
pricey due to its sluggish temperament of search procedure.
The solution search equation of ABC is notably motivated by a
haphazard quantity which facilitates in exploration at the cost
of exploitation of the search space. Due to the large step size in
the solution search equation of ABC there are chances of
skipping the factual solution are higher. For that reason, this
paper introduces a new search strategy in order to balance the
diversity and convergence capability of the ABC. Both
employed bee phase and onlooker bee phase are improved
with help of a local search strategy stimulated by memetic
algorithm. This paper also proposes a new strategy for fitness
calculation and probability calculation. The proposed
algorithm is named as Improved Memetic Search in ABC
(IMeABC). It is tested over 13 impartial benchmark functions
of different complexities and two real word problems are also
considered to prove proposed algorithms superiority over
original ABC algorithm and its recent variants
Artificial Bee Colony (ABC) is a distinguished optimization strategy that can resolve nonlinear and multifaceted problems. It is comparatively a straightforward and modern population based probabilistic approach for comprehensive optimization. In the vein of the other population based algorithms, ABC is moreover computationally classy due to its slow nature of search procedure. The solution exploration equation of ABC is extensively influenced by a arbitrary quantity which helps in exploration at the cost of exploitation of the better search space. In the solution exploration equation of ABC due to the outsized step size the chance of skipping the factual solution is high. Therefore, here this paper improve onlooker bee phase with help of a local search strategy inspired by memetic algorithm to balance the diversity and convergence capability of the ABC. The proposed algorithm is named as Improved Onlooker Bee Phase in ABC (IoABC). It is tested over 12 well known un-biased test problems of diverse complexities and two engineering optimization problems; results show that the anticipated algorithm go one better than the basic ABC and its recent deviations in a good number of the experiments.
Artificial Bee Colony (ABC) algorithm is a Nature
Inspired Algorithm (NIA) which based on intelligent food
foraging behaviour of honey bee swarm. This paper introduces
a local search strategy that enhances exploration competence
of ABC and avoids the problem of stagnation. The proposed
strategy introduces two new local search phases in original
ABC. One just after onlooker bee phase and one after scout
bee phase. The newly introduced phases are inspired by
modified Golden Section Search (GSS) strategy. The proposed
strategy named as new local search strategy in ABC
(NLSSABC). The proposed NLSSABC algorithm applied over
thirteen standard benchmark functions in order to prove its
efficiency.
A Heuristic Approach for optimization of Non Linear process using Firefly Alg...IJERA Editor
A comparison study of Firefly Algorithm (FA) and Bacterial Foraging Algorithm (BFO) optimization is carried out by applying them to a Non Linear pH neutralization process. In process control engineering, the Proportional, Derivative, Integral controller tuning parameters are deciding the performance of the controller to ensure the good performance of the plant. The FA and BFO algorithms are applied to obtain the optimum values of controller parameters. The performance indicators such as servo response and regulatory response tests are carried out to evaluate the efficiency of the heuristic algorithm based controllers. The error minimization criterion such as Integral Absolute Error (IAE), Integral Square Error (ISE), Integral Time Square Error (ITSE), Integral Time Absolute Error (ITAE) and Time domain specifications – rise time, Peak Overshoot and settling time are considered for the study of the performance of the controllers. The study indicates that, FA tuned PID controller provides marginally better set point tracking, load disturbance rejection, time domain specifications and error minimization for the Non Linear pH neutralization process compared to BFO tuned PID controller.
The document discusses a proposed Randomized Memetic Artificial Bee Colony (RMABC) algorithm for optimization problems. RMABC incorporates local search techniques into the Artificial Bee Colony algorithm to improve exploitation of promising solutions. It randomizes the step size in the local search to balance diversification and intensification. Experimental results on benchmark problems show RMABC outperforms other ABC algorithm variants in finding optimal solutions. The document provides background on optimization problems, nature-inspired algorithms, Artificial Bee Colony algorithm, and Memetic algorithms.
Artificial Bee Colony (ABC) algorithm is a Nature Inspired Algorithm (NIA) which based in intelligent food foraging behaviour of honey bee swarm. ABC outperformed over other NIAs and other local search heuristics when tested for benchmark functions as well as factual world problems but occasionally it shows premature convergence and stagnation due to lack of balance between exploration and exploitation. This paper establishes a local search mechanism that enhances exploration capability of ABC and avoids the dilemma of stagnation. With help of recently introduces local search strategy it tries to balance intensification and diversification of search space. The anticipated algorithm named as Enhanced local search in ABC (EnABC) and tested over eleven benchmark functions. Results are evidence for its dominance over other competitive algorithms.
Spider Monkey optimization (SMO) algorithm is newest addition in class of swarm intelligence. SMO is a population based stochastic meta-heuristic. It is motivated by intelligent foraging behaviour of fission fusion structured social creatures. SMO is a very good option for complex optimization problems. This paper proposed a modified strategy in order to enhance performance of original SMO. This paper introduces a position update strategy in SMO and modifies both local leader and global leader phase. The proposed strategy is named as Modified Position Update in Spider Monkey Optimization (MPU-SMO) algorithm. The proposed algorithm tested over benchmark problems and results show that it gives better results for considered unbiased problems.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
AUTOMATIC GENERATION AND OPTIMIZATION OF TEST DATA USING HARMONY SEARCH ALGOR...csandit
Software testing is the primary phase, which is performed during software development and it is
carried by a sequence of instructions of test inputs followed by expected output. The Harmony
Search (HS) algorithm is based on the improvisation process of music. In comparison to other
algorithms, the HSA has gain popularity and superiority in the field of evolutionary
computation. When musicians compose the harmony through different possible combinations of
the music, at that time the pitches are stored in the harmony memory and the optimization can
be done by adjusting the input pitches and generate the perfect harmony. The test case
generation process is used to identify test cases with resources and also identifies critical
domain requirements. In this paper, the role of Harmony search meta-heuristic search
technique is analyzed in generating random test data and optimized those test data. Test data
are generated and optimized by applying in a case study i.e. a withdrawal task in Bank ATM
through Harmony search. It is observed that this algorithm generates suitable test cases as well
as test data and gives brief details about the Harmony search method. It is used for test data
generation and optimization
Artificial bee colony (ABC) algorithm has proved its importance in solving a number of problems including engineering optimization problems. ABC algorithm is one of the most popular and youngest member of the family of population based nature inspired meta-heuristic swarm intelligence method. ABC has been proved its superiority over some other Nature Inspired Algorithms (NIA) when applied for both benchmark functions and real world problems. The performance of search process of ABC depends on a random value which tries to balance exploration and exploitation phase. In order to increase the performance it is required to balance the exploration of search space and exploitation of optimal solution of the ABC. This paper outlines a new hybrid of ABC algorithm with Genetic Algorithm. The proposed method integrates crossover operation from Genetic Algorithm (GA) with original ABC algorithm. The proposed method is named as Crossover based ABC (CbABC). The CbABC strengthens the exploitation phase of ABC as crossover enhances exploration of search space. The CbABC tested over four standard benchmark functions and a popular continuous optimization problem.
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
A HYBRID K-HARMONIC MEANS WITH ABCCLUSTERING ALGORITHM USING AN OPTIMAL K VAL...IJCI JOURNAL
Large quantities of data are emerging every year and an accurate clustering algorithm is needed to derive
information from these data. K-means clustering algorithm is popular and simple, but has many limitations
like its sensitivity to initialization, provides local optimum solutions. K-harmonic means clustering is an
improved variant of K-means which is insensitive to the initialization of centroids, but still in some cases it
ends up with local optimum solutions. Clustering using Artificial Bee Colony (ABC) algorithm always gives
global optimum solutions. In this paper a new hybrid clustering algorithm (KHM-ABC) is presented by
combining both K-harmonic means and ABC algorithm to perform accurate clustering. Experimental
results indicate that the performance of the proposed algorithm is superior to the available algorithms in
terms of the quality of clusters.
Artificial bee colony with fcm for data clusteringAlie Banyuripan
This document summarizes a research paper that proposes a modified artificial bee colony (ABC) algorithm for data clustering. The algorithm integrates a fuzzy C-means (FCM) operator into the ABC algorithm. Specifically:
- The ABC algorithm is modified to incorporate an FCM operator, which introduces scout bees based on a fuzzy function rather than randomly.
- In each cycle, after the employed bee and onlooker bee phases, the FCM operator generates a new solution for the scout bees based on previous solutions to improve optimization.
- The algorithm was tested on datasets from a machine learning repository and showed improved clustering quality compared to existing algorithms.
A hybrid optimization algorithm based on genetic algorithm and ant colony opt...ijaia
In optimization problem, Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) have
been known as good alternative techniques. GA is designed by adopting the natural evolution process,
while ACO is inspired by the foraging behaviour of ant species. This paper presents a hybrid GA-ACO for
Travelling Salesman Problem (TSP), called Genetic Ant Colony Optimization (GACO). In this method, GA
will observe and preserve the fittest ant in each cycle in every generation and only unvisited cities will be
assessed by ACO. From experimental result, GACO performance is significantly improved and its time
complexity is fairly equal compared to the GA and ACO.
Convergence tendency of genetic algorithms and artificial immune system in so...ijcsity
By the advances in the Evolution Algorithms (EAs) and the intelligent optimization metho
ds we witness the
big revolutions in solving the optimization problems. The application of the evolution algorithms are not
only not limited to the combined optimization problems, but also are vast in domain to the continuous
optimization problems. In this
paper we analyze and study the Genetic Algorithm (GA) and the Artificial
Immune System (AIS)
algorithm
which are capable in escaping the local optimization and also fastening
reaching the global optimization and to show the efficiency of the GA and AIS th
e application of them in
Solving Continuous Optimization Functions (SCOFs) are studied. Because of the multi variables and the
multi
-
dimensional spaces in SCOFs the use of the classic optimization methods, is generally non
-
efficient
and high cost. In other
words the use of the classic optimization methods for SCOFs generally leads to a
local optimized solution. A possible solution for SCOFs is to use the EAs which are high in probability of
succeeding reaching the local optimized solution. The results in pa
per show that GA is more efficient than
AIS in reaching the optimized solution in SCOFs.
An Hybrid Learning Approach using Particle Intelligence Dynamics and Bacteri...IJMER
The foraging behavior of E. Coli is used for optimization problems. This paper is based on a
hybrid method that combines particle swarm optimization and bacterial foraging (BF) algorithm for
solution of optimization results. We applied this proposed algorithm on different closed loop transfer
functions and the performance of the system using time response for the optimum value of PID
parameters is studied with incorporating PSO method on mutation, crossover, step sizes, and chemotactic
of the bacteria during the foraging. The bacterial foraging particle swarm optimization (BFPSO)
algorithm is applied to tune the PID controller of type 2, 3 and 4 systems with consideration of minimum
peak overshoot and steady state error objective function. The performance of the time response is
evaluated for the designed PID controller as the integral of time weighted squared error. The results
illustrate that the proposed approach is more efficient and provides better results as compared to the
conventional PSO algorithm.
A HYBRID COA-DEA METHOD FOR SOLVING MULTI-OBJECTIVE PROBLEMS ijcsa
The Cuckoo optimization algorithm (COA) is developed for solving single-objective problems and it cannot be used for solving multi-objective problems. So the multi-objective cuckoo optimization algorithm based on data envelopment analysis (DEA) is developed in this paper and it can gain the efficient Pareto frontiers. This algorithm is presented by the CCR model of DEA and the output-oriented approach of it.The selection criterion is higher efficiency for next iteration of the proposed hybrid method. So the profit function of the COA is replaced by the efficiency value that is obtained from DEA. This algorithm is
compared with other methods using some test problems. The results shows using COA and DEA approach for solving multi-objective problems increases the speed and the accuracy of the generated solutions.
A Survey of Solving Travelling Salesman Problem using Ant Colony OptimizationIRJET Journal
This document summarizes research on solving the travelling salesman problem (TSP) using ant colony optimization (ACO). It first provides background on TSP and describes how ACO mimics real ants finding food to solve optimization problems. The document then reviews several papers that have applied ACO to TSP and compared it to other algorithms. It finds that ACO generally performs better than genetic algorithms at finding optimal solutions to TSP as the number of cities increases. Finally, it proposes studying the effects of different ACO parameters on finding optimal TSP solutions.
محاضرات متقدمة تدرس لطلاب حاسبات بنى سويف السنة الثالثة لتنمية قدراتهم البحثية وهذة الموضوعات تدرس على مستوى الدكتوراة - - نريد تميز طلاب حاسبات ليتميزو فى البحث العلمى -
Chemical risk assessment is often limited by the lack of experimental toxicity data for a large number of diverse chemicals. In the absence of experimental data, potential chemical hazard is often predicted using data gap filling techniques such as quantitative structure activity relationship (QSAR) models. QSARs are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR tools are a widely utilized alternative to time-consuming clinical and animal testing methods, yet concerns over reliability and uncertainty limit application of QSAR models for regulatory chemical risk assessments. The reliability of a QSAR model depends on the quality and quantity of experimental training data and the applicability domain of the model. This talk will describe the basics concepts and best practices in QSAR modeling, principles associated with validation of QSAR models, summary of available QSAR tools, limitations and challenges in the acceptance of QSAR models, and the current status and prospects of QSAR modeling methods in the medical devices community.
The document summarizes the flower pollination algorithm, a nature-inspired optimization technique. It describes how flower pollination occurs in nature through both biotic cross-pollination involving pollinators, and abiotic self-pollination involving wind and water. The algorithm mimics these processes to update solution populations towards optimization. It initializes a population randomly, then iteratively explores through global pollination modeled by Levy flights, and exploits through local pollination. Applications include engineering problems, neural networks, and scheduling.
A Non-Revisiting Genetic Algorithm for Optimizing Numeric Multi-Dimensional F...ijcsa
The document describes a non-revisiting genetic algorithm with adaptive mutation for optimizing multi-dimensional numeric functions. The proposed algorithm avoids revisiting previously evaluated solutions by replacing any duplicates with a mutated version of either the best or random individual. The mutation rate adapts over generations, starting with more exploration and ending with more exploitation. Experimental results on 9 benchmark functions in 2 and 4 dimensions show the proposed algorithm achieves better best and average fitness than a standard genetic algorithm, with improvements ranging from 10-98% depending on the function.
We conducted comparative analysis of different supervised dimension reduction techniques by integrating a set of different data splitting algorithms and demonstrate the relative efficacy of learning algorithms dependence of sample complexity. The issue of sample complexity discussed in the dependence of data splitting algorithms. In line with the expectations, every supervised learning classifier demonstrated different capability for different data splitting algorithms and no way to calculate overall ranking of techniques was directly available. We specifically focused the classifier ranking dependence of data splitting algorithms and devised a model built on weighted average rank Weighted Mean Rank Risk Adjusted Model (WMRRAM) for consent ranking of learning classifier algorithms.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Este boletim paroquial discute as leituras bíblicas do IV Domingo da Quaresma, incluindo a reconciliação com Deus e com os outros. Também anuncia eventos locais como confissões, visitas aos doentes, e rendimentos de doações.
El documento presenta un análisis de la situación de la empresa Somoslamilk, incluyendo un diagnóstico DAFO, objetivos y estudios de mercado sobre tres empresas competidoras. Se describe la oferta de Somoslamilk y su evolución, y se analizan las estrategias, comunicación y objetivos de las empresas competidoras Canciones en busca de artistas, Green UFO y The Border Line Music.
Spider Monkey optimization (SMO) algorithm is newest addition in class of swarm intelligence. SMO is a population based stochastic meta-heuristic. It is motivated by intelligent foraging behaviour of fission fusion structured social creatures. SMO is a very good option for complex optimization problems. This paper proposed a modified strategy in order to enhance performance of original SMO. This paper introduces a position update strategy in SMO and modifies both local leader and global leader phase. The proposed strategy is named as Modified Position Update in Spider Monkey Optimization (MPU-SMO) algorithm. The proposed algorithm tested over benchmark problems and results show that it gives better results for considered unbiased problems.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
AUTOMATIC GENERATION AND OPTIMIZATION OF TEST DATA USING HARMONY SEARCH ALGOR...csandit
Software testing is the primary phase, which is performed during software development and it is
carried by a sequence of instructions of test inputs followed by expected output. The Harmony
Search (HS) algorithm is based on the improvisation process of music. In comparison to other
algorithms, the HSA has gain popularity and superiority in the field of evolutionary
computation. When musicians compose the harmony through different possible combinations of
the music, at that time the pitches are stored in the harmony memory and the optimization can
be done by adjusting the input pitches and generate the perfect harmony. The test case
generation process is used to identify test cases with resources and also identifies critical
domain requirements. In this paper, the role of Harmony search meta-heuristic search
technique is analyzed in generating random test data and optimized those test data. Test data
are generated and optimized by applying in a case study i.e. a withdrawal task in Bank ATM
through Harmony search. It is observed that this algorithm generates suitable test cases as well
as test data and gives brief details about the Harmony search method. It is used for test data
generation and optimization
Artificial bee colony (ABC) algorithm has proved its importance in solving a number of problems including engineering optimization problems. ABC algorithm is one of the most popular and youngest member of the family of population based nature inspired meta-heuristic swarm intelligence method. ABC has been proved its superiority over some other Nature Inspired Algorithms (NIA) when applied for both benchmark functions and real world problems. The performance of search process of ABC depends on a random value which tries to balance exploration and exploitation phase. In order to increase the performance it is required to balance the exploration of search space and exploitation of optimal solution of the ABC. This paper outlines a new hybrid of ABC algorithm with Genetic Algorithm. The proposed method integrates crossover operation from Genetic Algorithm (GA) with original ABC algorithm. The proposed method is named as Crossover based ABC (CbABC). The CbABC strengthens the exploitation phase of ABC as crossover enhances exploration of search space. The CbABC tested over four standard benchmark functions and a popular continuous optimization problem.
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
A HYBRID K-HARMONIC MEANS WITH ABCCLUSTERING ALGORITHM USING AN OPTIMAL K VAL...IJCI JOURNAL
Large quantities of data are emerging every year and an accurate clustering algorithm is needed to derive
information from these data. K-means clustering algorithm is popular and simple, but has many limitations
like its sensitivity to initialization, provides local optimum solutions. K-harmonic means clustering is an
improved variant of K-means which is insensitive to the initialization of centroids, but still in some cases it
ends up with local optimum solutions. Clustering using Artificial Bee Colony (ABC) algorithm always gives
global optimum solutions. In this paper a new hybrid clustering algorithm (KHM-ABC) is presented by
combining both K-harmonic means and ABC algorithm to perform accurate clustering. Experimental
results indicate that the performance of the proposed algorithm is superior to the available algorithms in
terms of the quality of clusters.
Artificial bee colony with fcm for data clusteringAlie Banyuripan
This document summarizes a research paper that proposes a modified artificial bee colony (ABC) algorithm for data clustering. The algorithm integrates a fuzzy C-means (FCM) operator into the ABC algorithm. Specifically:
- The ABC algorithm is modified to incorporate an FCM operator, which introduces scout bees based on a fuzzy function rather than randomly.
- In each cycle, after the employed bee and onlooker bee phases, the FCM operator generates a new solution for the scout bees based on previous solutions to improve optimization.
- The algorithm was tested on datasets from a machine learning repository and showed improved clustering quality compared to existing algorithms.
A hybrid optimization algorithm based on genetic algorithm and ant colony opt...ijaia
In optimization problem, Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) have
been known as good alternative techniques. GA is designed by adopting the natural evolution process,
while ACO is inspired by the foraging behaviour of ant species. This paper presents a hybrid GA-ACO for
Travelling Salesman Problem (TSP), called Genetic Ant Colony Optimization (GACO). In this method, GA
will observe and preserve the fittest ant in each cycle in every generation and only unvisited cities will be
assessed by ACO. From experimental result, GACO performance is significantly improved and its time
complexity is fairly equal compared to the GA and ACO.
Convergence tendency of genetic algorithms and artificial immune system in so...ijcsity
By the advances in the Evolution Algorithms (EAs) and the intelligent optimization metho
ds we witness the
big revolutions in solving the optimization problems. The application of the evolution algorithms are not
only not limited to the combined optimization problems, but also are vast in domain to the continuous
optimization problems. In this
paper we analyze and study the Genetic Algorithm (GA) and the Artificial
Immune System (AIS)
algorithm
which are capable in escaping the local optimization and also fastening
reaching the global optimization and to show the efficiency of the GA and AIS th
e application of them in
Solving Continuous Optimization Functions (SCOFs) are studied. Because of the multi variables and the
multi
-
dimensional spaces in SCOFs the use of the classic optimization methods, is generally non
-
efficient
and high cost. In other
words the use of the classic optimization methods for SCOFs generally leads to a
local optimized solution. A possible solution for SCOFs is to use the EAs which are high in probability of
succeeding reaching the local optimized solution. The results in pa
per show that GA is more efficient than
AIS in reaching the optimized solution in SCOFs.
An Hybrid Learning Approach using Particle Intelligence Dynamics and Bacteri...IJMER
The foraging behavior of E. Coli is used for optimization problems. This paper is based on a
hybrid method that combines particle swarm optimization and bacterial foraging (BF) algorithm for
solution of optimization results. We applied this proposed algorithm on different closed loop transfer
functions and the performance of the system using time response for the optimum value of PID
parameters is studied with incorporating PSO method on mutation, crossover, step sizes, and chemotactic
of the bacteria during the foraging. The bacterial foraging particle swarm optimization (BFPSO)
algorithm is applied to tune the PID controller of type 2, 3 and 4 systems with consideration of minimum
peak overshoot and steady state error objective function. The performance of the time response is
evaluated for the designed PID controller as the integral of time weighted squared error. The results
illustrate that the proposed approach is more efficient and provides better results as compared to the
conventional PSO algorithm.
A HYBRID COA-DEA METHOD FOR SOLVING MULTI-OBJECTIVE PROBLEMS ijcsa
The Cuckoo optimization algorithm (COA) is developed for solving single-objective problems and it cannot be used for solving multi-objective problems. So the multi-objective cuckoo optimization algorithm based on data envelopment analysis (DEA) is developed in this paper and it can gain the efficient Pareto frontiers. This algorithm is presented by the CCR model of DEA and the output-oriented approach of it.The selection criterion is higher efficiency for next iteration of the proposed hybrid method. So the profit function of the COA is replaced by the efficiency value that is obtained from DEA. This algorithm is
compared with other methods using some test problems. The results shows using COA and DEA approach for solving multi-objective problems increases the speed and the accuracy of the generated solutions.
A Survey of Solving Travelling Salesman Problem using Ant Colony OptimizationIRJET Journal
This document summarizes research on solving the travelling salesman problem (TSP) using ant colony optimization (ACO). It first provides background on TSP and describes how ACO mimics real ants finding food to solve optimization problems. The document then reviews several papers that have applied ACO to TSP and compared it to other algorithms. It finds that ACO generally performs better than genetic algorithms at finding optimal solutions to TSP as the number of cities increases. Finally, it proposes studying the effects of different ACO parameters on finding optimal TSP solutions.
محاضرات متقدمة تدرس لطلاب حاسبات بنى سويف السنة الثالثة لتنمية قدراتهم البحثية وهذة الموضوعات تدرس على مستوى الدكتوراة - - نريد تميز طلاب حاسبات ليتميزو فى البحث العلمى -
Chemical risk assessment is often limited by the lack of experimental toxicity data for a large number of diverse chemicals. In the absence of experimental data, potential chemical hazard is often predicted using data gap filling techniques such as quantitative structure activity relationship (QSAR) models. QSARs are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR tools are a widely utilized alternative to time-consuming clinical and animal testing methods, yet concerns over reliability and uncertainty limit application of QSAR models for regulatory chemical risk assessments. The reliability of a QSAR model depends on the quality and quantity of experimental training data and the applicability domain of the model. This talk will describe the basics concepts and best practices in QSAR modeling, principles associated with validation of QSAR models, summary of available QSAR tools, limitations and challenges in the acceptance of QSAR models, and the current status and prospects of QSAR modeling methods in the medical devices community.
The document summarizes the flower pollination algorithm, a nature-inspired optimization technique. It describes how flower pollination occurs in nature through both biotic cross-pollination involving pollinators, and abiotic self-pollination involving wind and water. The algorithm mimics these processes to update solution populations towards optimization. It initializes a population randomly, then iteratively explores through global pollination modeled by Levy flights, and exploits through local pollination. Applications include engineering problems, neural networks, and scheduling.
A Non-Revisiting Genetic Algorithm for Optimizing Numeric Multi-Dimensional F...ijcsa
The document describes a non-revisiting genetic algorithm with adaptive mutation for optimizing multi-dimensional numeric functions. The proposed algorithm avoids revisiting previously evaluated solutions by replacing any duplicates with a mutated version of either the best or random individual. The mutation rate adapts over generations, starting with more exploration and ending with more exploitation. Experimental results on 9 benchmark functions in 2 and 4 dimensions show the proposed algorithm achieves better best and average fitness than a standard genetic algorithm, with improvements ranging from 10-98% depending on the function.
We conducted comparative analysis of different supervised dimension reduction techniques by integrating a set of different data splitting algorithms and demonstrate the relative efficacy of learning algorithms dependence of sample complexity. The issue of sample complexity discussed in the dependence of data splitting algorithms. In line with the expectations, every supervised learning classifier demonstrated different capability for different data splitting algorithms and no way to calculate overall ranking of techniques was directly available. We specifically focused the classifier ranking dependence of data splitting algorithms and devised a model built on weighted average rank Weighted Mean Rank Risk Adjusted Model (WMRRAM) for consent ranking of learning classifier algorithms.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Este boletim paroquial discute as leituras bíblicas do IV Domingo da Quaresma, incluindo a reconciliação com Deus e com os outros. Também anuncia eventos locais como confissões, visitas aos doentes, e rendimentos de doações.
El documento presenta un análisis de la situación de la empresa Somoslamilk, incluyendo un diagnóstico DAFO, objetivos y estudios de mercado sobre tres empresas competidoras. Se describe la oferta de Somoslamilk y su evolución, y se analizan las estrategias, comunicación y objetivos de las empresas competidoras Canciones en busca de artistas, Green UFO y The Border Line Music.
1) O documento resume as leituras e temas litúrgicos do 7o Domingo do Tempo Comum, focando na salvação que Deus oferece.
2) Anuncia eventos religiosos nas próximas semanas, como a Quaresma, Congresso Eucarístico e missas.
3) Fornece informações sobre doações de sangue e IRS para instituições locais.
Este boletim paroquial fornece informações sobre os eventos religiosos nas três paróquias locais nos próximos dias, incluindo missas, visitas ao cemitério e atividades de catequese. Também anuncia o início de um Sagrado Lausperene em uma das paróquias.
This very short document does not contain enough contextual information to generate a meaningful 3 sentence summary. The document simply states "this is test document" without providing any additional details.
Este documento discute os riscos da internet para crianças e adolescentes e como os pais podem educá-los para um uso seguro. Alguns pontos importantes incluem: (1) as atividades mais comuns de crianças na internet como redes sociais podem expô-los a riscos; (2) conteúdos impróprios como pornografia são os maiores riscos; (3) pais devem monitorar o uso da internet, manter o computador em áreas comuns e ensinar filhos sobre segurança online.
Este documento presenta una introducción a la psicología evolutiva y el desarrollo de la personalidad. Explica que la personalidad surge de la interacción entre la disposición biológica y las experiencias aprendidas. Luego describe varios modelos biológicos y psicológicos de la personalidad, incluidos los ejes de la personalidad y los modelos de Eysenck y Bandura. Finalmente, resume las etapas del desarrollo cognitivo de Piaget y las etapas psicosexuales de Freud, y cómo estas influyen en la
Este boletim paroquial contém informações sobre eventos religiosos e comunitários em diferentes paróquias, como missas, procissões, vigílias e intenções de oração. Também inclui avisos sobre matrículas escolares e inscrições em creches da região.
Este documento fornece informações sobre eventos religiosos e serviços em várias comunidades nos próximos dias, incluindo missas, batizados, casamentos e procissões. Também fornece detalhes sobre doações recebidas e gastos com obras nas igrejas.
Este documento resume as atividades e eventos das comunidades de Parada, Padornelo e Moselos durante o mês de março de 2010, incluindo preparações para a Páscoa e eventos comunitários como festas religiosas e atividades de associações locais.
1) O documento discute uma reforma de bens eclesiásticos na diocese que visa garantir que os bens sejam usados para fins pastorais e de caridade, em linha com os ensinamentos do Beato Bartolomeu dos Mártires.
2) É anunciada uma missa nova para o Pe. Ricardo Barbosa em 22 de novembro.
3) São listadas intenções de missas e outros eventos nas paróquias de S. Paio de Moselos, Sta Marinha de Padornelo e S. Pedro de Parada.
The document describes various accounts payable processes at the University of Wisconsin. It outlines the invoice payment process, including invoice auditing, keying invoices into the system, verifying payments, editing and check printing. It also describes related processes like stop payments, payment corrections, international payments, vendor relations, pre-audit requisition auditing, emergency transaction processing, and direct charge processes. The main functional areas of accounts payable are invoice payment, stop payment, vendor relations, pre-audit, 1099 reporting.
La información clave del documento incluye varios servicios y beneficios de Internet como el correo electrónico, la transmisión de archivos, conversaciones en línea, mensajería instantánea y el mercadeo a través de la World Wide Web.
Slideshare.net es una página que permite subir presentaciones de diapositivas. El documento menciona que la página fue subida mediante slideshare y proporciona el nombre Arturo Mollo y el curso Simulación y Modelaje.
Este boletim paroquial resume as principais atividades religiosas da próxima semana, incluindo missas e procissões em três paróquias da região. Também anuncia eventos de arrecadação de fundos para projetos comunitários e convida os fiéis a acolherem o evangelho com alegria.
Reliable and accurate estimation of software has always been a matter of concern for industry and
academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all
types of datasets and environments. Since the motive of estimation model is to minimize the gap between
actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization,
Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of
COCOMO Model. The performance of these techniques has been analysed by established performance
measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
The document summarizes research using evolutionary computing techniques like particle swarm optimization, ant colony optimization, and bee colony optimization to improve software effort estimation compared to the COCOMO model. The techniques are applied to tune the parameters of the COCOMO model. The models are validated using a dataset of 48 interactive voice response projects, and the bee colony optimization technique produces the lowest mean magnitude of relative error (MMRE) of 0.11 and root mean squared error (RMSE) of 7.85, outperforming the other techniques and COCOMO model. Evolutionary computing techniques are found to provide more accurate effort estimates than existing estimation models.
Hybrid Data Clustering Approach Using K-Means and Flower Pollination Algorithmaciijournal
Data clustering is a technique for clustering set of objects into known number of groups. Several
approaches are widely applied to data clustering so that objects within the clusters are similar and objects
in different clusters are far away from each other. K-Means, is one of the familiar center based clustering
algorithms since implementation is very easy and fast convergence. However, K-Means algorithm suffers
from initialization, hence trapped in local optima. Flower Pollination Algorithm (FPA) is the global
optimization technique, which avoids trapping in local optimum solution. In this paper, a novel hybrid data
clustering approach using Flower Pollination Algorithm and K-Means (FPAKM) is proposed. The
proposed algorithm results are compared with K-Means and FPA on eight datasets. From the experimental
results, FPAKM is better than FPA and K-Means
AUTOMATIC GENERATION AND OPTIMIZATION OF TEST DATA USING HARMONY SEARCH ALGOR...cscpconf
Software testing is the primary phase, which is performed during software development and it is
carried by a sequence of instructions of test inputs followed by expected output. The Harmony
Search (HS) algorithm is based on the improvisation process of music. In comparison to other
algorithms, the HSA has gain popularity and superiority in the field of evolutionary
computation. When musicians compose the harmony through different possible combinations of
the music, at that time the pitches are stored in the harmony memory and the optimization can
be done by adjusting the input pitches and generate the perfect harmony. The test case
generation process is used to identify test cases with resources and also identifies critical
domain requirements. In this paper, the role of Harmony search meta-heuristic search
technique is analyzed in generating random test data and optimized those test data. Test data
are generated and optimized by applying in a case study i.e. a withdrawal task in Bank ATM
through Harmony search. It is observed that this algorithm generates suitable test cases as well
as test data and gives brief details about the Harmony search method. It is used for test data
generation and optimization.
APPLYING GENETIC ALGORITHMS TO INFORMATION RETRIEVAL USING VECTOR SPACE MODEL IJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms.
Applying genetic algorithms to information retrieval using vector space modelIJCSEA Journal
The document describes a study that applied genetic algorithms to information retrieval using the vector space model. The study used an adaptive genetic algorithm approach with two proposed fitness functions (cosine and Jaccard's), adaptive crossover and mutation probabilities. Experimental results on a test corpus showed improvements in precision and recall compared to traditional approaches, with the proposed cosine fitness function performing best. Precision generally decreased as recall increased. The modifications made to the genetic algorithm and fitness functions led to better weighting of query terms and improved results.
Applying Genetic Algorithms to Information Retrieval Using Vector Space ModelIJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
In this research, a hybrid wrapper model is proposed to identify the featured gene subset from the gene expression data. To balance the gap between exploration
and exploitation, a hybrid model with a popular meta-heuristic algorithm named
spider monkey optimizer (SMO) and simulated annealing (SA) is applied. In the proposed model, ReliefF is used as a filter to obtain the relevant gene subset
from dataset by removing the noise and outliers prior to feeding the data to the
wrapper SMO. To enhance the quality of the solution, simulated annealing is
deployed as local search with the SMO in the second phase, which will guide to the detection of the most optimal feature subset. To evaluate the performance of the proposed model, support vector machine (SVM) as a fitness function to recognize the most informative biomarker gene from the cancer datasets along with University of California, Irvine (UCI) datasets. To further evaluate the model, 4 different classifiers (SVM, na¨ıve Bayes (NB), decision tree (DT), and k-nearest neighbors (KNN)) are used. From the experimental results and analysis, it’s noteworthy to accept that the ReliefF-SMO-SA-SVM performs relatively better than its state-of-the-art counterparts. For cancer datasets, our model performs better in terms of accuracy with a maximum of 99.45%.
Chicken Swarm as a Multi Step Algorithm for Global Optimizationinventionjournals
A new modified of Chicken Swarm Optimization (CSO) algorithm called multi step CSO is proposed for global optimization. This modification is reducing the CSO algorithm’s steps by eliminates the parameter roosters, hens and chicks. Multi step CSO more efficient than CSO algorithm to solve optimization problems. Experiments on seven benchmark problems and a speed reducer design were conducted to compare the performance of Multi Step CSO with CSO algorithms and the other algorithms based population such as Cuckoo Search (CS),Particle Swarm Optimization (PSO), Differential Evolution (DE) and Genetic Algorithm (GA). Simulation results show that Multi step CSO algorithm performs better than those algorithms. Multi step CSO algorithm has the advantages of simple, high robustness, fast convergence, fewer control.
A hybrid approach from ant colony optimization and K-neare.pdfRayhanaKarar
This document proposes a hybrid approach using ant colony optimization and K-nearest neighbor for feature selection and classification. The approach uses multiple groups of ants, with each group selecting features using a different search criterion. A fitness function composed of heuristic and pheromone values is used to evaluate candidate features. The heuristic value represents the class separability of the feature, while the pheromone value is based on classification accuracy. Sequential forward selection is applied to iteratively select features that improve accuracy. The approach is tested on medical datasets, achieving promising results by increasing accuracy with fewer selected features.
Cuckoo algorithm with great deluge local-search for feature selection problemsIJECEIAES
Feature selection problem is concerned with searching in a dataset for a set of features aiming to reduce the training time and enhance the accuracy of a classification method. Therefore, feature selection algorithms are proposed to choose important features from large and complex datasets. The cuckoo search (CS) algorithm is a type of natural-inspired optimization algorithms and is widely implemented to find the optimum solution for a specified problem. In this work, the cuckoo search algorithm is hybridized with a local search algorithm to find a satisfactory solution for the problem of feature selection. The great deluge (GD) algorithm is an iterative search procedure, that can accept some worse moves to find better solutions for the problem, also to increase the exploitation ability of CS. The comparison is also provided to examine the performance of the proposed method and the original CS algorithm. As result, using the UCI datasets the proposed algorithm outperforms the original algorithm and produces comparable results compared with some of the results from the literature.
The document presents a particle swarm inspired cuckoo search algorithm for real parameter optimization. It combines two algorithms: cuckoo search and particle swarm optimization. In cuckoo search, agents find new solutions using levy flights. The proposed algorithm adds the global best solution from particle swarm optimization to enhance exploitation. It balances exploration and exploitation through two new search strategies with random probabilities. The algorithm is tested on benchmark functions and two real-world problems, showing better performance than other algorithms.
The document proposes a particle swarm inspired cuckoo search algorithm to improve the balance of exploration and exploitation in the standard cuckoo search algorithm. It adds neighborhood information from the global best solutions to increase diversity and uses two new search strategies with a random probability rule to balance exploration and exploitation. The algorithm is tested on 30 benchmark functions and two real-world problems, and shows better performance than the standard cuckoo search, particle swarm optimization, and other state-of-the-art algorithms.
Single parent mating in genetic algorithm for real robotic system identificationIAESIJAI
System identification (SI) is a method of determining a mathematical model
for a system given a set of input-output data. A representation is made using
a mathematical model based on certain specified assumptions. In SI, model
structure selection is a step where a model structure perceived as an adequate
system representation is selected. A typical rule is that the final model must
have a good balance between parsimony and accuracy. As a popular search
method, genetic algorithm (GA) is used for selecting a model structure.
However, the optimality of the final model depends much on the
effectiveness of GA operators. This paper presents a mating technique
named single parent mating (SPM) in GA for use in a real robotic SI. This
technique is based on the chromosome structure of the parents such that a
single parent is sufficient in achieving mating that eases the search for the
optimal model. The results show that using three different objective
functions (Akaike information criterion, Bayesian information criterion and
parameter magnitude–based information criterion 2) respectively, GA with
the mating technique is able to find more optimal models than without the
mating technique. Validations show that the selected models using the
mating technique are acceptable.
An efficient and powerful advanced algorithm for solving real coded numerica...IOSR Journals
This document discusses an artificial bee colony algorithm with crossover operators for solving numerical optimization problems. The algorithm is based on the intelligent behavior of honey bee swarms. It introduces crossover operations between individual food source positions to generate new offspring. The offspring replace parents if they have better fitness. The algorithm is tested on standard benchmark functions like Griewank and Rosenbrock and results compared to an X-ABC algorithm. Results show the ABC with crossover performs better with fewer parameters.
An Automatic Clustering Technique for Optimal ClustersIJCSEA Journal
This document presents a new automatic clustering algorithm called Automatic Merging for Optimal Clusters (AMOC). AMOC is a two-phase iterative extension of k-means clustering that aims to automatically determine the optimal number of clusters for a given dataset. In the first phase, AMOC initializes a large number of clusters k using k-means. In the second phase, it iteratively merges the lowest probability cluster with its closest neighbor, recomputing metrics each time to evaluate if the merge improved clustering quality. The algorithm stops merging once no improvements are found. Experimental results on synthetic and real datasets show AMOC finds nearly optimal cluster structures in terms of number, compactness and separation of clusters.
Improvement of genetic algorithm using artificial bee colonyjournalBEEI
This document proposes using an artificial bee colony algorithm to improve a genetic algorithm. It does this by generating the initial population for the genetic algorithm rather than using random generation. The proposed method is tested on random number generation and the travelling salesman problem. For random number generation, five statistical tests are used to evaluate fitness, with the goal of generating random numbers that pass all tests. For the travelling salesman problem, fitness is based on minimizing the total distance travelled. The results show the proposed method performs better than the traditional genetic algorithm in terms of mean iterations, execution time, error rate, and finding the shortest route.
Software testing is an important activity of the software development process. Software testing is most
efforts consuming phase in software development. One would like to minimize the effort and maximize the
number of faults detected and automated test case generation contributes to reduce cost and time effort.
Hence test case generation may be treated as an optimization problem In this paper we have used genetic
algorithm to optimize the test case that are generated applying conditional coverage on source code. Test
case data is generated automatically using genetic algorithm are optimized and outperforms the test cases
generated by random testing.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
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📕 Detailed agenda:
What is RPA? Benefits of RPA?
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"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
AUTOMATED TEST CASE GENERATION AND OPTIMIZATION: A COMPARATIVE REVIEW
1. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 5, October 2016
DOI:10.5121/ijcsit.2016.8502 19
AUTOMATED TEST CASE GENERATION AND
OPTIMIZATION: A COMPARATIVE REVIEW
Rajesh Kumar Sahoo1
, Deeptimanta Ojha2
, Durga Prasad Mohapatra3
, Manas
Ranjan Patra4
1
Department of Computer Engineering, A.B.I.T, Cuttack
2
Department of Computer Engineering, A.B.I.T, Cuttack
3
Department of Computer Engineering, NIT,Rourkela
4
Department of Computer Engineering, Berhampur University,Berhampur
ABSTRACT
Software testing is the primary phase, which is performed during software development and it is carried by
a sequence of instructions of test inputs followed by expected output. Evolutionary algorithms are most
popular in the computational field based on population. The test case generation process is used to identify
test cases with resources and also identifies critical domain requirements. The behavior of bees is based on
population and evolutionary method. Bee Colony algorithm (BCA) has gained superiority in comparison
to other algorithms in the field of computation. The Harmony Search (HS) algorithm is based on the
enhancement process of music. When musicians compose the harmony through different possible
combinations of the music, at that time the pitches are stored in the harmony memory and the optimization
can be done by adjusting the input pitches and generate the perfect harmony. Particle Swarm Optimization
(PSO) is an intelligence based meta-heuristic algorithm where each particle can locate their source of food
at different position.. In this algorithm, the particles will search for a better food source position in the
hope of getting a better result. In this paper, the role of Artificial Bee Colony, particle swarm optimization
and harmony search algorithms are analyzed in generating random test data and optimized those test data.
Test case generation and optimization through bee colony, PSO and harmony search (HS) algorithms
which are applied through a case study, i.e., withdrawal operation in Bank ATM and it is observed that
these algorithms are able to generate suitable automated test cases or test data in a client manner. This
section further gives the brief details and compares between HS, PSO, and Bee Colony (BC) Optimization
methods which are used for test case or test data generation and optimization.
KEYWORDS
Bee Colony algorithm, particle swarm optimization, harmony search algorithm, meta-heuristics, test case
generation, test case optimization, test data.
1. INTRODUCTION
Generations of test cases are based on the requirements. It completely ignores the aspect of
system execution. Apart from this, the test case design from program code may cause difficult to
imbrute [8]. Test cases may not expose the missing functionalities. The proposed approach
focuses the redundancy, test cases, and test case optimization challenges. It uses HS, PSO and
Bee colony optimization algorithm to optimize the random test cases. Moreover, this proposed
methods inspired the developers to generate random test cases to improve the design quality of
2. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 5, October 2016
20
software. This paper is intended to present the result of the outcome of different evolutionary
meta heuristic algorithms like HS, pso and bee colony to find the optimum solutions. These
solutions are essential for the software construct. Optimization can be represented through the
process to find the best result under the given circumstances which might be used for maximizing
or minimizing the local or overall optimum value of a function. The evolutionary algorithms like
Harmony search, Particle swarm optimization and Bee colony algorithms are discussed in this
paper. Z. W. Geem [11] introduced Harmony search method in 2009.By this technique, a flawless
state of harmony is observed through the musical process. It is correspondent to generate the
optimum value through the musical optimization process. The improvisation of music is a
technique where the musician plays various musical notes with divergent types of musical
instruments and generates the best amalgamation of tunes with frequency. D.D.Karaboga[7]
introduced Bee colony algorithm in 2005. In Bee colony algorithm (BCA), three types of bees are
employed, onlooker and scout bees. Those bees will continuously search for the different food
source position as per quality in order to replace the solution with new improved solution.
J.Kenedy[15] developed Particle swarm optimization algorithm in 1995.Particles searching their
foods through the positions and velocities. The best position and velocity is updated with older
one. Modification of velocity helps the particle to move faster for searching foods.
The rest of the paper is organized as follows. Section 2 discusses basics of test data automation,
over view of harmony search,basic particle swarm optimization and bee colony algorithm.Section
3 is for literature survey, Section 4 represents the fundamentals of Harmony Search (HS)
algorithm,particle swarm aoptimization,bee colony algorithm, proposed systems, and
methodology. Section 5 focuses the simulation results, Section 6 represents discussion and future
scope and Section 7 concludes the paper.
2. BASIC CONCEPTS
2.1 AUTOMATED TEST DATA GENERATION
Testing is the phenomenon of finding errors after executing the programs. Software testing can be
defined by many processes designed sequentially and does not do anything unintended [21].The
objective of software testing is to finalize the application software against the user requirements.
It must have good test coverage to test the application software and perform as per the
specifications. For generating list of coverage’s, the test cases should be designed with maximum
possibilities of finding various errors or bugs [22]. The test cases should be very effective and is
measured through the number of defects or errors reported. Generation of test cases or test data is
a method to identify the data set which satisfies the criteria. Most of the researchers are used the
heuristics approaches for automated generation of test cases or data. Automated test data
generation helps to minimizing the time and cost in developing test cases.
2.2 OVERVIEW OF HARMONY SEARCH
The harmony search metaheuristic optimization algorithm is based on the music. The harmony
search method is inspired by a musician when he composes the music. Usually harmony consists
of different possible amalgamations of music pitches saved in the memory. In this technique first,
random solutions are directly stored in the harmony memory based on memory considering rate
and pitch adjustment rate, and then pitch adjustment distance will be calculated between different
3. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 5, October 2016
21
selected random solutions. The best solution is stored in the memory of harmony by discarding
the worst solution.
2.3 OVERVIEW OF PARTICLE SWARM OPTIMIZATION
The particle swarm optimization (PSO) is an evolutionary algorithm motivated by social behavior
of grouped migrating birds. Kennedy and Eberhart introduced PSO in 1995 [15] which can solve
the optimization problem. Schooling of fish and the behavior of birds in a group inspired a
population based optimization method. In a search space each particle stays at a particular
position. The quality of each particle position determines its fitness. Particles have their own
independent velocity and speed. The velocity of each particle is dependent on its neighbors which
gives the best fitness functional value. Generations of test cases are used to identify test cases
with resources and also identify critical domain requirements.
2.4 OVERVIEW OF BEE COLONY OPTIMIZATION
Bee Colony algorithm (BCA) is a evolutionary population based method which based on the
behaviors of the bees .It is developed by Dervis Karaboga[7] for optimization purpose in 2005.
Bee colony method states that the bees are found their food source through their foraging
behavior. The aims of honey bees are to establish places of food source with highest nectar
amount. This algorithm is very popular in computational field .In this algorithm, the bees will
search for a best position of food source in the hope to get better result. . The food source position
represents a possible set of solutions and the amount of nectar represent corresponding fitness
values or quality of all solutions or the food source.
3. LITERATURE SURVEY
Kaur Arvinder et.al[1] proposed how different factors like coordination, synchronization are
applied in bee colony optimization technique.Through this optimization techniques different
kinds of large and complex programs are optimized. Biswal et.al [2] focused how the test case
scenarios are derived from activity diagram which may be converted into control flow graph
(CFG ) where each node represents an activity and the edges of the flow graph.Euclidean distance
is used by Korel [3] to quantify the distance between two paths of the control flow graph.
According to Basturk. B et.al and Dahiya. S et.al [4][6] proposed an approach which aimed to
generate the optimal test cases with good path coverage. This paper also explains how the bee
agents gather the food source through test cases and identify optimal test cases with respect to
fitness functional value. According to Geem et al. [11,12] harmony search a metaheuristic
population-based algorithm where multiple harmonies are used in parallel which gives better
performance with high efficiency. Geem [12] focused the Pitch Adjustment Rate (PAR) function
which is used in simulated annealing and it also increases the robustness of the algorithm. It is
highly reliable. Geem et al. [13] described how the parameter like Pitch adjustment rate (PAR)
increases in a linear way with the number of generations while the bandwidth (BW) decreases
exponentially in Harmony Search algorithm. Manjarres et al. [9] focused described various
applications of HS like industry, power systems, construction design, and information
technology. Das et al. [10] described the background of the power of Harmony Search (HS)
which gives the better solutions. The major drawback is user must specify the minimum and
maximum bandwidth values. It is difficult to deduce the program which is dependent. Suresh et
4. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 5, October 2016
22
al. [23] described a genetic algorithm used for generation of test data and it also generates the
basic path. In this case the genetic algorithm combines the features of local and global test data
optimization. According to this paper sequence diagram is converted into control flow graph and
generates the optimal test cases using genetic algorithm. Sthamer [14] explained about the test
data generation by using structural test coverage through Genetic Algorithms. Lin and Yeh [16]
discussed about the generation of test data through Genetic Algorithms automatically. Wegener et
al. [17] described a test environment which is used for test data generation in statement and
branch testing automatically. S.K. Swain and D.P. Mohapatra[25] discussed about the number or
test paths generated through a model and it also covers the coverage criteria like branch coverage,
condition coverage and full test path coverage. According to Kaur et al[18] Hybrid Particle
Swarm Optimization (HPSO) algorithm for performing in a search space to get an efficient
solution through regression testing. According to Mustafa Servet Kiran and Ahmet Babalik[19]
bee colony algorithm (BCA) gives optimum solution for combinatorial optimization problems.
This technique acts as a decentralized way and self organized. Pargas et al. [20] focused on the
Genetic Algorithm is directed by control-dependence graph program under test for searching test
data to satisfy the criteria of all-nodes and all-branches. According to Michael et al. [24] the
genetic algorithm (GA) is used for automated test data generation to satisfy decision and
condition coverage criterion.
4. PROPOSED SYSTEM
This paper proposed a methodology for generating test cases for withdrawal system of an ATM
machine and test cases are optimized by different evolutionary algorithms like Harmony search
algorithm (HSA), Particle swarm optimization algorithm (PSOA) and Bee colony algorithm
(BCA).These methods are used for evaluating its efficiency and effectiveness for generating the
test cases and for maximizing to achieve the goal.
4.1. NECESSITY OF PROPOSED SYSTEM
The proposed system is intended to generate an automatic and optimized test case with existing
approaches of Harmony Search, PSOA and Bee Colony Algorithm. Optimized test cases may not
be helpful in the testing process. It may be required to differentiate between the various test cases.
In case of HSA all the system may be initialized with harmony memories. Each harmony memory
maintains its own prevailing location. Harmony Search has a memory that helps to maintain the
solutions by harmonies. For PSOA the system may be initialized with particle positions. Each
particle maintains its locations as test cases. In Bee colony algorithm (BCA) food source
positions may be initialized by the system and each food source maintains its positions. This
paper also finds out the effectiveness of the proposed approach through the number of test cases
or test data.
4.1.1 HARMONY SEARCH ALGORITHM (HSA)
Harmony search is a meta heuristic population-based optimization technique. Through this
technique, the problem is represented through different test cases. The quality of each test case is
calculated through the fitness value of the problem. The working principle of harmony search
technique is inspired by the musician when he composes the music; a musician usually tries
different combinations of music pitches which are stored in the harmony memory. Perfect
5. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 5, October 2016
23
harmony search needs a correspondent for generating the process to find the solution which is
optimal. The main steps of a harmony search method are:
• Generate random solutions which will be stored in the Harmony Memory (HM).
• Select a random solution from Harmony Memory based factors that are Harmony
Memory Considering Rate (HMCR) and Pitching Adjustment Rate (PAR).
• Apply adjustments pitch distance to the selected random solution.
• Compare the fitness function values of the mutated solution or newly formed solution
with the worst solution.
• If better solution is found then the worst solution is substituted with the mutated solution
available in the harmony memory otherwise the solution is discarded.
• Remember the solution which is best so far.
In this case, mainly two parameters are used. They are HMCR and PAR. Harmony Memory
Considering Rate (HMCR) is described as the probability of selecting a particular component or
solution available in HM. Pitching Adjust Rate (PAR) describes the probability distribution of the
candidate solution for mutation purpose through HM.
The value of Pitching Adjust Rate (PAR) can be calculated as follows.
PAR= (PARmax - PARmin) / (itmax)*it + PARmin .(1)
Where PARmax →maximum pitch adjusting rate
PARmin →minimum pitch adjusting rate
itmax → given maximum iterations
it → current iteration number
The bandwidth factor ‘bw’ can be calculated as.
bw= bwmax * exp (coef * it) ......(2)
Here the value of ‘coef’ is evaluated as follows:
coef=log (bwmin/ bwmax/ itmax) ..........(3)
Where bw = bandwidth
bwmin = minimum bandwidth
bwmax = maximum bandwidth
The bandwidth factor bw is used to alter the value of existing solution available in Harmony
Memory. This can be done as follows:
xnew = xold + rand (0,1) * bw (4)
Where xnew = new solution
xold = old existing solution
rand (0,1) = a random value ranging in between 0 and 1.
Harmony search a meta heuristic search technique which is generally used to optimize problems
whose solution may be analyzed in an n-dimensional space. According to Harmony Search
6. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 5, October 2016
24
Algorithm, each musician plays a musical note to generate the best possible harmony altogether.
A new solution is generated for a musical note with the help of harmony factors like HMCR
(Harmony Memory Considering Rate), PAR (Pitch Adjustment Rate) and bw (Bandwidth). Each
musical note usually possesses its current position. After iteration, the worst harmony is
substituted by a new better solution.
The flow chart of Harmony Search is depicted in Figure-1
Figure 1: Flow chart of Harmony Search
4.1.2 PARTICLE SWARM OPTIMIZATION
Particle Swarm Optimization (PSO) is an evolutionary searching method used to optimize
problems in an n-dimensional space. It is derived and motivated by the social behavior of birds.
In PSO,a potential solution is called particle. In PSO swarm is the combination of different
particles and each particle having a candidate solution. Each particle usually posses its current
position, current velocity, and its own best position called as pbest. Pbest is the personal best
position explored and also incorporates gbest that global best position achieved by all its
individuals. It is a simple approach and it is effective across a variety of problem domains.
PSO starts with initialization of particle velocity and current position. In this case the particle is in
2-D space. The fitness value of the particle may be calculated according to function. If the fitness
of the particle is better than its previous value, then the position of the particle may be updated to
its personal best position. Again if the value of pbest is better than gbest position, the position of
global best of the particle is updated. The process may be repeated until termination criteria are
met or the optimal solution is found.
7. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 5, October 2016
25
The ideas behind Particle Swarm Optimization are as follows:
• Every particle has the capability to find the optimal solution.
• Every particle moves with a velocity and position of particle is adjusting through the
velocity.
• Pbest is the best position of a particle which gives best result.
• Each particle knows its fitness in its neighborhoods according to the position which gives
best fitness functional value.
The flow chart of Particle Swarm Optimization is depicted in Figure 2.
Figure 2: Flow chart of Particle swarm optimization algorithm (PSOA)
The new position of the particle is given by
x1=x+v1 (5)
where x=candidate solution
v1= updated velocity value
x1= updated particle position
The velocity for the new solution is given as follows:
v1=w*v+c1*(pbest-x)*rand+c2*(gbest-x)*rand (6)
where v1=new solution
rand= a random number in the range of [0,1]
pbest = personal best solution
gbest = global best solution
c1,c2 and w are the PSO parameters
8. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 5, October 2016
26
4.1.3 BEE COLONY ALGORITHM
Bee colony algorithm (BCA) is a stochastic method. Behavior of bees is manipulated through this
algorithm. Possible set of solutions represent the food source position. The amount of nectar
represents the fitness values of all solutions or food source. Generally employed, onlooker and
scout bees are available in bee colony algorithm. At first employed bee produces the new
solution. Onlooker bee chooses an employed bee to improve its solution. According to the fitness
value each candidate solution is done by roulette wheel method through employed bees. Scout
Bees are used when the food source is exhausted. The old food source is replaced by new food
source randomly through scout bees.
The main steps involved in Bee Colony Algorithm are as follows:
Employed bee initiated the generation of food sources and their fitness functional values are
calculated randomly.
According to employed bee new position of food source is generated randomly.
Through onlooker bee selected food sources are improved to produce better results.
Finally the best food source or candidate solution is memorized.
The flow chart of Bee Colony Algorithm is depicted in Figure 3.
Figure 3: Flow chart of bee colony algorithm
The bee colony algorithm (BCA) having few steps, which are replicated till it ceased. The steps
are as follow:
9. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 5, October 2016
27
Step 1.Initially food source population is generated.
Step 2.Evaluate the fitness function values.
Step 3.The current best solution is evaluated.
Step 4.In Employed Bee phase, produce a random solution.
Step 5.Evaluate the fitness function of new solution through employed bee.
Step 6.In Onlooker Bee phase, produce a random solution.
Step 7.According to onlooker bee evaluates the fitness function of new solution.
Step 8. Probability of occurrence of each food source is calculated.
Step 9. Solutions having higher probability factor are improved.
Step 10. Evaluate fitness functional value of the solution and memorize best solution.
Step 11. Continue the process until termination condition reached.
The new solution can be calculated as
c=x(j)+ebf*x(j) (7)
where x(j)= candidate solution at jth
position
ebf = a random number in the range of [-1,+1]
The probability of occurrence for each candidate solution is calculated as follows:
prob(j)=fx(j)/tfx; (8)
Where prob =probability factor
fx(j)=fitness function value
tfx =total fitness value of all candidate solution
In Onlooker Bee phase, the solution having probability greater than a random value within the
range of 0 and 1are selected and there corresponding solutions are improved by using the
equation like
v(j)=x(j)+ebf*x(jj); (9)
where ebf=a random number within a range of [-0.1,+0.1]
4.2. METHODOLOGY:
For Mathematical function
f(x)=1/(abs(suc_bal)+ ε)2
(10)
Where 0.1<= ε <0.9 (taking ε -value because overflow condition due to infinity).
Here Successive Amount (suc_amt) is defined as:
suc_bal = net_bal - (wtd_amt - min_bal) (11)
Where net_bal = current account balance
min_bal= minimum bank balance limit
In Harmony Search algorithm(HSA), each solution is initialized with a musical note. The
musician will search for optimal solutions by changing the pitch and bandwidth of their musical
note. It will keep track of best note in the population and upgrade its solution. Particle swarm
optimization algorithm (PSOA) will keep track of the optimal solution which is population-based
10. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 5, October 2016
28
and upgrade its position or location. In Bee Colony Algorithm (BCA) each solution is initialized
with the food source. The Employed and Onlooker Bee will search for optimal solution. It will
keep track of the population based solution and upgrade its position or location. Optimal solution
is used to maximize the mathematical function f x which may be implemented in Harmony
search algorithm, PSOA and Bee Colony algorithm using MATLAB-7.0.as shown Table-1. This
table primarily focuses on the musician’s attempt to generate the best note, particles’ movement
and movement of employed, Onlooker’s Bee to find the best solution in the search space.
Table 1( Fitness Function Value for each sample space or test case)
Iteration
Number
Test
Cases/Test
Data(HSA)
Fitness
Function
Value
Test
Cases/Test
Data(PSO)
Fitness
Function
Value
Test
Cases/Test
Data(BCO)
Fitness
Function
Value
1 4000 5.9488e-010 4100 5.9779e-010 3900 5.9198e-010
10 5700 6.4746e-010 5900 6.541e-010 4300 6.0368e-010
20 7300 7.0357e-010 7900 7.2652e-010 5000 6.25e-010
40 10800 8.5496e-010 10000 8.1632e-010 7100 6.9618e-010
60 10800 8.5496e-010 17200 1.2939e-009 9700 8.025e-010
80 14300 1.061e-009 21200 1.7654e-009 13900 1.0339e-009
100 16400 1.2225e-009 25200 2.5507e-009 17300 1.3033e-009
120 18700 1.4457e-009 29200 4.0057e-009 27400 3.2282e-009
140 20800 1.7075e-009 33200 7.1813e-009 43300 3.4598e-007
160 24300 2.3338e-009 37200 1.6436e-008 44000 9.9971e-007
180 26600 2.9537e-009 41200 6.9248e-008 44000 9.9979e-007
200 29800 4.3282e-009 44000 9.9964e-007 44000 9.998e-007
220 31000 5.1018e-009 44000 9.9959e-007 44000 9.998e-007
240 33700 7.8313e-009 44000 9.9962e-007 44000 9.998e-007
260 35400 1.085e-008 44000 9.9975e-007 44000 9.998e-007
280 39900 3.8445e-008 44000 9.9969e-007 44000 9.998e-007
300 43500 4.4438e-007 44000 9.9943e-007 44000 9.998e-007
320 43800 6.943e-007 44000 9.9969e-007 44000 9.998e-007
340 44000 9.9976e-007 44000 9.9975e-007 44000 9.998e-007
350 44000 9.9975e-007 44000 9.9979e-007 44000 9.998e-007
In this case, 20 numbers of sample test cases are considered. The function value depends upon the
parametric values of the input variable. It was found that the solution reaches its optimum value
after 198 iterations for particle swarm optimization algorithm (PSOA).After 154 iterations bee
colony algorithm (BCA) reaches its optimum value but in case of harmony search algorithm
(HSA) takes 325 iterations to get the optimal solution.
5. SIMULATION RESULTS
The proposed approach generates the automated test cases through test cases or test data for Bank
ATM by using evolutionary algorithms like Harmony Search, particle swarm optimization and
bee colony methods. The figure-4 shows the relation between two variable quantities which are
fitness function value range and test data measured along one of a pair of axis represented in
table-1.
11. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 5, October 2016
29
Figure 4: Graphical representation of test data and fitness function value for table-1
The proposed approach generates the test data for Bank ATM’s withdrawal operation using
Harmony Search Algorithm, PSOA and BCA. Table-2 represents the range of fitness value with
different test data and also it gives the individual candidate solution according to the fitness value
range in terms of percentage.
Table 2: %of test data in terms of maximum fitness value
FITNESS VALUE RANGE % OF TEST
DATA(HSA)
% OF TEST
DATA(PSO)
% OF TEST
DATA(BCO)
0 ≤ f(x) < 0.3 25 20 10
0.3≤ f(x) < 0.7 55 20 30
0.7≤ f(x) < 1.0 20 60 60
The above table shows that around 60% test cases or test data are having the higher fitness
function f(x) value and lies in between 0.7 and 1.0 by using bee colony and particle swarm
optimization algorithms but in case of harmony search algorithm, only 20% of test data are
available within the higher range of fitness function. By considering all the functional value of
fitness function from table 2 bee colony algorithm is having higher fitness value range as
compared to particle swarm optimization and harmony search algorithm.Figure-2 shows a
pictorial representation of the relation of two variable quantities like percentage of test data and
fitness value range.
0
20000
40000
60000
80000
100000
120000
140000
1 3 5 7 9 11 13 15 17 19
Fitness Function Value
Test Cases/Test
Data(BCO)
Fitness Function Value
Test Cases/Test
Data(PSO)
Fitness Function Value
Test Cases/Test
Data(HSA)
12. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 5, October 2016
30
Figure 5: Graphical representation of % test data and fitness value range for table-2
6. DISCUSSION AND FUTURE SCOPE
By considering the mathematical function fx=1/(abs(net_bal-wd_amt)+ε)2
,where ε varies from
0.1 to 0.9,this proposed paper generates and optimized the test cases as well as test data
automatically through harmony search, particle swarm optimization and bee colony algorithms.
According to harmony search with a harmony it has been found that solution of optimality of
solution keeps track of best and worst member in the harmony memory and updates its solution
accordingly. By considering some sample test cases it has been observed that the functional value
depends upon the parametric values of the input variables like Harmony Memory Considering
Rate, Pitching Adjusting Rate and the Bandwidth. In case of particle swarm optimization
algorithm the particle initialized with current position and velocity it has been found that solution
of optimality of particle keeps track of personal best and global best position and updates its
position and velocity .In this case the function value depends upon the parametric values of the
input variables and the velocity. It has also been seen that the pbest value depends upon the gbest
value and pbest is directly proportional to the gbest value along with test cases. In Bee Colony
Algorithm (BCA) with the bee initialized with current position it has been found that the optimal
solution keeps track of best food source position among a number of food source positions.
According to bee colony algorithm the function value depends upon the parametric values of the
input variables and food source position.
The future approach to this work could enhance the test case or test data generation for large
programs automatically. The different parameters could be added which gives more optimized
test cases and also increases the efficiency of Harmony Search (HS), PSOA and BCA techniques.
Another perspective area could be the randomly generated test data by using various paths
according to the control flow graph (CFG).Test Cases can be generated by using various kinds of
hybrid meta heuristic algorithms like BCFA, FABA, BABC etc. The test data generated by using
HS algorithm is compared with test data generated by PSOA and BCA and it was found that BCA
produces optimal result in very less time and with more accuracy.
0
10
20
30
40
50
60
70
% OF TEST
DATA(HSA)
% OF TEST
DATA(PSO)
% OF TEST
DATA(BCO)
0 ≤ f(x) < 0.3
0.3≤ f(x) < 0.7
0.7≤ f(x) < 1.0
13. International Journal of Computer Science & Information Technology (IJCSIT) Vol 8, No 5, October 2016
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7. CONCLUSION
Evolutionary algorithms like Harmony search (HS), PSOA and bee colony are very important
tools for optimization of test cases or test data. It has been diversified the problems in a very
effective manner for generating the test data automatically. In this paper, HS, PSOA and BCA
have been discussed to generate the optimized test cases by taking an example of withdrawal
operation of an ATM machine. This paper also describes the fundamental notions of HSA, PSOA
and BCA. It also explains how the random test cases are generated and finding the optimal
solution to maximize the problem. This paper will inspire researchers to work on the various
evolutionary algorithms by applying in computer science engineering area to generate the
effective automated test cases.
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