Now the Meta-Heuristic algorithms have been used vastly in solving the problem of continuous optimization. In this paper the Artificial Bee Colony (ABC) algorithm and the Firefly Algorithm (FA) are valuated. And for presenting the efficiency of the algorithms and also for more analysis of them, the continuous optimization problems which are of the type of the problems of vast limit of answer and the
close optimized points are tested. So, in this paper the efficiency of the ABC algorithm and FA are presented for solving the continuous optimization problems and also the said algorithms are studied from the accuracy in reaching the optimized solution and the resulting time and the reliability of the optimized answer points of view.
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.
A NEW APPROACH IN DYNAMIC TRAVELING SALESMAN PROBLEM: A HYBRID OF ANT COLONY ...ijmpict
Nowadays swarm intelligence-based algorithms are being used widely to optimize the dynamic traveling salesman problem (DTSP). In this paper, we have used mixed method of Ant Colony Optimization (AOC) and gradient descent to optimize DTSP which differs with ACO algorithm in evaporation rate and innovative data. This approach prevents premature convergence and scape from local optimum spots and also makes it possible to find better solutions for algorithm. In this paper, we’re going to offer gradient descent and ACO algorithm which in comparison to some former methods it shows that algorithm has significantly improved routes optimization.
Recent research in finding the optimal path by ant colony optimizationjournalBEEI
The computation of the optimal path is one of the critical problems in graph theory. It has been utilized in various practical ranges of real world applications including image processing, file carving and classification problem. Numerous techniques have been proposed in finding optimal path solutions including using ant colony optimization (ACO). This is a nature-inspired metaheuristic algorithm, which is inspired by the foraging behavior of ants in nature. Thus, this paper study the improvement made by many researchers on ACO in finding optimal path solution. Finally, this paper also identifies the recent trends and explores potential future research directions in file carving.
MOCANAR: A Multi-Objective Cuckoo Search Algorithm for Numeric Association Ru...csandit
Extracting association rules from numeric features
involves searching a very large search space. To
deal with this problem, in this paper a meta-heuris
tic algorithm is used that we have called
MOCANAR. The MOCANAR is a Pareto based multi-object
ive cuckoo search algorithm which
extracts high quality association rules from numeri
c datasets. The support, confidence,
interestingness and comprehensibility are the objec
tives that have been considered in the
MOCANAR. The MOCANAR extracts rules incrementally,
in which, in each run of the algorithm, a
small number of high quality rules are made. In thi
s paper, a comprehensive taxonomy of meta-
heuristic algorithm have been presented. Using this
taxonomy, we have decided to use a Cuckoo
Search algorithm because this algorithm is one of t
he most matured algorithms and also, it is simple
to use and easy to comprehend. In addition, until n
ow, to our knowledge this method has not been
used as a multi-objective algorithm and has not bee
n used in the association rule mining area. To
demonstrate the merit and associated benefits of th
e proposed methodology, the methodology has
been applied to a number of datasets and high quali
ty results in terms of the objectives were
extracted
Feature selection using modified particle swarm optimisation for face recogni...eSAT Journals
Abstract
One of the major influential factors which affects the accuracy of classification rate is the selection of right features. Not all features have vital role in classification. Many of the features in the dataset may be redundant and irrelevant, which increase the computational cost and may reduce classification rate. In this paper, we used DCT(Discrete cosine transform) coefficients as features for face recognition application. The coefficients are optimally selected based on a modified PSO algorithm. In this, the choice of coefficients is done by incorporating the average of the mean normalized standard deviations of various classes and giving more weightage to the lower indexed DCT coefficients. The algorithm is tested on ORL database. A recognition rate of 97% is obtained. Average number of features selected is about 40 percent for a 10 × 10 input. The modified PSO took about 50 iterations for convergence. These performance figures are found to be better than some of the work reported in literature.
Keywords: Particle swarm optimization, Discrete cosine transform, feature extraction, feature selection, face recognition, classification rate.
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.
A NEW APPROACH IN DYNAMIC TRAVELING SALESMAN PROBLEM: A HYBRID OF ANT COLONY ...ijmpict
Nowadays swarm intelligence-based algorithms are being used widely to optimize the dynamic traveling salesman problem (DTSP). In this paper, we have used mixed method of Ant Colony Optimization (AOC) and gradient descent to optimize DTSP which differs with ACO algorithm in evaporation rate and innovative data. This approach prevents premature convergence and scape from local optimum spots and also makes it possible to find better solutions for algorithm. In this paper, we’re going to offer gradient descent and ACO algorithm which in comparison to some former methods it shows that algorithm has significantly improved routes optimization.
Recent research in finding the optimal path by ant colony optimizationjournalBEEI
The computation of the optimal path is one of the critical problems in graph theory. It has been utilized in various practical ranges of real world applications including image processing, file carving and classification problem. Numerous techniques have been proposed in finding optimal path solutions including using ant colony optimization (ACO). This is a nature-inspired metaheuristic algorithm, which is inspired by the foraging behavior of ants in nature. Thus, this paper study the improvement made by many researchers on ACO in finding optimal path solution. Finally, this paper also identifies the recent trends and explores potential future research directions in file carving.
MOCANAR: A Multi-Objective Cuckoo Search Algorithm for Numeric Association Ru...csandit
Extracting association rules from numeric features
involves searching a very large search space. To
deal with this problem, in this paper a meta-heuris
tic algorithm is used that we have called
MOCANAR. The MOCANAR is a Pareto based multi-object
ive cuckoo search algorithm which
extracts high quality association rules from numeri
c datasets. The support, confidence,
interestingness and comprehensibility are the objec
tives that have been considered in the
MOCANAR. The MOCANAR extracts rules incrementally,
in which, in each run of the algorithm, a
small number of high quality rules are made. In thi
s paper, a comprehensive taxonomy of meta-
heuristic algorithm have been presented. Using this
taxonomy, we have decided to use a Cuckoo
Search algorithm because this algorithm is one of t
he most matured algorithms and also, it is simple
to use and easy to comprehend. In addition, until n
ow, to our knowledge this method has not been
used as a multi-objective algorithm and has not bee
n used in the association rule mining area. To
demonstrate the merit and associated benefits of th
e proposed methodology, the methodology has
been applied to a number of datasets and high quali
ty results in terms of the objectives were
extracted
Feature selection using modified particle swarm optimisation for face recogni...eSAT Journals
Abstract
One of the major influential factors which affects the accuracy of classification rate is the selection of right features. Not all features have vital role in classification. Many of the features in the dataset may be redundant and irrelevant, which increase the computational cost and may reduce classification rate. In this paper, we used DCT(Discrete cosine transform) coefficients as features for face recognition application. The coefficients are optimally selected based on a modified PSO algorithm. In this, the choice of coefficients is done by incorporating the average of the mean normalized standard deviations of various classes and giving more weightage to the lower indexed DCT coefficients. The algorithm is tested on ORL database. A recognition rate of 97% is obtained. Average number of features selected is about 40 percent for a 10 × 10 input. The modified PSO took about 50 iterations for convergence. These performance figures are found to be better than some of the work reported in literature.
Keywords: Particle swarm optimization, Discrete cosine transform, feature extraction, feature selection, face recognition, classification rate.
A novel population-based local search for nurse rostering problem IJECEIAES
Population-based approaches regularly are better than single based (local search) approaches in exploring the search space. However, the drawback of population-based approaches is in exploiting the search space. Several hybrid approaches have proven their efficiency through different domains of optimization problems by incorporating and integrating the strength of population and local search approaches. Meanwhile, hybrid methods have a drawback of increasing the parameter tuning. Recently, population-based local search was proposed for a university course-timetabling problem with fewer parameters than existing approaches, the proposed approach proves its effectiveness. The proposed approach employs two operators to intensify and diversify the search space. The first operator is applied to a single solution, while the second is applied for all solutions. This paper aims to investigate the performance of population-based local search for the nurse rostering problem. The INRC2010 database with a dataset composed of 69 instances is used to test the performance of PB-LS. A comparison was made between the performance of PB-LS and other existing approaches in the literature. Results show good performances of proposed approach compared to other approaches, where population-based local search provided best results in 55 cases over 69 instances used in experiments.
Improved optimization of numerical association rule mining using hybrid parti...IJECEIAES
Particle Swarm Optimization (PSO) has been applied to solve optimization problems in various fields, such as Association Rule Mining (ARM) of numerical problems. However, PSO often becomes trapped in local optima. Consequently, the results do not represent the overall optimum solutions. To address this limitation, this study aims to combine PSO with the Cauchy distribution (PARCD), which is expected to increase the global optimal value of the expanded search space. Furthermore, this study uses multiple objective functions, i.e., support, confidence, comprehensibility, interestingness and amplitude. In addition, the proposed method was evaluated using benchmark datasets, such as the Quake, Basket ball, Body fat, Pollution, and Bolt datasets. Evaluation results were compared to the results obtained by previous studies. The results indicate that the overall values of the objective functions obtained using the proposed PARCD approach are satisfactory.
Selecting the best stochastic systems for large scale engineering problemsIJECEIAES
Selecting a subset of the best solutions among large-scale problems is an important area of research. When the alternative solutions are stochastic in nature, then it puts more burden on the problem. The objective of this paper is to select a set that is likely to contain the actual best solutions with high probability. If the selected set contains all the best solutions, then the selection is denoted as correct selection. We are interested in maximizing the probability of this selection; P(CS). In many cases, the available computation budget for simulating the solution set in order to maximize P(CS) is limited. Therefore, instead of distributing these computational efforts equally likely among the alternatives, the optimal computing budget allocation (OCBA) procedure came to put more effort on the solutions that have more impact on the selected set. In this paper, we derive formulas of how to distribute the available budget asymptotically to find the approximation of P(CS). We then present a procedure that uses OCBA with the ordinal optimization (OO) in order to select the set of best solutions. The properties and performance of the proposed procedure are illustrated through a numerical example. Overall results indicate that the procedure is able to select a subset of the best systems with high probability of correct selection using small number of simulation samples under different parameter settings.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
A Preference Model on Adaptive Affinity PropagationIJECEIAES
In recent years, two new data clustering algorithms have been proposed. One of them is Affinity Propagation (AP). AP is a new data clustering technique that use iterative message passing and consider all data points as potential exemplars. Two important inputs of AP are a similarity matrix (SM) of the data and the parameter ”preference” p. Although the original AP algorithm has shown much success in data clustering, it still suffer from one limitation: it is not easy to determine the value of the parameter ”preference” p which can result an optimal clustering solution. To resolve this limitation, we propose a new model of the parameter ”preference” p, i.e. it is modeled based on the similarity distribution. Having the SM and p, Modified Adaptive AP (MAAP) procedure is running. MAAP procedure means that we omit the adaptive p-scanning algorithm as in original Adaptive-AP (AAP) procedure. Experimental results on random non-partition and partition data sets show that (i) the proposed algorithm, MAAP-DDP, is slower than original AP for random non-partition dataset, (ii) for random 4-partition dataset and real datasets the proposed algorithm has succeeded to identify clusters according to the number of dataset’s true labels with the execution times that are comparable with those original AP. Beside that the MAAP-DDP algorithm demonstrates more feasible and effective than original AAP procedure.
Particle Swarm Optimization based K-Prototype Clustering Algorithm iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Feature selection in high-dimensional datasets is
considered to be a complex and time-consuming problem. To
enhance the accuracy of classification and reduce the execution
time, Parallel Evolutionary Algorithms (PEAs) can be used. In
this paper, we make a review for the most recent works which
handle the use of PEAs for feature selection in large datasets.
We have classified the algorithms in these papers into four main
classes (Genetic Algorithms (GA), Particle Swarm Optimization
(PSO), Scattered Search (SS), and Ant Colony Optimization
(ACO)). The accuracy is adopted as a measure to compare the
efficiency of these PEAs. It is noticeable that the Parallel Genetic
Algorithms (PGAs) are the most suitable algorithms for feature
selection in large datasets; since they achieve the highest accuracy.
On the other hand, we found that the Parallel ACO is timeconsuming
and less accurate comparing with other PEA.
Optimal rule set generation using pso algorithmcsandit
Classification and Prediction is an important resea
rch area of data mining. Construction of
classifier model for any decision system is an impo
rtant job for many data mining applications.
The objective of developing such a classifier is to
classify unlabeled dataset into classes. Here
we have applied a discrete Particle Swarm Optimizat
ion (PSO) algorithm for selecting optimal
classification rule sets from huge number of rules
possibly exist in a dataset. In the proposed
DPSO algorithm, decision matrix approach was used f
or generation of initial possible
classification rules from a dataset. Then the propo
sed algorithm discovers important or
significant rules from all possible classification
rules without sacrificing predictive accuracy.
The proposed algorithm deals with discrete valued d
ata, and its initial population of candidate
solutions contains particles of different sizes. Th
e experiment has been done on the task of
optimal rule selection in the data sets collected f
rom UCI repository. Experimental results show
that the proposed algorithm can automatically evolv
e on average the small number of
conditions per rule and a few rules per rule set, a
nd achieved better classification performance
of predictive accuracy for few classes.
Multi-Population Methods with Adaptive Mutation for Multi-Modal Optimization ...ijscai
This paper presents an efficient scheme to locate multiple peaks on multi-modal optimization problems by
using genetic algorithms (GAs). The premature convergence problem shows due to the loss of diversity,
the multi-population technique can be applied to maintain the diversity in the population and the
convergence capacity of GAs. The proposed scheme is the combination of multi-population with adaptive
mutation operator, which determines two different mutation probabilities for different sites of the
solutions. The probabilities are updated by the fitness and distribution of solutions in the search space
during the evolution process. The experimental results demonstrate the performance of the proposed
algorithm based on a set of benchmark problems in comparison with relevant algorithms.
A Genetic Algorithm on Optimization Test FunctionsIJMERJOURNAL
ABSTRACT: Genetic Algorithms (GAs) have become increasingly useful over the years for solving combinatorial problems. Though they are generally accepted to be good performers among metaheuristic algorithms, most works have concentrated on the application of the GAs rather than the theoretical justifications. In this paper, we examine and justify the suitability of Genetic Algorithms in solving complex, multi-variable and multi-modal optimization problems. To achieve this, a simple Genetic Algorithm was used to solve four standard complicated optimization test functions, namely Rosenbrock, Schwefel, Rastrigin and Shubert functions. These functions are benchmarks to test the quality of an optimization procedure towards a global optimum. We show that the method has a quicker convergence to the global optima and that the optimal values for the Rosenbrock, Rastrigin, Schwefel and Shubert functions are zero (0), zero (0), -418.9829 and -14.5080 respectively
Enhancing three variants of harmony search algorithm for continuous optimizat...IJECEIAES
Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization problems. Harmony search (HS) is a recognized meta-heuristic algorithm with an efficient exploration process. But the HS has a slow convergence rate, which causes the algorithm to have a weak exploitation process in finding the global optima. Different variants of HS introduced in the literature to enhance the algorithm and fix its problems, but in most cases, the algorithm still has a slow convergence rate. Meanwhile, opposition-based learning (OBL), is an effective technique used to improve the performance of different optimization algorithms, including HS. In this work, we adopted a new improved version of OBL, to improve three variants of Harmony Search, by increasing the convergence rate speed of these variants and improving overall performance. The new OBL version named improved opposition-based learning (IOBL), and it is different from the original OBL by adopting randomness to increase the solution's diversity. To evaluate the hybrid algorithms, we run it on benchmark functions to compare the obtained results with its original versions. The obtained results show that the new hybrid algorithms more efficient compared to the original versions of HS. A convergence rate graph is also used to show the overall performance of the new algorithms.
Using particle swarm optimization to solve test functions problemsriyaniaes
In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.
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.
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.
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.
A novel population-based local search for nurse rostering problem IJECEIAES
Population-based approaches regularly are better than single based (local search) approaches in exploring the search space. However, the drawback of population-based approaches is in exploiting the search space. Several hybrid approaches have proven their efficiency through different domains of optimization problems by incorporating and integrating the strength of population and local search approaches. Meanwhile, hybrid methods have a drawback of increasing the parameter tuning. Recently, population-based local search was proposed for a university course-timetabling problem with fewer parameters than existing approaches, the proposed approach proves its effectiveness. The proposed approach employs two operators to intensify and diversify the search space. The first operator is applied to a single solution, while the second is applied for all solutions. This paper aims to investigate the performance of population-based local search for the nurse rostering problem. The INRC2010 database with a dataset composed of 69 instances is used to test the performance of PB-LS. A comparison was made between the performance of PB-LS and other existing approaches in the literature. Results show good performances of proposed approach compared to other approaches, where population-based local search provided best results in 55 cases over 69 instances used in experiments.
Improved optimization of numerical association rule mining using hybrid parti...IJECEIAES
Particle Swarm Optimization (PSO) has been applied to solve optimization problems in various fields, such as Association Rule Mining (ARM) of numerical problems. However, PSO often becomes trapped in local optima. Consequently, the results do not represent the overall optimum solutions. To address this limitation, this study aims to combine PSO with the Cauchy distribution (PARCD), which is expected to increase the global optimal value of the expanded search space. Furthermore, this study uses multiple objective functions, i.e., support, confidence, comprehensibility, interestingness and amplitude. In addition, the proposed method was evaluated using benchmark datasets, such as the Quake, Basket ball, Body fat, Pollution, and Bolt datasets. Evaluation results were compared to the results obtained by previous studies. The results indicate that the overall values of the objective functions obtained using the proposed PARCD approach are satisfactory.
Selecting the best stochastic systems for large scale engineering problemsIJECEIAES
Selecting a subset of the best solutions among large-scale problems is an important area of research. When the alternative solutions are stochastic in nature, then it puts more burden on the problem. The objective of this paper is to select a set that is likely to contain the actual best solutions with high probability. If the selected set contains all the best solutions, then the selection is denoted as correct selection. We are interested in maximizing the probability of this selection; P(CS). In many cases, the available computation budget for simulating the solution set in order to maximize P(CS) is limited. Therefore, instead of distributing these computational efforts equally likely among the alternatives, the optimal computing budget allocation (OCBA) procedure came to put more effort on the solutions that have more impact on the selected set. In this paper, we derive formulas of how to distribute the available budget asymptotically to find the approximation of P(CS). We then present a procedure that uses OCBA with the ordinal optimization (OO) in order to select the set of best solutions. The properties and performance of the proposed procedure are illustrated through a numerical example. Overall results indicate that the procedure is able to select a subset of the best systems with high probability of correct selection using small number of simulation samples under different parameter settings.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
A Preference Model on Adaptive Affinity PropagationIJECEIAES
In recent years, two new data clustering algorithms have been proposed. One of them is Affinity Propagation (AP). AP is a new data clustering technique that use iterative message passing and consider all data points as potential exemplars. Two important inputs of AP are a similarity matrix (SM) of the data and the parameter ”preference” p. Although the original AP algorithm has shown much success in data clustering, it still suffer from one limitation: it is not easy to determine the value of the parameter ”preference” p which can result an optimal clustering solution. To resolve this limitation, we propose a new model of the parameter ”preference” p, i.e. it is modeled based on the similarity distribution. Having the SM and p, Modified Adaptive AP (MAAP) procedure is running. MAAP procedure means that we omit the adaptive p-scanning algorithm as in original Adaptive-AP (AAP) procedure. Experimental results on random non-partition and partition data sets show that (i) the proposed algorithm, MAAP-DDP, is slower than original AP for random non-partition dataset, (ii) for random 4-partition dataset and real datasets the proposed algorithm has succeeded to identify clusters according to the number of dataset’s true labels with the execution times that are comparable with those original AP. Beside that the MAAP-DDP algorithm demonstrates more feasible and effective than original AAP procedure.
Particle Swarm Optimization based K-Prototype Clustering Algorithm iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Feature selection in high-dimensional datasets is
considered to be a complex and time-consuming problem. To
enhance the accuracy of classification and reduce the execution
time, Parallel Evolutionary Algorithms (PEAs) can be used. In
this paper, we make a review for the most recent works which
handle the use of PEAs for feature selection in large datasets.
We have classified the algorithms in these papers into four main
classes (Genetic Algorithms (GA), Particle Swarm Optimization
(PSO), Scattered Search (SS), and Ant Colony Optimization
(ACO)). The accuracy is adopted as a measure to compare the
efficiency of these PEAs. It is noticeable that the Parallel Genetic
Algorithms (PGAs) are the most suitable algorithms for feature
selection in large datasets; since they achieve the highest accuracy.
On the other hand, we found that the Parallel ACO is timeconsuming
and less accurate comparing with other PEA.
Optimal rule set generation using pso algorithmcsandit
Classification and Prediction is an important resea
rch area of data mining. Construction of
classifier model for any decision system is an impo
rtant job for many data mining applications.
The objective of developing such a classifier is to
classify unlabeled dataset into classes. Here
we have applied a discrete Particle Swarm Optimizat
ion (PSO) algorithm for selecting optimal
classification rule sets from huge number of rules
possibly exist in a dataset. In the proposed
DPSO algorithm, decision matrix approach was used f
or generation of initial possible
classification rules from a dataset. Then the propo
sed algorithm discovers important or
significant rules from all possible classification
rules without sacrificing predictive accuracy.
The proposed algorithm deals with discrete valued d
ata, and its initial population of candidate
solutions contains particles of different sizes. Th
e experiment has been done on the task of
optimal rule selection in the data sets collected f
rom UCI repository. Experimental results show
that the proposed algorithm can automatically evolv
e on average the small number of
conditions per rule and a few rules per rule set, a
nd achieved better classification performance
of predictive accuracy for few classes.
Multi-Population Methods with Adaptive Mutation for Multi-Modal Optimization ...ijscai
This paper presents an efficient scheme to locate multiple peaks on multi-modal optimization problems by
using genetic algorithms (GAs). The premature convergence problem shows due to the loss of diversity,
the multi-population technique can be applied to maintain the diversity in the population and the
convergence capacity of GAs. The proposed scheme is the combination of multi-population with adaptive
mutation operator, which determines two different mutation probabilities for different sites of the
solutions. The probabilities are updated by the fitness and distribution of solutions in the search space
during the evolution process. The experimental results demonstrate the performance of the proposed
algorithm based on a set of benchmark problems in comparison with relevant algorithms.
A Genetic Algorithm on Optimization Test FunctionsIJMERJOURNAL
ABSTRACT: Genetic Algorithms (GAs) have become increasingly useful over the years for solving combinatorial problems. Though they are generally accepted to be good performers among metaheuristic algorithms, most works have concentrated on the application of the GAs rather than the theoretical justifications. In this paper, we examine and justify the suitability of Genetic Algorithms in solving complex, multi-variable and multi-modal optimization problems. To achieve this, a simple Genetic Algorithm was used to solve four standard complicated optimization test functions, namely Rosenbrock, Schwefel, Rastrigin and Shubert functions. These functions are benchmarks to test the quality of an optimization procedure towards a global optimum. We show that the method has a quicker convergence to the global optima and that the optimal values for the Rosenbrock, Rastrigin, Schwefel and Shubert functions are zero (0), zero (0), -418.9829 and -14.5080 respectively
Enhancing three variants of harmony search algorithm for continuous optimizat...IJECEIAES
Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization problems. Harmony search (HS) is a recognized meta-heuristic algorithm with an efficient exploration process. But the HS has a slow convergence rate, which causes the algorithm to have a weak exploitation process in finding the global optima. Different variants of HS introduced in the literature to enhance the algorithm and fix its problems, but in most cases, the algorithm still has a slow convergence rate. Meanwhile, opposition-based learning (OBL), is an effective technique used to improve the performance of different optimization algorithms, including HS. In this work, we adopted a new improved version of OBL, to improve three variants of Harmony Search, by increasing the convergence rate speed of these variants and improving overall performance. The new OBL version named improved opposition-based learning (IOBL), and it is different from the original OBL by adopting randomness to increase the solution's diversity. To evaluate the hybrid algorithms, we run it on benchmark functions to compare the obtained results with its original versions. The obtained results show that the new hybrid algorithms more efficient compared to the original versions of HS. A convergence rate graph is also used to show the overall performance of the new algorithms.
Using particle swarm optimization to solve test functions problemsriyaniaes
In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.
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
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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.
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method is combining the ε-Constraint and the Cuckoo algorithm. First the multi objective problem
transfers into a single-objective problem using ε-Constraint, then the Cuckoo optimization algorithm will
optimize the problem in each task. At last the optimized Pareto frontier will be drawn. The advantage of
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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.
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 Study of Firefly Algorithm and its Application in Non-Linear Dynamic Systemsijtsrd
Firefly Algorithm (FA) is a newly proposed computation technique with inherent parallelism, capable for local as well as global search, meta-heuristic and robust in computing process. In this paper, Firefly Algorithm for Dynamic System (FADS) is a proposed system to find instantaneous behavior of the dynamic system within a single framework based on the idealized behavior of the flashing characteristics of fireflies. Dynamic system where flows of mass and / or energy is cause of dynamicity is generally represented as a set of differential equations and Fourth Order Runge-Kutta (RK4) method is one of used tool for numerical measurement of instantaneous behaviours of dynamic system. In FADS, experimental results are demonstrating the existence of more accurate and effective RK4 technique for the study of dynamic system. Gautam Mahapatra | Srijita Mahapatra | Soumya Banerjee"A Study of Firefly Algorithm and its Application in Non-Linear Dynamic Systems" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd8393.pdf http://www.ijtsrd.com/computer-science/artificial-intelligence/8393/a-study-of-firefly-algorithm-and-its-application-in-non-linear-dynamic-systems/gautam-mahapatra
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EVALUATION THE EFFICIENCY OF ARTIFICIAL BEE COLONY AND THE FIREFLY ALGORITHM IN SOLVING THE CONTINUOUS OPTIMIZATION PROBLEM
1. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013
DOI:10.5121/ijcsa.2013.3403 23
EVALUATION THE EFFICIENCY OF ARTIFICIAL BEE
COLONY AND THE FIREFLY ALGORITHM IN SOLVING
THE CONTINUOUS OPTIMIZATION PROBLEM
Seyyed Reza Khaze1
, Isa maleki2
, Sohrab Hojjatkhah3
and Ali Bagherinia4
1
Department of Computer Engineering, Dehdasht Branch, Islamic Azad University, Iran,
khaze@iaudehdasht.ac.ir, khaze.reza@gmail.com
2
Department of Computer Engineering, Dehdasht Branch, Islamic Azad University, Iran,
maleki@iaudehdasht.ac.ir, maleki.misa@gmail.com
3
Department of Computer Engineering, Dehdasht Branch, Islamic Azad University, Iran,
hojjatkhah@gmail.com
4
Department of Computer Engineering, Dehdasht Branch, Islamic Azad University, Iran,
ali.bagherinia@gmail.com
ABSTRACT
Now the Meta-Heuristic algorithms have been used vastly in solving the problem of continuous
optimization. In this paper the Artificial Bee Colony (ABC) algorithm and the Firefly Algorithm (FA) are
valuated. And for presenting the efficiency of the algorithms and also for more analysis of them, the
continuous optimization problems which are of the type of the problems of vast limit of answer and the
close optimized points are tested. So, in this paper the efficiency of the ABC algorithm and FA are
presented for solving the continuous optimization problems and also the said algorithms are studied from
the accuracy in reaching the optimized solution and the resulting time and the reliability of the optimized
answer points of view.
KEYWORDS
Meta-Heuristic Algorithm, Artificial Bee Colony (ABC), Firefly Algorithm (FA), Continuous Optimization
1. INTRODUCTION
Now the use of the Meta-Heuristic algorithms in accessing the optimized solution in the
continuous optimization problems has progressed a lot. According to the increase of the
complexity of the continuous optimization problems and the inability of the mathematical
methods for the optimized solution, the Meta Heuristic algorithms are the suitable solution for the
continuous optimization problems. The mathematical methods are used in many scientific and
engineering problems and cover a vast area of the different problems but despite the accurate
efficiency, the mathematical methods still face many problems solving the optimization problems.
The late researches and the struggles of the researchers have led to innovation of the algorithms
which have been inspired by the natural phenomenon, the ones which study the completion and
the behavior of the creatures of the nature and finally they have led to the Met-Heuristic
algorithms. The Meta-Heuristic algorithms have been efficient in solving the combined
optimization problems in finding the optimized solution [1, 2, 3 and 4].
Many Meta-Heuristic algorithms have been innovated inspiring the nature of which the Particle
Swarm Optimization (PSO) [5], Artificial Bee Colony (ABC) [6], Firefly Algorithm (FA) [7],
Bee Colony Optimization (BCO) [8] and Ants Colony Optimization (ACO) [9] could be pointed
out. The ABC algorithm [6, 10] is a Meta-Heuristic algorithm which is inspired by the mining
2. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013
24
behavior of the bee colony for solving the continuous optimization problems of large space. ABC
algorithm starts working by creating the primary population of the random vectors. It works in a
way that in any repetition of the algorithm, the artificial bees randomly search the answers which
have been resulted in the previous repetition to find new answers. It is clear that the new answers
would not be necessarily better than the answers found in previous repetition. When any of the
artificial bees find a new answer, they will go back to the hive and will make decision for the next
move path in the next repetition. So, the optimization rate of the answers is calculated by the bees
and then the answer which is more befitting, will be selected as the search path in next repetition.
So, the area around the more optimized answers will be searched by more bees in the next
repetition. The search process continues until the needed conditions for ending the execution of
the program would be met.
The FA [7] is one of the newest Meta-Heuristic algorithms based on the swarm intelligence which
is used in solving the continuous optimization problems. First some artificial firefly are randomly
distributed in the problem space in FA, and then any firefly emit light, the intensity of which is in
conformity to the optimization rate of the point the firefly stands on. Then the light intensity of
any firefly is compared to the light intensity of the fireflies and the low light firefly goes toward
the intense lighted firefly. Also the most intense firefly moves around the problem for finding the
global optimized answer randomly. So, in FA the fireflies get in relationship with each other via
the light. The combination of these operations leads to the movement of the all fireflies toward
the more optimized points. In this paper, we study the ABC algorithm and FA and to show the
efficiency of these algorithms, will solve some continuous optimization functions.
The structure of the paper is as follows: in the section 2, we have introduce the related works; in
the section 3, the Meta-Heuristic algorithm is introduced; in the section 4, the analysis of ABC
and FA are for solving the continuous optimization problems has been studied; in the section 5,
we have evaluated the results of ABC and FA are; in the section 6, ABC and FA are discussed
and at finally in section 7, conclusion and future works is presented.
2. RELATED WORKS
X. Lia [11] has used the Particle Swarm Optimization (PSO) Algorithm and Genetic Algorithm
(GA) to solve the continuous optimization problems. He has studied the PSO and GA algorithms
to test and evaluate the efficiency factor on 36 functions. To clearly show the efficiency of the
PSO and FA algorithms, he tested the functions in 30 dimensional spaces. The results of the tests
show that these algorithms have worked well on the 30 functions and found the optimized
answers. The researchers [12] have used the Dynamic PSO and Simple PSO to solve the
continuous mathematical functions. They have studied the functions in the 2 and 10 dimensional
spaces to show the efficiency of both algorithms. They have studied the parameters of the
Dynamic PSO and Simple PSO to analyze the mathematical functions and they have resulted that
Dynamic PSO is more efficient for solving the continuous problems and creates the answers close
to the optimized one. Reference [13] has used the ABC algorithm and the Differential Evolution
(DE) for solving the optimization problems in large scale. In this reference, a combined method
named DEM-ABC has been suggested. In the combined method for global convergence of the
ABC Algorithm, the DE Mutation Strategy is used. The combined algorithm is tested on some
functions in large scales. The results show that the combined algorithms more efficient than the
ABC Algorithm.
Researchers [14] have used ABC algorithm and Hybrid Artificial Bee Colony (HABC)
Algorithms for solving the continuous optimization problems. The DE algorithm is used for
optimizing the answers of the continuous optimization functions in HABC. They have tested the
ABC and HABC algorithms for evaluation and efficiency factors on 6 functions in the paper. To
3. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013
25
show the efficiency of the ABC and HABC algorithms, they have studied the functions in 30 and
60 dimensional spaces. The results show that HABC is more efficient than ABC algorithm.
Researchers [15] have used FA to solve the non-linear continuous functions. The results of the
tests show that FA is fast enough in convergence toward the optimized solution and finds the
optimized answer in a very short time. Reference [16] has used the ABC algorithm for solving the
continuous optimization functions. Improving the ABC algorithm, it is tried to go through
Exploitation and Local Search operations for reaching the optimized the answer in this reference.
The results show that the improve ABC algorithm has reached more optimized answer than the
ABC algorithm.
Researchers [17] have used the Bacterial Foraging Optimization Algorithm (BFOA) to study and
analyze the continuous functions. They have showed the efficiency of the BFOA by solving some
continuous functions. The goal of these functions is to find the optimized answers in multi-
dimensional spaces. They have also studied the effects of the number of the bacteria on solving
the functions and have cited that accurate identification of the parameters makes BFOA be very
effective in optimizing the problems. Reference [18] has used the ABC algorithm to solve the
continuous optimization functions. To make better global search in this reference, some changes
has took place on exploration and exploitation operations using DE algorithm. The goal was to
make better global search and make ABC algorithm reach the answer as fast as possible. The
results of the tests show that the algorithm is able to find the optimized solution. The researchers
[19] have used FA and PSO algorithms to solve the continuous optimization problems. They have
cited that the PSO algorithm is very efficient in exploration and exploitation operations of the
continuous optimizations problems and also the FA is not very complex and is able to find the
optimized answer in a very short time.
3. META-HEURISTIC ALGORITHMS
The Meta-Heuristic algorithms are the tools for finding the answers or the answers close to the
optimized ones [20]. These algorithms utilize the two concepts of searching and cooperation
search the searching space of an optimization problem. So, the more powerful is an algorithm in
controlling these two parameters, the more able is the algorithm in finding the answers close to
the optimized one for the problem.
3.1 ABC
The ABC algorithm is innovated in 2005 by Karaboga inspiring the social life of the bees to solve
the optimization problems [6]. This algorithm is a simulation of the food search of the group of
the bees. The bees can be distributed in far and different distances to utilize the food resources
[21]. In ABC algorithm, the bees are classified in three groups: 1. Employed bees, 2. Onlooker
bees, 3. Scout bees.
The food search process starts by the employed bees. Each employed bee dances in a specific way
when finds food resource and the onlooker bees look at the dance of the employed bees to
understand the food resource location and the scout bees randomly look for the food in the around
environment.
In ABC algorithm the primary value stages of the employed, onlooker and scout bees are as
follows:
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26
Employed bees: In this stage the artificial bees searching around the food resource at xi point will
search for the better food resource at new location of vi [22]. Identification of the new food
resource takes place by the equation (1) [23].
SNknjxxxv kjijijijij ,...,2,1;,...,2,1)(
)1(
In equation (1), vi= [vi1, vi2,… vin] is the new location vector of the bees, xi= [xi1, xi2, …, xin] is the
location vector of the ith
bee, k (k≠j) is a correct random number in [1, SN] and the SN is the
number of the artificial bees. Φij is a random number uniformly distributed in [-1, 1]. The random
xi number selection from the problem limit is done by the equation (2) [23].
)()1,0( jjjij LUrandLx
)2(
In equation (2), Uj and Lj are the top limit and the down limit of the xi variable respectively and
the rand() is the random numbers function in (0, 1). When the new location of the food resource is
identified, the optimization of it must be calculated. So, the befitting rate of the xi vector is
identified according to the equation (3) [23].
0)(1
0
1
1
ii
i
ii
ffabs
f
ffit
)3(
Onlooker bees: In this stage any of the onlooker bees decide to search around the found food
resource by the specified possibility [22]. The onlooker bees make their selection according to the
possible values of the employed bees. So, the possibility of selection of the food resource by the
onlooker bees is calculated using the equation (4) [23].
SN
j
i
i
i
fit
fit
p
1
)4(
Scout bees: In ABC algorithm, if the known number of the repetitions would not lead to the
optimized answer, some of the bees leave their solution and become scout bees to randomly
search the limits of the problem for increasing the search process efficiency [22]. Execution of the
scout bees’ stage can increase the possibility of finding the global optimized answer.
3.2 FA
FA is of the algorithms based on the population which is introduced in 2008 by Yang [7]. First a
number of artificial bees are randomly produced in problem space in FA. Then a light intense is
related to any of the fireflies using the value found for the goal function at that point. The light
5. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013
27
intense valuing of any of the artificial fireflies is in a way that increasing the optimization rate of
the point a firefly stands on leads to increase of the light intense of it. The low light fireflies are
attracted toward the high light fireflies and this continues till all fireflies are gathered in one point
which is probably the global optimized point. Updating the law of the movement of the low light
bees toward the high light ones takes place using equation (5) [24].
)
2
1
()(
2
0
randxxexx ij
r
ii
ij
)5(
In equation (5), the values of α, β0 and γ are considered constant. α, β0 are selected from [0, 1] and
γ is selected from [0, ∞). Also, rij is the Euclid distance of the two fireflies which is identified as
equation (6) [24].
n
k
kjkijiij xxxxr
1
2
,, )(
)6(
The absorption coefficient between two fireflies takes place using equation (7).
ijr
e
0
)7(
In equation (7), β0 shows the maximum of absorption and is identified in [0, 1]. The γ parameter
is the absorption coefficient and is identified in [0, ∞). The r parameter identifies the distance
between the fireflies and the value of it is calculated by equation (6). If β0=0, any of the fireflies
searches the problem space without any contribution of the other fireflies and the search takes
place randomly. Also, if γ=∞, this leads to the random search in problem space.
4. EVALUATION THE EFFICIENCY OF ABC AND FA FOR SOLVING THE
CONTINUOUS OPTIMIZATION FUNCTIONS
Searching the continuous optimization functions using the Meta-Heuristic algorithms leads to the
most optimized solution among the possible solutions. The Meta-Heuristic algorithms in solving
the continuous optimization problems are very efficient in getting close to the optimized answer.
These algorithms study the continuous optimization problems answers using the testing and the
better searching methods according to the problem structure and the complexity type of it and
produce accessing the optimized answers.
4.1 ABC in Solving the Continuous Optimization Functions
The goal of the optimization is to find the optimized solutions according to the limits and the
needs of the problems. It is possible for an optimization problem to have different solutions and
for selecting the optimized answer the goal function must be used. The flowchart of evaluation of
the goal function of the ABC algorithm for continuous optimization problems is showed in figure
(1).
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Figure 1: The Flowchart of ABC Algorithm for Solving the Continuous Optimization Functions
Figure (2) shows the quasi code of ABC algorithm for solving the continuous optimization
Functions.
Figure 2: The Quasi Code of ABC Algorithm for Solving the Continuous Optimization Functions.
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29
4.2 FA in Solving the Continuous Optimization Functions
Evaluation flowchart of the FA for solving the continuous optimization functions is shown in
figure (3).
Figure 3: Flowchart of the FA for Solving the Continuous Optimization Functions
Figure (4) shows the FA quasi code for solving continuous optimization functions
Figure 4: FA Quasi Code for Solving Continuous Optimization Functions
1. Initialize Parameters
2. Do
3. Evaluation Function
4. Evaluation Fitness
5. The Algorithm of Firefly Algorithm
for i=1 to n do
for j= 1 to i do
Move firefly i towards j
Move firefly i randomly
Evaluate new solutions
End for j
End for i
Rank the fireflies and find the current best
6. While (a stop criteria maximum iteration)
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30
5. EVALUATION AND RESULTS
For evaluation and efficiency of the ABC and FA are, the Rastrigin two and three dimensional
function is studied [25]. This function holds many maximum and minimum points which have
made it be used as a test function for evaluation of the Meta-Heuristic Algorithms. So, for
comparison of the evaluation and efficiency of these algorithms, the Rastrigin function based on
optimized solution factor is used.
Two dimensional Rastrigin function
)]2cos(10[20)(
2
1
2
1 i
i
i xxxf
Three dimensional Rastrigin function
)]2cos(10[30)(
3
1
2
2 i
i
i xxxf
The Meta-Heuristic algorithms are very delicate for their parameters and the settlement of the
parameters can affect their operation. The parameters settlement leads to more flexibility and
reliability of the algorithm. So, settlement of the parameters is one of the important factors in
reaching the optimized solution in continuous optimization problems. The population selection is
very important in Meta-Heuristic algorithms. If the population number is low, the problem will
soon be convergent and we will not get the favored answer or close to the global optimized
answer, and if the population number is high a long time is needed for the algorithm to be
convergent. So, the number of the population must be suitable and in conformity to the
optimization problem to get the optimized solution. For ABC and FA to effectively search the
functions space, the number of primary population is set 50 and the number of repetitions is set
100 for both algorithms. The results of Table (1) show that using the ABC and FA algorithm
makes getting the optimized solution possible. So, ABC and FA are well able to find the
optimized points in continuous optimization problems.
Table 1: Finding the Optimized Solution
FAABCRange of search SpaceFunction
0.12870.0059±30f1
0.75160.0136±30f2
To show the efficiency of ABC and FA are, the convergence diagram is used. The operation of
the algorithms in convergence toward the optimized answer or the suitable number of the
repetitions is showed in figure (5). Studying the diagrams show that first the starting answers of
the algorithms are randomly selected from the answer space and then by repeating the algorithms,
the value of the goal function will get close to the optimized answer.
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Figure 5: The Convergence Diagram of ABC and FA for Solving the Two and Three Dimensional Rastrigin
Function
Table (2) shows the number of the repetition of the execution of ABC and FA are for two
dimensional Rastrigin function. The reason of using the repetitive repetitions is to show the
existence of probability in the structure of these algorithms. The results of Table (2) show the fact
that ABC algorithm is more efficient in finding the global optimized points.
Table 2: Comparison of the Results of ABC and FA for f1 function
IterationAlgorithm
10080604020
0.00590.01481.00561.16101.0372ABC
0.12871.02281.13121.36041.5273FA
Figure (6) shows comparison diagram of the results of ABC and FA for f1 function.
Figure 6: The Comparison Diagram of the Results of ABC and FA for f1 Function
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Table (3) shows the repetition number of execution of ABC and FA for three dimensional
Rastrigin function. As it is seen, ABC algorithm is more efficient in solving the continuous
optimization functions of large dimensions and is more able in finding the global optimized
points. So, ABC algorithm is able to retain the balance of the local and global search of the
problem despite the increase in the dimensions of it in an optimized way.
Table 3: Comparison of the Results of ABC and FA for f2 Function
IterationAlgorithm
10080604020
0.01360.08261.61651.16881.6723ABC
0.75161.17431.62282.12982.2512FA
Figure (7) shows comparison diagram of the results of ABC and FA for f2 function.
Figure 7: The Comparison Diagram of the Results of ABC and FA for f2 Function
6. DISCUSSION
ABC and FA are efficient algorithms in different solving the continuous optimization problems.
These algorithms use the quality parameters number the value of which could be settled easily.
Also the speed of convergence of ABC and FA is very high in probability of finding the global
optimized answer. SO, it is possible to find the optimized solution in continuous optimization
problems using ABC and FA are. One of the merits of ABC algorithm is the abandonment
phenomenon. It means the time the employed bees would not find the optimized solutions after
some repetitions, and then they transform to the scout bees again and move in random paths to
start searching for optimized solutions. By this way the solutions which are not optimized will be
abandoned and again the global optimized points are searched. So, the behaviour of the bees for
finding the optimized points is a combination of the two methods of local and global searching.
In comparison to the other Meta-Heuristic algorithms, ABC algorithm is a sample one to some
extent because this algorithm just uses the three basic specifications of colony population, the
maximum number of the repetitions and the abandonment factor. So, the implementation of ABC
is very simple from calculation point of view and if the right values are used for its parameters,
there would be high probability of finding the global
optimized answer. For escaping the local optimization, ABC algorithm acts in a way that when it
faces such location, the bees will be transferred to other parts of the search space and then will
search the optimized answers there and will repeat this till reaching the global optimized answer.
11. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013
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ABC algorithm is well efficient in multi variable functions and also the functions which have
local minimums and maximums. The movement of the employed bees to go further than the
unsuitable places makes the algorithm work well facing the problems of very high dimensions
and also the problems in which the population is primarily unsuitably distributed. So, the success
rate of ABC algorithm in solving the optimization problems is high. The employed bees try to get
close to the optimized areas an any stage in ABC and finally they try to find the optimized
solutions by the help of scout bees. One of the merits of FA in solving the continuous
optimization of complex functions is that it is able to change status from one optimization point to
the other one. In FA, if the best solution is not found, the search is not stopped and it is done
around the neighbour of the previous points to find the optimized solution. ABC and FA use the
random variables in continuous optimization process. In accurate words, the answers of these
algorithms are probable in their nature. In fact in these algorithms the search process takes place
around the answers of the previous stage. So, these algorithms are able to escape the local
optimized points. So, these algorithms must be executed repetitively to reach the optimized
solution. In ABC algorithm the befitting function is used to get the optimized points and the
problem space is studied more accurately by the cooperation. But in FA as the fireflies gather the
more lighted firefly, it is not possible to get the optimized points well and this leads to increase of
the speed of convergence to the local optimization points.
7. CONCLUSION AND FUTURE WORKS
In this paper we have studied the ABC and FA for solving the continuous optimization problems
of vast area and the answers close to the optimized one. To evaluate the efficiency of the
algorithms, two types of comparison from answer accuracy and reliability in convergence points
have been studied. After studying the results, it became clear that the probability of convergence
and getting the optimized answer via ABC algorithm is a little more than FA in solving the
continuous optimization problems. ABC algorithm is more compatible in solving such problems
and is also more powerful from answering point and when it is convergent, it is faster than
FA.This is not generalizable to the other problems and such verdict is not right for all
optimization problems. So, ABC algorithm is more reliable in reaching the global optimized
points in comparison to FA algorithm in solving the problems like functions of continuous
optimization. We hope in future will use other Meta-Heuristic algorithms for studying the
continuous optimization problems of high dimensions and more optimized points presenting this
paper.
8. REFERENCES
[1] F.S. Gharehchopogh, I. Maleki, S.R. Khaze, “A New Optimization Method for Dynamic Travelling
Salesman Problem with Hybrid Ant Colony Optimization Algorithm and Particle Swarm
Optimization”, International Journal of Advanced Research in Computer Engineering & Technology
(IJARCET), Vol. 2, Issue 2, pp. 352-358, February 2013.
[2] F.S. Gharehchopogh, I.Maleki, S.R.Khaze, “A New Approach in Dynamic Traveling Salesman
Solution: A Hybrid of Ant Colony Optimization and Descending Gradients”,
International Journal of Managing Public Sector Information and Communication Technologies
(IJMPICT), Vol. 3, No. 2, pp. 1-9, December 2012.
[3] I. Maleki, S.R. Khaze, F.S. Gharehchopogh, “A New Solution for Dynamic Travelling Salesman
Problem with Hybrid Ant Colony Optimization Algorithm and Chaos Theory”, International Journal
of Advanced Research in Computer Science (IJARCS), Vol. 3, No. 7, pp. 39-44, Nov-Dec 2012.
[4] F.S. Gharehchopogh, I. Maleki, M. Farahmandian, "New Approach for Solving Dynamic Traveling
Salesman Problem with Hybrid Genetic Algorithms and Ant Colony Optimization", International
Journal of Computer Applications (IJCA), Vol. 53, No.1, pp. 39-44, September 2012.
[5] J. Kennedy, R.C. Eberhart, "Particle Swarm Optimization", In Proceedings of the IEEE International
Conference on Neural Networks, pp. 1942-1948, 1995.
12. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013
34
[6] D. Karaboga, “An idea based on honeybee swarm for numerical optimization”, Technical Report
TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
[7] X.S. Yang, “Nature-Inspired Meta-heuristic Algorithms”, Luniver Press, 2008.
[8] P. Lucic, D. Teodorovic, “Bee system: Modeling Combinatorial Optimization Transportation
Engineering Problems by Swarm Intelligence”, Preprints of the TRISTAN IV Triennial Symposium
on Transportation Analysis, Sao Miguel, Azores Islands, pp. 441-445, 2001.
[9] M. Dorigo, V. Maniezzo, A. Colorni, “Ant system: optimization by a colony of cooperating agents”,
IEEE Trans. on Systems, Man and Cybernetics, Part B, Vol.26, No.1, pp.29-41, 1996.
[10] P. Shunmugapriya , S. Kanmani, “Artificial Bee Colony Approach for Optimizing Feature Selection”,
International Journal of Computer Science Issues, Vol. 9, Issue 3, No. 3, pp. 432-438, May 2012.
[11] X. Lai, “Ensembles of GA and PSO for Real Function Optimization”, Journal of Information &
Computational Science, Vol. 7, No. 3, pp. 667-675, 2010.
[12] H.S. Urade, R. Patel, “Performance Evaluation of Dynamic Particle Swarm Optimization”,
International Journal of Computer Science and Network (IJCSN), Vol. 1, Issue 1, February 2012.
[13] N. Stanarevic, “Hybridizing Artificial Bee Colony (ABC) Algorithm with Differential Evolution for
Large Scale Optimization Problems”, International Journal of Mathematics and Computers in
Simulation, Vol. 6, Issue 1, pp. 194-202, 2012.
[14] X. Kong, S. Liu, Z. Wang, L. Yong, “Hybrid Artificial Bee Colony Algorithm for Global Numerical
Optimization”, Journal of Computational Information Systems, Vol. 8, No. 6, pp. 2367-2374, 2012.
[15] N. Chai-ead, P. Aungkulanon, P. Luangpaiboon, “Bees and Firefly Algorithms for Noisy Non-Linear
Optimization Problems”, Proceedings of the International MultiConference of Engineers and
Computer Scientists(IMECS), Vol. 2, Hong Kong, March 2011.
[16] M.S. Kiran, M. Gunduz, “A Novel Artificial Bee Colony Based Algorithm for Solving the Numerical
Optimization Problems”, International Journal of Innovative Computing Information and Control,
Vol. 8, No. 9, pp. 6107-6121, 2012.
[17] L. Kaur, M. P.Joshi, “Analysis of Chemotaxis in Bacterial Foraging Optimization Algorithm”,
International Journal of Computer Applications, Vol. 46, No. 4, pp. 18-23, May 2012.
[18] J. Qiu, J. Wang, D. Yang, J. Xie, “An Artificial Bee Colony Algorithm with Modified Search
Strategies for Global Numerical Optimization”, Journal of Theoretical and Applied Information
Technology, Vol. 48, No.1, pp. 293-302, February 2013.
[19] S.K. Pal, C.S. Rai, A.P. Singh, “Comparative Study of Firefly Algorithm and Particle Swarm
Optimization for Noisy Non-Linear Optimization Problems”, International Journal Intelligent Systems
and Applications, pp. 50-57, 2012.
[20] M. Baghel, Sh. Agrawal, S. Silakari, “Survey of Metaheuristic Algorithms for Combinatorial
Optimization”, International Journal of Computer Applications, Vol. 58, No.19, pp. 21-31, November
2012.
[21] Sh. Goyal, “The Applications Survey: Bee Colony”, Engineering Science and Technology: An
International Journal (ESTIJ), Vol.2, No. 2, pp. 293-297, April 2012.
[22] W.F. Gao, S.Y. Liu, “A Modified Artificial Bee Colony Algorithm”, Computers & Operations
Research, Vol. 39, pp. 687-697, Elsevier Ltd, 2012.
[23] W. Xiang, M.Q. An, “An Efficient and Robust Artificial Bee Colony Algorithm for Numerical
Optimization”, Computers & Operations Research, Vol. 40, pp. 1256-1265, Elsevier Ltd, 2013.
[24] P.R. Srivatsava, B. Mallikarjun, X.S. Yang, “Optimal Test Sequence Generation using Firefly
Algorithm”, Swarm and Evolutionary Computation, Vol. 8, pp. 44-53, Elsevier B.V, 2013.
[25] M. Molga, C. Smutnicki, “Test functions for optimization needs”, 2005.
Authors
Seyyed Reza Khaze is a Lecturer and Member of the Research Committee of
the Department of Computer Engineering, Dehdasht Branch, Islamic Azad
University, Iran. He is a Member of Editorial Board and Review Board in
Several International Journals and National Conferences. His interested research
areas are in the Software Cost Estimation, Machine learning, Data Mining,
Optimization and Artificial Intelligence.
13. International Journal on Computational Sciences & Applications (IJCSA) Vol.3, No.4, August 2013
35
Isa Maleki is a Lecturer and Member of The Research Committee of The
Department of Computer Engineering, Dehdasht Branch, Islamic Azad
University, Iran. He Also Has Research Collaboration with Dehdasht
Universities Research Association Ngo. He is a Member of Review Board in
Several National Conferences. His Interested Research Areas Are in the
Software Cost Estimation, Machine learning, Data Mining, Optimization and
Artificial Intelligence.
Sohrab Hojjatkhah is Currently Head of the Department of Computer
Engineering, Dehdasht Branch, Islamic Azad University, Iran. He Has a
Bachelor's Degree In Software Engineering, Received From Amir Kabir
University, Iran, Then Received A Master's Degree In Artificial Intelligence
From The University Of Shiraz And Currently PhD Candidate In Department Of
Computer Engineering At Science And Research Branch, Islamic Azad
University, Iran. His Interested Research Areas Are In The Image Processing,
Speech Processing, Machine Learning, Data Mining And Artificial Intelligence.
Ali Bagherinia is a lecturer and member of the Research Committee of the
Department of Computer Engineering, Dehdasht Branch, Islamic Azad
University, Iran. He Has a Currently PhD Candidate In Department Of
Computer Engineering At Science And Research Branch, Islamic Azad
University, Iran. His Interested Research Areas Are in the Wireless Sensor
Networks, data mining, optimization and artificial intelligence.