This Paper provides a brief description of the Genetic Algorithm (GA), the Simulated Annealing (SA) Algorithm, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm and attempts to explain the way as how our Proposed Genetic Algorithm (GA), Proposed Simulated Annealing (SA) Algorithm using GA, Classical Backtracking (BT) Algorithm and Classical Brute Force (BF) Search Algorithm can be employed in finding the best solution of N Queens Problem and also, makes a comparison between these four algorithms. It is entirely a review based work. The four algorithms were written as well as implemented. From the Results, it was found that, the Proposed Genetic Algorithm (GA) performed better than the Proposed Simulated Annealing (SA) Algorithm using GA, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm and it also provided better fitness value (solution) than the Proposed Simulated Annealing Algorithm (SA) using GA, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm, for different N values. Also, it was noticed that, the Proposed GA took more time to provide result than the Proposed SA using GA.
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.
Differential Evolution (DE) is a renowned optimization stratagem that can easily solve nonlinear and comprehensive problems. DE is a well known and uncomplicated population based probabilistic approach for comprehensive optimization. It has apparently outperformed a number of Evolutionary Algorithms and further search heuristics in the vein of Particle Swarm Optimization at what time of testing over both yardstick and actual world problems. Nevertheless, DE, like other probabilistic optimization algorithms, from time to time exhibits precipitate convergence and stagnates at suboptimal position. In order to stay away from stagnation behavior while maintaining an excellent convergence speed, an innovative search strategy is introduced, named memetic search in DE. In the planned strategy, positions update equation customized as per a memetic search stratagem. In this strategy a better solution participates more times in the position modernize procedure. The position update equation is inspired from the memetic search in artificial bee colony algorithm. The proposed strategy is named as Memetic Search in Differential Evolution (MSDE). To prove efficiency and efficacy of MSDE, it is tested over 8 benchmark optimization problems and three real world optimization problems. A comparative analysis has also been carried out among proposed MSDE and original DE. Results show that the anticipated algorithm go one better than the basic DE and its recent deviations in a good number of the experiments.
AN IMPROVED ITERATIVE METHOD FOR SOLVING GENERAL SYSTEM OF EQUATIONS VIA GENE...Zac Darcy
Various algorithms are known for solving linear system of equations. Iteration methods for solving the
large sparse linear systems are recommended. But in the case of general n× m matrices the classic
iterative algorithms are not applicable except for a few cases. The algorithm presented here is based on the
minimization of residual of solution and has some genetic characteristics which require using Genetic
Algorithms. Therefore, this algorithm is best applicable for construction of parallel algorithms. In this
paper, we describe a sequential version of proposed algorithm and present its theoretical analysis.
Moreover we show some numerical results of the sequential algorithm and supply an improved algorithm
and compare the two algorithms.
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.
Differential Evolution (DE) is a renowned optimization stratagem that can easily solve nonlinear and comprehensive problems. DE is a well known and uncomplicated population based probabilistic approach for comprehensive optimization. It has apparently outperformed a number of Evolutionary Algorithms and further search heuristics in the vein of Particle Swarm Optimization at what time of testing over both yardstick and actual world problems. Nevertheless, DE, like other probabilistic optimization algorithms, from time to time exhibits precipitate convergence and stagnates at suboptimal position. In order to stay away from stagnation behavior while maintaining an excellent convergence speed, an innovative search strategy is introduced, named memetic search in DE. In the planned strategy, positions update equation customized as per a memetic search stratagem. In this strategy a better solution participates more times in the position modernize procedure. The position update equation is inspired from the memetic search in artificial bee colony algorithm. The proposed strategy is named as Memetic Search in Differential Evolution (MSDE). To prove efficiency and efficacy of MSDE, it is tested over 8 benchmark optimization problems and three real world optimization problems. A comparative analysis has also been carried out among proposed MSDE and original DE. Results show that the anticipated algorithm go one better than the basic DE and its recent deviations in a good number of the experiments.
AN IMPROVED ITERATIVE METHOD FOR SOLVING GENERAL SYSTEM OF EQUATIONS VIA GENE...Zac Darcy
Various algorithms are known for solving linear system of equations. Iteration methods for solving the
large sparse linear systems are recommended. But in the case of general n× m matrices the classic
iterative algorithms are not applicable except for a few cases. The algorithm presented here is based on the
minimization of residual of solution and has some genetic characteristics which require using Genetic
Algorithms. Therefore, this algorithm is best applicable for construction of parallel algorithms. In this
paper, we describe a sequential version of proposed algorithm and present its theoretical analysis.
Moreover we show some numerical results of the sequential algorithm and supply an improved algorithm
and compare the two algorithms.
TYPE-2 FUZZY LINEAR PROGRAMMING PROBLEMS WITH PERFECTLY NORMAL INTERVAL TYPE-...ijceronline
In this paper, the Perfectly normal Interval Type-2 Fuzzy Linear Programming (PnIT2FLP) model is considered. This model is reduced to crisp linear programming model. This transformation is performed by a proposed ranking method. Based on the proposed fuzzy ranking method and arithmetic operation, the solution of Perfectly normal Interval Type-2 Fuzzy Linear Programming model is obtained by the solutions of linear programming model with help of MATLAB. Finally, the method is illustrated by numerical examples.
In real world applications, most of the optimization problems involve more than one objective to
be optimized. The objectives in most of engineering problems are often conflicting, i.e., maximize
performance, minimize cost, maximize reliability, etc. In the case, one extreme solution would not satisfy
both objective functions and the optimal solution of one objective will not necessary be the best solution
for other objective(s). Therefore different solutions will produce trade-offs between different objectives
and a set of solutions is required to represent the optimal solutions of all objectives. Multi-objective
formulations are realistic models for many complex engineering optimization problems. Customized
genetic algorithms have been demonstrated to be particularly effective to determine excellent solutions to
these problems. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each
of which satisfies the objectives at an acceptable level without being dominated by any other solution. In
this paper, an overview is presented describing various multi objective genetic algorithms developed to
handle different problems with multiple objectives.
ON AVERAGE CASE ANALYSIS THROUGH STATISTICAL BOUNDS : LINKING THEORY TO PRACTICEcscpconf
Theoretical analysis of algorithms involves counting of operations and a separate bound is provided for a specific operation type . Such a methodology is plagued with its inherent
limitations. In this paper we argue as to why we should prefer weight based statistical bounds,which permit mixing of operations, instead as a robust approach. Empirical analysis is an important idea and should be used to supplement and compliment its existing theoretical counterpart as empirically we can work on weights (e.g. time of an operation can be taken as its weight). Not surprisingly, it should not only be taken as an opportunity so as to amend the mistakes already committed knowingly or unknowingly but also to tell a new story.
GENETIC ALGORITHM FOR FUNCTION APPROXIMATION: AN EXPERIMENTAL INVESTIGATIONijaia
Function Approximation is a popular engineering problems used in system identification or Equation
optimization. Due to the complex search space it requires, AI techniques has been used extensively to spot
the best curves that match the real behavior of the system. Genetic algorithm is known for their fast
convergence and their ability to find an optimal structure of the solution. We propose using a genetic
algorithm as a function approximator. Our attempt will focus on using the polynomial form of the
approximation. After implementing the algorithm, we are going to report our results and compare it with
the real function output.
This Presentation discusses he following topics:
Introduction
Need for Problem formulation
Problem Solving Components
Definition of Problem
Problem Limitation
Goal or Solution
Solution Space
Operators
Examples of Problem Formulation
Well-defined Problems and Solution
Examples of Well-Defined Problems
Constraint satisfaction problems (CSPs)
Examples of constraint satisfaction problem
Decision problem
OPTIMAL CHOICE: NEW MACHINE LEARNING PROBLEM AND ITS SOLUTIONijcsity
We introduce the task of learning to pick a single preferred example out a finite set of examples, an
“optimal choice problem”, as a supervised machine learning problem with complex input. Problems of
optimal choice emerge often in various practical applications. We formalize the problem, show that it does
not satisfy the assumptions of statistical learning theory, yet it can be solved efficiently in some cases. We propose two approaches to solve the problem. Both of them reach good solutions on real life data from a signal processing application.
The Nurse Scheduling Problem (NSP), like the well-known Travelling Salesman Problem
(TSP), is an NP-hard problem. In this study, we use a tailor-made meta-heuristic Memetic Algorithm (MA)
to optimize the NSP. The MAis a hybrid algorithm, being a combination of the Genetic Algorithm (GA)
and a local search algorithm. The performance of the MA is found to be superior to that of a solitary
algorithm like GA. The MA solves the NSP in two stages. In the first stage, the randomly generated
solutions are evolved till they become feasible (i.e., the hard constraints are satisfied) and in the second
stage, these solutions are further evolved so as to minimize the violations of the soft constraints. In the final
stage, the MA produces optimal solutions in which the hard as well as the soft constraints are completely
satisfied.
These slides were prepared for a talk I presented at Eindhoven University of Technology, Ghent University, and KU Leuven in June 2019. The main thesis is that project activities are distributed as per the lognormal, but various complications may mask that. To resolve these complications we may need to partition the data, account for the Parkinson effect (early completions may be hidden), and account for rounding. It is also important to note that even under similar conditions some projects are slower on average than others, thus implying that we cannot use the ubiquitous independence assumption. Instead, the simplest model we can recommend is that projects are subject to linear association. Linear Association posits that there is a random bias element representing the between-projects variation. For prediction, we must take into account both the between-projects and within-project variation. If we do all that, we can correct one of the major shortcomings of PERT, namely its reliance on the invalid beta distribution and the independence assumption.
A MODIFIED VORTEX SEARCH ALGORITHM FOR NUMERICAL FUNCTION OPTIMIZATIONijaia
The Vortex Search (VS) algorithm is one of the recently proposed metaheuristic algorithms which was
inspired from the vortical flow of the stirred fluids. Although the VS algorithm is shown to be a good
candidate for the solution of certain optimization problems, it also has some drawbacks. In the VS
algorithm, candidate solutions are generated around the current best solution by using a Gaussian
distribution at each iteration pass. This provides simplicity to the algorithm but it also leads to some
problems along. Especially, for the functions those have a number of local minimum points, to select a
single point to generate candidate solutions leads the algorithm to being trapped into a local minimum
point. Due to the adaptive step-size adjustment scheme used in the VS algorithm, the locality of the created
candidate solutions is increased at each iteration pass. Therefore, if the algorithm cannot escape a local
point as quickly as possible, it becomes much more difficult for the algorithm to escape from that point in
the latter iterations. In this study, a modified Vortex Search algorithm (MVS) is proposed to overcome
above mentioned drawback of the existing VS algorithm. In the MVS algorithm, the candidate solutions
are generated around a number of points at each iteration pass. Computational results showed that with
the help of this modification the global search ability of the existing VS algorithm is improved and the
MVS algorithm outperformed the existing VS algorithm, PSO2011 and ABC algorithms for the benchmark
numerical function set.
EXPERIMENTS ON HYPOTHESIS "FUZZY K-MEANS IS BETTER THAN K-MEANS FOR CLUSTERING"IJDKP
Clustering is one of the data mining techniques that have been around to discover business intelligence by grouping objects into clusters using a similarity measure. Clustering is an unsupervised learning process that has many utilities in real time applications in the fields of marketing, biology, libraries, insurance, city-planning, earthquake studies and document clustering. Latent trends and relationships among data objects can be unearthed using clustering algorithms. Many clustering algorithms came into existence. However, the quality of clusters has to be given paramount importance. The quality objective is to achieve
highest similarity between objects of same cluster and lowest similarity between objects of different clusters. In this context, we studied two widely used clustering algorithms such as the K-Means and Fuzzy K-Means. K-Means is an exclusive clustering algorithm while the Fuzzy K-Means is an overlapping clustering algorithm. In this paper we prove the hypothesis “Fuzzy K-Means is better than K-Means for Clustering” through both literature and empirical study. We built a prototype application to demonstrate the differences between the two clustering algorithms. The experiments are made on diabetes dataset
obtained from the UCI repository. The empirical results reveal that the performance of Fuzzy K-Means is better than that of K-means in terms of quality or accuracy of clusters. Thus, our empirical study proved the hypothesis “Fuzzy K-Means is better than K-Means for Clustering”.
Privacy preserving data mining in four group randomized response technique us...eSAT Journals
Abstract Data mining is a process in which data collected from different sources is analyzed for useful information. Data mining is also known as knowledge discovery in database (KDD). Privacy and accuracy are the important issues in data mining when data is shared. Most of the methods use random permutation techniques to mask the data, for preserving the privacy of sensitive data. Randomize response techniques were developed for the purpose of protecting surveys privacy and avoiding biased answers. The proposed work thesis is to enhance the privacy level in RR technique using four group schemes. First according to the algorithm random attributes a, b, c, d were considered, Then the randomization have been performed on every dataset according to the values of theta. Then ID3 and CART algorithm are applied on the randomized data. The result shows that by increasing the group, the privacy level will increase. This work shows that as compared with three group scheme with four groups scheme the accuracy decreases 6% but the privacy increases 65%.
TYPE-2 FUZZY LINEAR PROGRAMMING PROBLEMS WITH PERFECTLY NORMAL INTERVAL TYPE-...ijceronline
In this paper, the Perfectly normal Interval Type-2 Fuzzy Linear Programming (PnIT2FLP) model is considered. This model is reduced to crisp linear programming model. This transformation is performed by a proposed ranking method. Based on the proposed fuzzy ranking method and arithmetic operation, the solution of Perfectly normal Interval Type-2 Fuzzy Linear Programming model is obtained by the solutions of linear programming model with help of MATLAB. Finally, the method is illustrated by numerical examples.
In real world applications, most of the optimization problems involve more than one objective to
be optimized. The objectives in most of engineering problems are often conflicting, i.e., maximize
performance, minimize cost, maximize reliability, etc. In the case, one extreme solution would not satisfy
both objective functions and the optimal solution of one objective will not necessary be the best solution
for other objective(s). Therefore different solutions will produce trade-offs between different objectives
and a set of solutions is required to represent the optimal solutions of all objectives. Multi-objective
formulations are realistic models for many complex engineering optimization problems. Customized
genetic algorithms have been demonstrated to be particularly effective to determine excellent solutions to
these problems. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each
of which satisfies the objectives at an acceptable level without being dominated by any other solution. In
this paper, an overview is presented describing various multi objective genetic algorithms developed to
handle different problems with multiple objectives.
ON AVERAGE CASE ANALYSIS THROUGH STATISTICAL BOUNDS : LINKING THEORY TO PRACTICEcscpconf
Theoretical analysis of algorithms involves counting of operations and a separate bound is provided for a specific operation type . Such a methodology is plagued with its inherent
limitations. In this paper we argue as to why we should prefer weight based statistical bounds,which permit mixing of operations, instead as a robust approach. Empirical analysis is an important idea and should be used to supplement and compliment its existing theoretical counterpart as empirically we can work on weights (e.g. time of an operation can be taken as its weight). Not surprisingly, it should not only be taken as an opportunity so as to amend the mistakes already committed knowingly or unknowingly but also to tell a new story.
GENETIC ALGORITHM FOR FUNCTION APPROXIMATION: AN EXPERIMENTAL INVESTIGATIONijaia
Function Approximation is a popular engineering problems used in system identification or Equation
optimization. Due to the complex search space it requires, AI techniques has been used extensively to spot
the best curves that match the real behavior of the system. Genetic algorithm is known for their fast
convergence and their ability to find an optimal structure of the solution. We propose using a genetic
algorithm as a function approximator. Our attempt will focus on using the polynomial form of the
approximation. After implementing the algorithm, we are going to report our results and compare it with
the real function output.
This Presentation discusses he following topics:
Introduction
Need for Problem formulation
Problem Solving Components
Definition of Problem
Problem Limitation
Goal or Solution
Solution Space
Operators
Examples of Problem Formulation
Well-defined Problems and Solution
Examples of Well-Defined Problems
Constraint satisfaction problems (CSPs)
Examples of constraint satisfaction problem
Decision problem
OPTIMAL CHOICE: NEW MACHINE LEARNING PROBLEM AND ITS SOLUTIONijcsity
We introduce the task of learning to pick a single preferred example out a finite set of examples, an
“optimal choice problem”, as a supervised machine learning problem with complex input. Problems of
optimal choice emerge often in various practical applications. We formalize the problem, show that it does
not satisfy the assumptions of statistical learning theory, yet it can be solved efficiently in some cases. We propose two approaches to solve the problem. Both of them reach good solutions on real life data from a signal processing application.
The Nurse Scheduling Problem (NSP), like the well-known Travelling Salesman Problem
(TSP), is an NP-hard problem. In this study, we use a tailor-made meta-heuristic Memetic Algorithm (MA)
to optimize the NSP. The MAis a hybrid algorithm, being a combination of the Genetic Algorithm (GA)
and a local search algorithm. The performance of the MA is found to be superior to that of a solitary
algorithm like GA. The MA solves the NSP in two stages. In the first stage, the randomly generated
solutions are evolved till they become feasible (i.e., the hard constraints are satisfied) and in the second
stage, these solutions are further evolved so as to minimize the violations of the soft constraints. In the final
stage, the MA produces optimal solutions in which the hard as well as the soft constraints are completely
satisfied.
These slides were prepared for a talk I presented at Eindhoven University of Technology, Ghent University, and KU Leuven in June 2019. The main thesis is that project activities are distributed as per the lognormal, but various complications may mask that. To resolve these complications we may need to partition the data, account for the Parkinson effect (early completions may be hidden), and account for rounding. It is also important to note that even under similar conditions some projects are slower on average than others, thus implying that we cannot use the ubiquitous independence assumption. Instead, the simplest model we can recommend is that projects are subject to linear association. Linear Association posits that there is a random bias element representing the between-projects variation. For prediction, we must take into account both the between-projects and within-project variation. If we do all that, we can correct one of the major shortcomings of PERT, namely its reliance on the invalid beta distribution and the independence assumption.
A MODIFIED VORTEX SEARCH ALGORITHM FOR NUMERICAL FUNCTION OPTIMIZATIONijaia
The Vortex Search (VS) algorithm is one of the recently proposed metaheuristic algorithms which was
inspired from the vortical flow of the stirred fluids. Although the VS algorithm is shown to be a good
candidate for the solution of certain optimization problems, it also has some drawbacks. In the VS
algorithm, candidate solutions are generated around the current best solution by using a Gaussian
distribution at each iteration pass. This provides simplicity to the algorithm but it also leads to some
problems along. Especially, for the functions those have a number of local minimum points, to select a
single point to generate candidate solutions leads the algorithm to being trapped into a local minimum
point. Due to the adaptive step-size adjustment scheme used in the VS algorithm, the locality of the created
candidate solutions is increased at each iteration pass. Therefore, if the algorithm cannot escape a local
point as quickly as possible, it becomes much more difficult for the algorithm to escape from that point in
the latter iterations. In this study, a modified Vortex Search algorithm (MVS) is proposed to overcome
above mentioned drawback of the existing VS algorithm. In the MVS algorithm, the candidate solutions
are generated around a number of points at each iteration pass. Computational results showed that with
the help of this modification the global search ability of the existing VS algorithm is improved and the
MVS algorithm outperformed the existing VS algorithm, PSO2011 and ABC algorithms for the benchmark
numerical function set.
EXPERIMENTS ON HYPOTHESIS "FUZZY K-MEANS IS BETTER THAN K-MEANS FOR CLUSTERING"IJDKP
Clustering is one of the data mining techniques that have been around to discover business intelligence by grouping objects into clusters using a similarity measure. Clustering is an unsupervised learning process that has many utilities in real time applications in the fields of marketing, biology, libraries, insurance, city-planning, earthquake studies and document clustering. Latent trends and relationships among data objects can be unearthed using clustering algorithms. Many clustering algorithms came into existence. However, the quality of clusters has to be given paramount importance. The quality objective is to achieve
highest similarity between objects of same cluster and lowest similarity between objects of different clusters. In this context, we studied two widely used clustering algorithms such as the K-Means and Fuzzy K-Means. K-Means is an exclusive clustering algorithm while the Fuzzy K-Means is an overlapping clustering algorithm. In this paper we prove the hypothesis “Fuzzy K-Means is better than K-Means for Clustering” through both literature and empirical study. We built a prototype application to demonstrate the differences between the two clustering algorithms. The experiments are made on diabetes dataset
obtained from the UCI repository. The empirical results reveal that the performance of Fuzzy K-Means is better than that of K-means in terms of quality or accuracy of clusters. Thus, our empirical study proved the hypothesis “Fuzzy K-Means is better than K-Means for Clustering”.
Privacy preserving data mining in four group randomized response technique us...eSAT Journals
Abstract Data mining is a process in which data collected from different sources is analyzed for useful information. Data mining is also known as knowledge discovery in database (KDD). Privacy and accuracy are the important issues in data mining when data is shared. Most of the methods use random permutation techniques to mask the data, for preserving the privacy of sensitive data. Randomize response techniques were developed for the purpose of protecting surveys privacy and avoiding biased answers. The proposed work thesis is to enhance the privacy level in RR technique using four group schemes. First according to the algorithm random attributes a, b, c, d were considered, Then the randomization have been performed on every dataset according to the values of theta. Then ID3 and CART algorithm are applied on the randomized data. The result shows that by increasing the group, the privacy level will increase. This work shows that as compared with three group scheme with four groups scheme the accuracy decreases 6% but the privacy increases 65%.
AUTOMATED WORD PREDICTION IN BANGLA LANGUAGE USING STOCHASTIC LANGUAGE MODELSijfcstjournal
Word completion and word prediction are two important phenomena in typing that benefit users who type
using keyboard or other similar devices. They can have profound impact on the typing of disable people.
Our work is based on word prediction on Bangla sentence by using stochastic, i.e. N-gram language model
such as unigram, bigram, trigram, deleted Interpolation and backoff models for auto completing a sentence
by predicting a correct word in a sentence which saves time and keystrokes of typing and also reduces
misspelling. We use large data corpus of Bangla language of different word types to predict correct word
with the accuracy as much as possible. We have found promising results. We hope that our work will
impact on the baseline for automated Bangla typing.
This presentation gives subtle tips on how to chair & conduct important board meetings successfully.
Some tips are very simple, but need to be followed for a successful outcome.
This requires leadership skills.
Decision-making is vital in the business world. From a top management director to a low-level supervisor, everyone makes several decisions each day. To make effective decisions, there are several things we need to know.
REGIONE UMBRIA
10 Novembre 2010
Bando per il finanziamento di interventi volti alla promo-commercializzazione turistica di prodotti tematici e prodotti d’area mediante la realizzazione di progetti integrati collettivi
COMPARATIVE STUDY OF DIFFERENT ALGORITHMS TO SOLVE N QUEENS PROBLEMijfcstjournal
This Paper provides a brief description of the Genetic Algorithm (GA), the Simulated Annealing (SA)
Algorithm, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm and attempts to
explain the way as how the Proposed Genetic Algorithm (GA), the Proposed Simulated Annealing (SA)
Algorithm using GA, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm can be
employed in finding the best solution of N Queens Problem and also, makes a comparison between these
four algorithms. It is entirely a review based work. The four algorithms were written as well as
implemented. From the Results, it was found that, the Proposed Genetic Algorithm (GA) performed better
than the Proposed Simulated Annealing (SA) Algorithm using GA, the Backtracking (BT) Algorithm and
the Brute Force (BF) Search Algorithm and it also provided better fitness value (solution) than the
Proposed Simulated Annealing Algorithm (SA) using GA, the Backtracking (BT) Algorithm and the Brute
Force (BF) Search Algorithm, for different N values. Also, it was noticed that, the Proposed GA took more
time to provide result than the Proposed SA using GA.
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
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
In recent years, consumers and legislation have been pushing companies to optimize their activities in such a way as to reduce negative environmental and social impacts more and more. In the other side, companies
must keep their total supply chain costs as low as possible to remain competitive.This work aims to develop a model to traveling salesman problem including environmental impacts and to identify, as far as possible, the contribution of genetic operator’s tuning and setting in the success and
efficiency of genetic algorithms for solving this problem with consideration of CO2 emission due to transport. This efficiency is calculated in terms of CPU time consumption and convergence of the solution. The best transportation policy is determined by finding a balance between financial and environmental
criteria.Empirically, we have demonstrated that the performance of the genetic algorithm undergo relevant
improvements during some combinations of parameters and operators which we present in our results part.
Artificial Intelligence in Robot Path Planningiosrjce
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.
Simulation-based Optimization of a Real-world Travelling Salesman Problem Usi...CSCJournals
This paper presents a real-world case study of optimizing waste collection in Sweden. The problem, involving approximately 17,000 garbage bins served by three bin lorries, is approached as a travelling salesman problem and solved using simulation-based optimization and an evolutionary algorithm. To improve the performance of the evolutionary algorithm, it is enhanced with a repair function that adjusts its genome values so that shorter routes are found more quickly. The algorithm is tested using two crossover operators, i.e., the order crossover and heuristic crossover, combined with different mutation rates. The results indicate that the order crossover is superior to the heuristics crossover, but that the driving force of the search process is the mutation operator combined with the repair function.
The potential role of ai in the minimisation and mitigation of project delayPieter Rautenbach
Artificial intelligence (AI) can have wide reaching application within the construction
industry, however, the actual application of this set of technologies is currently under exploited. This
paper considers the role that the application of AI can take in optimising the efficiencies of project
execution and how this can potentially reduce project duration and minimise and mitigate delay on
projects.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
Traveling Salesman Problem (TSP) is a kind of NPHard problem which cant be solved in polynomial time for
asymptotically large values of n. In this paper a balanced combination of Genetic algorithm and Simulated Annealing is used. To
improve the performance of finding optimal solution from huge
search space, we have incorporated the use of tournament and
rank as selection operator. And Inver-over operator Mechanism
for crossover and mutation . To illustrate it more clearly an
implementation in C++ (4.9.9.2) has been done.
Index Terms—Genetic Algorithm (GA) , Simulated Annealing
(SA) , Inver-over operator , Lin-Kernighan algorithm , selection
operator , crossover operator , mutation operator.
A MULTI-POPULATION BASED FROG-MEMETIC ALGORITHM FOR JOB SHOP SCHEDULING PROBLEMacijjournal
The Job Shop Scheduling Problem (JSSP) is a well known practical planning problem in the
manufacturing sector. We have considered the JSSP with an objective of minimizing makespan. In this
paper, we develop a three-stage hybrid approach called JSFMA to solve the JSSP. In JSFMA,
considering a method similar to Shuffled Frog Leaping algorithm we divide the population in several sub
populations and then solve the problem using a Memetic algorithm. The proposed approach have been
compared with other algorithms for the Job Shop Scheduling and evaluated with satisfactory results on a
set of the JSSP instances derived from classical Job Shop Scheduling benchmarks. We have solved 20
benchmark problems from Lawrence’s datasets and compared the results obtained with the results of the
algorithms established in the literature. The experimental results show that JSFMA could gain the best
known makespan in 17 out of 20 problems.
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.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
ENHANCING ENGLISH WRITING SKILLS THROUGH INTERNET-PLUS TOOLS IN THE PERSPECTI...ijfcstjournal
This investigation delves into incorporating a hybridized memetic strategy within the framework of English
composition pedagogy, leveraging Internet Plus resources. The study aims to provide an in-depth analysis
of how this method influences students’ writing competence, their perceptions of writing, and their
enthusiasm for English acquisition. Employing an explanatory research design that combines qualitative
and quantitative methods, the study collects data through surveys, interviews, and observations of students’
writing performance before and after the intervention. Findings demonstrate a beneficial impact of
integrating the memetic approach alongside Internet Plus tools on the writing aptitude of English as a
Foreign Language (EFL) learners. Students reported increased engagement with writing, attributing it to
the use of Internet plus tools. They also expressed that the memetic approach facilitated a deeper
understanding of cultural and social contexts in writing. Furthermore, the findings highlight a significant
improvement in students’ writing skills following the intervention. This study provides significant insights
into the practical implementation of the memetic approach within English writing education, highlighting
the beneficial contribution of Internet Plus tools in enriching students' learning journeys.
A SURVEY TO REAL-TIME MESSAGE-ROUTING NETWORK SYSTEM WITH KLA MODELLINGijfcstjournal
Messages routing over a network is one of the most fundamental concept in communication which requires
simultaneous transmission of messages from a source to a destination. In terms of Real-Time Routing, it
refers to the addition of a timing constraint in which messages should be received within a specified time
delay. This study involves Scheduling, Algorithm Design and Graph Theory which are essential parts of
the Computer Science (CS) discipline. Our goal is to investigate an innovative and efficient way to present
these concepts in the context of CS Education. In this paper, we will explore the fundamental modelling of
routing real-time messages on networks. We study whether it is possible to have an optimal on-line
algorithm for the Arbitrary Directed Graph network topology. In addition, we will examine the message
routing’s algorithmic complexity by breaking down the complex mathematical proofs into concrete, visual
examples. Next, we explore the Unidirectional Ring topology in finding the transmission’s
“makespan”.Lastly, we propose the same network modelling through the technique of Kinesthetic Learning
Activity (KLA). We will analyse the data collected and present the results in a case study to evaluate the
effectiveness of the KLA approach compared to the traditional teaching method.
A COMPARATIVE ANALYSIS ON SOFTWARE ARCHITECTURE STYLESijfcstjournal
Software architecture is the structural solution that achieves the overall technical and operational
requirements for software developments. Software engineers applied software architectures for their
software system developments; however, they worry the basic benchmarks in order to select software
architecture styles, possible components, integration methods (connectors) and the exact application of
each style.
The objective of this research work was a comparative analysis of software architecture styles by its
weakness and benefits in order to select by the programmer during their design time. Finally, in this study,
the researcher has been identified architectural styles, weakness, and Strength and application areas with
its component, connector and Interface for the selected architectural styles.
SYSTEM ANALYSIS AND DESIGN FOR A BUSINESS DEVELOPMENT MANAGEMENT SYSTEM BASED...ijfcstjournal
A design of a sales system for professional services requires a comprehensive understanding of the
dynamics of sale cycles and how key knowledge for completing sales is managed. This research describes
a design model of a business development (sales) system for professional service firms based on the Saudi
Arabian commercial market, which takes into account the new advances in technology while preserving
unique or cultural practices that are an important part of the Saudi Arabian commercial market. The
design model has combined a number of key technologies, such as cloud computing and mobility, as an
integral part of the proposed system. An adaptive development process has also been used in implementing
the proposed design model.
AN ALGORITHM FOR SOLVING LINEAR OPTIMIZATION PROBLEMS SUBJECTED TO THE INTERS...ijfcstjournal
Frank t-norms are parametric family of continuous Archimedean t-norms whose members are also strict
functions. Very often, this family of t-norms is also called the family of fundamental t-norms because of the
role it plays in several applications. In this paper, optimization of a linear objective function with fuzzy
relational inequality constraints is investigated. The feasible region is formed as the intersection of two
inequality fuzzy systems defined by frank family of t-norms is considered as fuzzy composition. First, the
resolution of the feasible solutions set is studied where the two fuzzy inequality systems are defined with
max-Frank composition. Second, some related basic and theoretical properties are derived. Then, a
necessary and sufficient condition and three other necessary conditions are presented to conceptualize the
feasibility of the problem. Subsequently, it is shown that a lower bound is always attainable for the optimal
objective value. Also, it is proved that the optimal solution of the problem is always resulted from the
unique maximum solution and a minimal solution of the feasible region. Finally, an algorithm is presented
to solve the problem and an example is described to illustrate the algorithm. Additionally, a method is
proposed to generate random feasible max-Frank fuzzy relational inequalities. By this method, we can
easily generate a feasible test problem and employ our algorithm to it.
LBRP: A RESILIENT ENERGY HARVESTING NOISE AWARE ROUTING PROTOCOL FOR UNDER WA...ijfcstjournal
Underwater detector network is one amongst the foremost difficult and fascinating analysis arenas that
open the door of pleasing plenty of researchers during this field of study. In several under water based
sensor applications, nodes are square measured and through this the energy is affected. Thus, the mobility
of each sensor nodes are measured through the water atmosphere from the water flow for sensor based
protocol formations. Researchers have developed many routing protocols. However, those lost their charm
with the time. This can be the demand of the age to supply associate degree upon energy-efficient and
ascendable strong routing protocol for under water actuator networks. During this work, the authors tend
to propose a customary routing protocol named level primarily based routing protocol (LBRP), reaching to
offer strong, ascendable and energy economical routing. LBRP conjointly guarantees the most effective use
of total energy consumption and ensures packet transmission which redirects as an additional reliability in
compare to different routing protocols. In this work, the authors have used the level of forwarding node,
residual energy and distance from the forwarding node to the causing node as a proof in multicasting
technique comparisons. Throughout this work, the authors have got a recognition result concerning about
86.35% on the average in node multicasting performances. Simulation has been experienced each in a
wheezy and quiet atmosphere which represents the endorsement of higher performance for the planned
protocol.
STRUCTURAL DYNAMICS AND EVOLUTION OF CAPSULE ENDOSCOPY (PILL CAMERA) TECHNOLO...ijfcstjournal
This research paper examined and re-evaluates the technological innovation, theory, structural dynamics
and evolution of Pill Camera(Capsule Endoscopy) technology in redirecting the response manner of small
bowel (intestine) examination in human. The Pill Camera (Endoscopy Capsule) is made up of sealed
biocompatible material to withstand acid, enzymes and other antibody chemicals in the stomach is a
technology that helps the medical practitioners especially the general physicians and the
gastroenterologists to examine and re-examine the intestine for possible bleeding or infection. Before the
advent of the Pill camera (Endoscopy Capsule) the colonoscopy was the local method used but research
showed that some parts (bowel) of the intestine can’t be reach by mere traditional method hence the need
for Pill Camera. Countless number of deaths from stomach disease such as polyps, inflammatory bowel
(Crohn”s diseases), Cancers, Ulcer, anaemia and tumours of small intestines which ordinary would have
been detected by sophisticated technology like Pill Camera has become norm in the developing nations.
Nevertheless, not only will this paper examine and re-evaluate the Pill Camera Innovation, theory,
Structural dynamics and evolution it unravelled and aimed to create awareness for both medical
practitioners and the public.
AN OPTIMIZED HYBRID APPROACH FOR PATH FINDINGijfcstjournal
Path finding algorithm addresses problem of finding shortest path from source to destination avoiding
obstacles. There exist various search algorithms namely A*, Dijkstra's and ant colony optimization. Unlike
most path finding algorithms which require destination co-ordinates to compute path, the proposed
algorithm comprises of a new method which finds path using backtracking without requiring destination
co-ordinates. Moreover, in existing path finding algorithm, the number of iterations required to find path is
large. Hence, to overcome this, an algorithm is proposed which reduces number of iterations required to
traverse the path. The proposed algorithm is hybrid of backtracking and a new technique(modified 8-
neighbor approach). The proposed algorithm can become essential part in location based, network, gaming
applications. grid traversal, navigation, gaming applications, mobile robot and Artificial Intelligence.
EAGRO CROP MARKETING FOR FARMING COMMUNITYijfcstjournal
The Major Occupation in India is the Agriculture; the people involved in the Agriculture belong to the poor
class and category. The people of the farming community are unaware of the new techniques and Agromachines, which would direct the world to greater heights in the field of agriculture. Though the farmers
work hard, they are cheated by agents in today’s market. This serves as a opportunity to solve
all the problems that farmers face in the current world. The eAgro crop marketing will serve as a better
way for the farmers to sell their products within the country with some mediocre knowledge about using
the website. This would provide information to the farmers about current market rate of agro-products,
their sale history and profits earned in a sale. This site will also help the farmers to know about the market
information and to view agricultural schemes of the Government provided to farmers.
EDGE-TENACITY IN CYCLES AND COMPLETE GRAPHSijfcstjournal
It is well known that the tenacity is a proper measure for studying vulnerability and reliability in graphs.
Here, a modified edge-tenacity of a graph is introduced based on the classical definition of tenacity.
Properties and bounds for this measure are introduced; meanwhile edge-tenacity is calculated for cycle
graphs and also for complete graphs.
PSTECEQL: A NOVEL EVENT QUERY LANGUAGE FOR VANET’S UNCERTAIN EVENT STREAMSijfcstjournal
In recent years, the complex event processing technology has been used to process the VANET’s temporal
and spatial event streams. However, we usually cannot get the accurate data because the device sensing
accuracy limitations of the system. We only can get the uncertain data from the complex and limited
environment of the VANET. Because the VANET’s event streams are consist of the uncertain data, so they
are also uncertain. How effective to express and process these uncertain event streams has become the core
issue for the VANET system. To solve this problem, we propose a novel complex event query language
PSTeCEQL (probabilistic spatio-temporal constraint event query language). Firstly, we give the definition
of the possible world model of VANET’s uncertain event streams. Secondly, we propose an event query
language PSTeCEQL and give the syntax and the operational semantics of the language. Finally, we
illustrate the validity of the PSTeCEQL by an example.
CLUSTBIGFIM-FREQUENT ITEMSET MINING OF BIG DATA USING PRE-PROCESSING BASED ON...ijfcstjournal
Now a day enormous amount of data is getting explored through Internet of Things (IoT) as technologies
are advancing and people uses these technologies in day to day activities, this data is termed as Big Data
having its characteristics and challenges. Frequent Itemset Mining algorithms are aimed to disclose
frequent itemsets from transactional database but as the dataset size increases, it cannot be handled by
traditional frequent itemset mining. MapReduce programming model solves the problem of large datasets
but it has large communication cost which reduces execution efficiency. This proposed new pre-processed
k-means technique applied on BigFIM algorithm. ClustBigFIM uses hybrid approach, clustering using kmeans algorithm to generate Clusters from huge datasets and Apriori and Eclat to mine frequent itemsets
from generated clusters using MapReduce programming model. Results shown that execution efficiency of
ClustBigFIM algorithm is increased by applying k-means clustering algorithm before BigFIM algorithm as
one of the pre-processing technique.
A MUTATION TESTING ANALYSIS AND REGRESSION TESTINGijfcstjournal
Software testing is a testing which conducted a test to provide information to client about the quality of the
product under test. Software testing can also provide an objective, independent view of the software to
allow the business to appreciate and understand the risks of software implementation. In this paper we
focused on two main software testing –mutation testing and mutation testing. Mutation testing is a
procedural testing method, i.e. we use the structure of the code to guide the test program, A mutation is a
little change in a program. Such changes are applied to model low level defects that obtain in the process
of coding systems. Ideally mutations should model low-level defect creation. Mutation testing is a process
of testing in which code is modified then mutated code is tested against test suites. The mutations used in
source code are planned to include in common programming errors. A good unit test typically detects the
program mutations and fails automatically. Mutation testing is used on many different platforms, including
Java, C++, C# and Ruby. Regression testing is a type of software testing that seeks to uncover
new software bugs, or regressions, in existing functional and non-functional areas of a system after
changes such as enhancements, patches or configuration changes, have been made to them. When defects
are found during testing, the defect got fixed and that part of the software started working as needed. But
there may be a case that the defects that fixed have introduced or uncovered a different defect in the
software. The way to detect these unexpected bugs and to fix them used regression testing. The main focus
of regression testing is to verify that changes in the software or program have not made any adverse side
effects and that the software still meets its need. Regression tests are done when there are any changes
made on software, because of modified functions.
GREEN WSN- OPTIMIZATION OF ENERGY USE THROUGH REDUCTION IN COMMUNICATION WORK...ijfcstjournal
Advances in micro fabrication and communication techniques have led to unimaginable proliferation of
WSN applications. Research is focussed on reduction of setup operational energy costs. Bulk of operational
energy costs are linked to communication activities of WSN. Any progress towards energy efficiency has a
potential of huge savings globally. Therefore, every energy efficient step is an endeavour to cut costs and
‘Go Green’. In this paper, we have proposed a framework to reduce communication workload through: Innetwork compression and multiple query synthesis at the base-station and modification of query syntax
through introduction of Static Variables. These approaches are general approaches which can be used in
any WSN irrespective of application.
A NEW MODEL FOR SOFTWARE COSTESTIMATION USING HARMONY SEARCHijfcstjournal
Accurate and realistic estimation is always considered to be a great challenge in software industry.
Software Cost Estimation (SCE) is the standard application used to manage software projects. Determining
the amount of estimation in the initial stages of the project depends on planning other activities of the
project. In fact, the estimation is confronted with a number of uncertainties and barriers’, yet assessing the
previous projects is essential to solve this problem. Several models have been developed for the analysis of
software projects. But the classical reference method is the COCOMO model, there are other methods
which are also applied such as Function Point (FP), Line of Code(LOC); meanwhile, the expert`s opinions
matter in this regard. In recent years, the growth and the combination of meta-heuristic algorithms with
high accuracy have brought about a great achievement in software engineering. Meta-heuristic algorithms
which can analyze data from multiple dimensions and identify the optimum solution between them are
analytical tools for the analysis of data. In this paper, we have used the Harmony Search (HS)algorithm for
SCE. The proposed model which is a collection of 60 standard projects from Dataset NASA60 has been
assessed.The experimental results show that HS algorithm is a good way for determining the weight
similarity measures factors of software effort, and reducing the error of MRE.
AGENT ENABLED MINING OF DISTRIBUTED PROTEIN DATA BANKSijfcstjournal
Mining biological data is an emergent area at the intersection between bioinformatics and data mining
(DM). The intelligent agent based model is a popular approach in constructing Distributed Data Mining
(DDM) systems to address scalable mining over large scale distributed data. The nature of associations
between different amino acids in proteins has also been a subject of great anxiety. There is a strong need to
develop new models and exploit and analyze the available distributed biological data sources. In this study,
we have designed and implemented a multi-agent system (MAS) called Agent enriched Quantitative
Association Rules Mining for Amino Acids in distributed Protein Data Banks (AeQARM-AAPDB). Such
globally strong association rules enhance understanding of protein composition and are desirable for
synthesis of artificial proteins. A real protein data bank is used to validate the system.
International Journal on Foundations of Computer Science & Technology (IJFCST)ijfcstjournal
International Journal on Foundations of Computer Science & Technology (IJFCST) is a Bi-monthly peer-reviewed and refereed open access journal that publishes articles which contribute new results in all areas of the Foundations of Computer Science & Technology. Over the last decade, there has been an explosion in the field of computer science to solve various problems from mathematics to engineering. This journal aims to provide a platform for exchanging ideas in new emerging trends that needs more focus and exposure and will attempt to publish proposals that strengthen our goals. Topics of interest include, but are not limited to the following:
Because the technology is used largely in the last decades; cybercrimes have become a significant
international issue as a result of the huge damage that it causes to the business and even to the ordinary
users of technology. The main aims of this paper is to shed light on digital crimes and gives overview about
what a person who is related to computer science has to know about this new type of crimes. The paper has
three sections: Introduction to Digital Crime which gives fundamental information about digital crimes,
Digital Crime Investigation which presents different investigation models and the third section is about
Cybercrime Law.
DISTRIBUTION OF MAXIMAL CLIQUE SIZE UNDER THE WATTS-STROGATZ MODEL OF EVOLUTI...ijfcstjournal
In this paper, we analyze the evolution of a small-world network and its subsequent transformation to a
random network using the idea of link rewiring under the well-known Watts-Strogatz model for complex
networks. Every link u-v in the regular network is considered for rewiring with a certain probability and if
chosen for rewiring, the link u-v is removed from the network and the node u is connected to a randomly
chosen node w (other than nodes u and v). Our objective in this paper is to analyze the distribution of the
maximal clique size per node by varying the probability of link rewiring and the degree per node (number
of links incident on a node) in the initial regular network. For a given probability of rewiring and initial
number of links per node, we observe the distribution of the maximal clique per node to follow a Poisson
distribution. We also observe the maximal clique size per node in the small-world network to be very close
to that of the average value and close to that of the maximal clique size in a regular network. There is no
appreciable decrease in the maximal clique size per node when the network transforms from a regular
network to a small-world network. On the other hand, when the network transforms from a small-world
network to a random network, the average maximal clique size value decreases significantly
A STATISTICAL COMPARATIVE STUDY OF SOME SORTING ALGORITHMSijfcstjournal
This research paper is a statistical comparative study of a few average case asymptotically optimal sorting
algorithms namely, Quick sort, Heap sort and K- sort. The three sorting algorithms all with the same
average case complexity have been compared by obtaining the corresponding statistical bounds while
subjecting these procedures over the randomly generated data from some standard discrete and continuous
probability distributions such as Binomial distribution, Uniform discrete and continuous distribution and
Poisson distribution. The statistical analysis is well supplemented by the parameterized complexity
analysis
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
TOP 10 B TECH COLLEGES IN JAIPUR 2024.pptxnikitacareer3
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Comparative study of different algorithms
1. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.5, No.2, March 2015
DOI:10.5121/ijfcst.2015.5202 15
COMPARATIVE STUDY OF DIFFERENT ALGORITHMS
TO SOLVE N QUEENS PROBLEM
Soham Mukherjee1
, Santanu Datta1
, Pramit Brata Chanda2
, Pratik Pathak1
1
Computer Science & Engineering, M.Tech, Academy of Technology, Hooghly, India
2
Computer Science & Engineering, M.Tech, Kalyani Govt. Engg. College, Kalyani, India
ABSTRACT
This Paper provides a brief description of the Genetic Algorithm (GA), the Simulated Annealing (SA) Algorithm, the
Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm and attempts to explain the way as how our
Proposed Genetic Algorithm (GA), Proposed Simulated Annealing (SA) Algorithm using GA, Classical Backtracking
(BT) Algorithm and Classical Brute Force (BF) Search Algorithm can be employed in finding the best solution of N
Queens Problem and also, makes a comparison between these four algorithms. It is entirely a review based work. The
four algorithms were written as well as implemented. From the Results, it was found that, the Proposed Genetic
Algorithm (GA) performed better than the Proposed Simulated Annealing (SA) Algorithm using GA, the Backtracking
(BT) Algorithm and the Brute Force (BF) Search Algorithm and it also provided better fitness value (solution) than the
Proposed Simulated Annealing Algorithm (SA) using GA, the Backtracking (BT) Algorithm and the Brute Force (BF)
Search Algorithm, for different N values. Also, it was noticed that, the Proposed GA took more time to provide result
than the Proposed SA using GA.
KEYWORDS
Tractable and Intractable Problems, N Queens Problem, Genetic Algorithm, Simulated Annealing
Algorithm,Backtracking Algorithm, Brute Force Search Algorithm, Fitness, No. of Solutions, Time.
1. INTRODUCTION
Depending on the classification of functions into polynomial and exponential, we can divide
computational problems into two types-tractable and intractable. So, the tractability of a problem
depends on how difficult the problem is w.r.t. the amount of time it takes to successfully solve the
problem. This is related to the time complexity of the problem. If a problem has given solution in
a small amount of time, then it can be easily solved in polynomial time and named as tractable
problem. But, there are some problems, which can only be solved by some algorithms, whose
execution time grows very quickly in case of larger input size and these problems cannot be
solved in polynomial time by a conventional algorithm. These problems are named as intractable
[23]. The N Queens Problem is a classical intractable problem, which is often used in case of
discussing about various searching problems. In general, the goal is to place N number of queens
on the (N*N) chessboard, so that no two queens can threaten each other [4]. According to the
rule of this problem, a queen can move in either along a row, or along a column, or along a
diagonal. In an (N*N) chessboard, each of the N queens will be located on exactly one row, one
column, and two diagonals. The rest of the paper is organized as follows. The Overview of
Genetic Algorithm is discussed in Section 2. The Overview of SA Algorithm is narrated in
Section 3. The Overviews of Backtracking and Brute Force Search Algorithms are given in
2. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.5, No.2, March 2015
16
Section 4 and 5 respectively. A Theoretical Comparison among the used Algorithms is made in
Section 6. The Proposed Work and Algorithms are explained in Section 7. The Results are shown
in Section 8 with Analysis and Discussion in Section 9 and ultimate Conclusion is made in
Section 10, followed by Future Works in Section 11, Acknowledgement and finally, References.
Fig. 1: Queens’ Position in (4*4) Chess Board
2. OVERVIEW OF GENETIC ALGORITHM
Genetic Algorithm is an optimization algorithm, which follows the concept of natural selection. It
is a probabilistic search method, based on the ideas of evolution. It follows the Darwinian’s
Survival of the Fittest Principle. Unlike the traditional methods, it is suitable for many real world
problems, in which the goal is to find optimal solution. It is a process, which operates on
chromosomes. It differs from classical search algorithms. It searches among a group of points,
rather than a single point; and works with a coding of parameter set, rather than the parameters
themselves. The method of the genetic algorithm is probabilistic; whereas, traditional algorithms
use deterministic methods. Because of these features of genetic algorithm, it is used as a general
optimization algorithm. It also searches in irregular search areas and hence, it is applied to
varieties function optimization. In case of GA, a population of strings is used, and these strings
are named as chromosomes. Genetic Algorithm can reach to a solution, close to the global
optima. It has advantages over traditional algorithms, which cannot always achieve a global or
close to global optima. But, it does not always guarantee optimal solution, because of its
randomness [5, 19]. The steps of Genetic Algorithm are given in Fig. 2.
Fig. 2: Steps of Genetic Algorithm
Q1
Q2
Q3
Q4
Step 1: Create an initial population of solutions of a certain size randomly (Parent Pool).
Step 2: Evaluate each solution in the population and assign it a fitness value.
Step 3: Select good solutions based on fitness values and discard the rest of the solutions.
Step 4: If suitable solution(s) found in the current generation or maximum number of
generations has completed, then stop.
Step 5: Otherwise, Change the population using crossover and mutation to create a new
population of solutions (Child Pool).
Step 6: Copy the Child Pool to the Parent Pool.
Step 7: Go to step 2.
3. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.5, No.2, March 2015
17
3. OVERVIEW OF SIMULATED ANNEALING ALGORITHM
Simulated Annealing (SA) is a probabilistic search and optimization method. It is very often used,
when the search space is discrete. It follows “Annealing”, which is a process, in which metals are
cooled gradually to make them reach a state of less energy, where they are very strong. Simulated
Annealing is a meta-heuristic optimization algorithm. The random movement occurs at high
temperature and at low temperature, there is very less randomness. Simulated Annealing, at each
step, picks a variable randomly; and then, it also picks a value randomly. If giving that value to
the variable makes an improvement to the solution or keeps it same, then the algorithm accepts
the assignment and there comes a new current value. Otherwise, it accepts the value with some
probability, depending on the temperature and how much bad the value is than the current value.
If the change to the system is not accepted, then the current value is unaltered [3, 15]. It is better
than Hill Climbing Algorithm, where bad states are not accepted at any cost and global optima
may not be reached in case of most of the problems. The steps of SA Algorithm are given in Fig.
3.
Fig. 3: Steps of Simulated Annealing Algorithm
4. OVERVIEW OF BACKTRACKING
It is a general algorithm for finding the solutions of some problem, which use the concept of a
"partial candidate solution" and a quick test of whether it can possibly be completed to a valid
solution or not. When it is applicable, backtracking is often much faster than brute force search,
since it can eliminate a large number of candidates with a single test. Backtracking is an
important tool for solving problems like Crosswords, Sudoku and many other puzzles etc. It is
often the most effective technique for the knapsack problem and other optimization problems.
Backtracking depends on user-defined procedures, which define the problem to be solved, the
nature of the partial candidates and how they are extended into complete solution. It is, therefore,
not a specific algorithm – although, unlike many other non-specific algorithms, it is guaranteed to
find all solutions to a problem in a comparatively less amount of time [20].
5. OVERVIEW OF BRUTE FORCE SEARCH
Brute Force Search, which is also known as Generate and Test, is a very well known algorithm,
which examines all possible candidates for the solution and checks, whether each candidate
satisfies the problem's criteria or not. A brute force search algorithm for the n queens problem
will examine all possible placements of n queens on the (n*n) chessboard, and, for each
placement, check, whether each queen can attack any other or not. The basic idea of the brute
force search algorithm for n queen’s problem is to place each of the n queens on all possible
Step 1: Start with a random initial placement. Initialize a high temperature.
Step 2: Modify the placement through a defined change.
Step 3: Calculate the difference in the energy due to the change made.
Step 4: Depending on the difference in energy, accept or reject the change.
Step 5: Update the temperature value by reducing the temperature very slowly.
Step 6: Go back to Step 2 [16, 18].
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positions and check each time, whether the queens threaten each other. If not, then it has reached
a solution. Brute-force search is very simple to implement, and it will always find a solution, if it
exists. Its complexity grows very quickly, as the problem size increases. Therefore, brute-force
search is mostly used, when the problem size is relatively small. It is also used, when the
simplicity of implementation is more crucial than speed [21].
6. THEORITICAL COMPARISON AMONG THE USED
ALGORITHMS
Genetic Algorithm (GA) and Simulated Annealing (SA) Algorithm are both Optimization
Algorithms, while Backtracking (BT) and Brute Force (BF) Search Algorithms are both not at all
Optimization Algorithms. Their methodology is quite different. Let us make a brief comparison
among GA, SA, BT and BF Algorithms. In SA, we discuss about solutions, temperature,
neighbours, moves etc. But with GA, we discuss about chromosomes, fitness,
selection, crossover, mutation, elitism etc. SA makes a new solution by modifying a solution by
making a move. But GA makes solutions using the combination of two or three different
solutions [22]. GA is a heuristic Algorithm, while SA is a meta-heuristic Algorithm. In GA, a
few probabilities like crossover probability, mutation probability are there; while in SA, there is
only one probability used; i.e., probability to accept a bad state. Unlike GA, a term Entropy is
used in SA. It is an extremely important term in SA. Both GA and SA have some randomness. In
case of BT and BF Algorithms, there is no randomness. They are very much similar, but little bit
different in their methodologies. BF uses the concept Generate and Test. It generates a solution
and then tests that solution to check, whether it is correct or not. But, BT builds and checks each
and every partial solution and discards wrong partial solutions. It uses the concept Test and
Generate. Hence, BT takes less time to solve problems than BF. But, both these two algorithms
are not at all efficient and effective to solve problems, when the input size becomes large. These
algorithms are good for solving problems, when the input size is relatively small.
7. PROPOSED WORK AND ALGORITHMS
In both of the Proposed Genetic Algorithm (GA) and Proposed Simulated Annealing (SA)
Algorithm using GA, our entire concentration was focused on the fitnesses of the solutions, as in
case of Genetic Algorithm, fitness is the most important factor, which signifies the goodness and
optimality of the solution. We also had a good and conscious look on the execution time. Here,
we applied some modified approaches in both of the proposed algorithms. Firstly, in both
algorithms, we didn’t start with unique valued population of chromosomes. We just randomly
generated the initial population (Parent Pool) of chromosomes and therefore, some duplicate
valued chromosomes may be present in that population. So, initially, there may be column
conflicts in that population, which are completely eliminated, when we mutated the child
population (Child Pool), that was generated after the two point crossover operation in the
Proposed GA, which may also increase the column conflicts in the population of chromosomes.
Also, the same mutation operation is done on the child population (Child Pool), which was
generated after the evaluation operation in the Proposed SA using GA. We also used the
temperature loop operation as the generation loop operation; in the SA Algorithm using GA.
Most important of all; as mentioned earlier, we always concentrated hard on the fitness of the
solutions. Hence, in the Proposed GA, we, not only just copied the Child Pool to the Parent Pool,
but also took the best fitness of the Parent Pool and the Child Pool and compared between them.
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Then, we copied the corresponding best fitness’ chromosome (between the two best fitness’
chromosomes) to the 0th
location of the New Parent Pool and showed the corresponding fitness as
the “GA Fitness” in the Result (8) Section. In both the proposed algorithms, we evaluated the
solutions by using the formula (n
c2 – the no. of diagonal conflicts), where n
c2 is the maximum
number of non-attacking queen pairs. The Proposed Genetic Algorithm (GA) and the Proposed
Simulated Annealing (SA) Algorithm using GA are given in Fig. 4 and Fig. 5 respectively.
7.1 ALGORITHM N_QUEEN_SOLUTION BY PROPOSED GA
Input: A Population (Group) of Solutions (Chromosomes), each representing the placement of N
number of Queens in (N*N) Chessboard.
Output: The Optimal Solution (Fitness), representing the placement of N number of Queens in
the (N*N) Chessboard according to the rule of the N Queens Problem.
Fig. 4: Proposed Genetic Algorithm to solve N Queens Problem
7.2 ALGORITHM N_QUEEN_SOLUTION BY PROPOSED SA USING GA
Input: A Solution (Chromosome), representing the placement of N number of Queens in the
(N*N) Chessboard.
Output: The Optimal Solution (Fitness), representing the placement of N number of Queens in
the (N*N) Chessboard according to the rule of the N Queens Problem.
Step 1: Randomly generate the Initial Population of Queens (Parent Pool).
Step 2: Evaluate the chromosomes of the Parent Pool as:
2.1: Fitness function is the no. of non-attacking queen pairs=nc2 (maximum value).
2.2: Calculate the fitness value of each of the chromosomes as: ((nc2)-the no. of
diagonal conflicts).
Step 3: Find the best fitness’ chromosome of the Parent Pool.
Loop (generation)
Step 4: Perform Tournament Selection as the selection procedure.
Step 5: Now, generate offsprings (Child Pool) by performing Two Point Crossover
Operation.
Step 6: Mutate the offsprings by replacing the duplicate bits of the Child Pool
chromosomes by the unused ones.
Step 7: Perform Elitism operation as:
7.1: Copy the Child Pool to the Parent Pool.
7.2: Find the best fitness’ chromosome of the New Parent Pool.
7.3: Compare the fitnesses of the best fitness’ chromosomes of the Parent Pool and the New
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Fig. 5: Proposed Simulated Annealing Algorithm using GA to solve N Queens Problem
7.3 ALGORITHM N_QUEEN_SOLUTION BY BACKTRACKING
Input: The number of Queens (N).
Output: The Number of Solutions (Placements) of that very number of Queens’ Problem,
according to the rule of the problem.
Fig. 6: Classical Backtracking Algorithm to solve N Queens Problem
7.4 ALGORITHM N_QUEEN_SOLUTION BY BRUTE FORCE SEARCH
Input: The number of Queens (N).
Output: The Number of Solutions (Placements) of that very number of Queens’ Problem,
according to the rule of the problem.
Step 1: Firstly, Initialize 2 Temperatures-Initial Temperature (high) and Final Temperature
(low).
Step 2: Randomly generate the Parent Chromosome of Queens.
Loop (temperature T)
Step 3: Evaluate the Parent Chromosome as:
3.1: Fitness function is the no. of non-attacking queen pairs=nc2 (maximum value).
3.2: Calculate the fitness value of the chromosome as: ((nc2)-the no. of diagonal
conflicts).
Step 4: Mutate the Parent Chromosome by replacing the duplicate bit(s) of it by the unused
one(s), to generate the Child Chromosome.
Step 5: Compute the fitness of the Child Chromosome.
Step 6: Perform Elitism operation as:
6.1: If fitness of the Child is better than or equal to that of the Parent, make it Parent
for the next generation.
6.2: If Child fitness is not better than Parent fitness, calculate the probability of
accepting the Child as- p=e^ ((-delta E)/T) - where- delta E is the change of
energy (Parent fitness-Child fitness). Then, make the Child as the Parent for the
next generation depending upon this probability p.
End Loop (temperature T)
Step 1: Place a queen in the top row, then note the column and diagonals it occupies.
Step 2: Then, place another queen in the next row, such that, it is not in the same column or
diagonal. Keep track of the occupied columns and diagonals and proceed to the next
row to place the next queen.
Step 3: If no position is open in the next row, get back to the previous row and place that
queen to the next available position in its row and the process starts over again, until
finding the correct arrangements for all the queens.
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Fig. 7: Classical Brute Force Search Algorithm to solve N Queens Problem
8. RESULTS
For experiment, some different numbers of queens have been taken as input in the
implementations of the four algorithms. The Fitness and Execution Time obtained by
implementing the Proposed GA and the Proposed SA using GA techniques are given in Table II.
The No. of Solutions and Execution Time obtained by implementing the Classical Backtracking
and Classical Brute Force Search Algorithms are mentioned in Table III. The Graphical
Representations of Fitness, No. of Solutions and Execution Time are also shown in Fig. 6, Fig. 7,
Fig. 8 and Fig. 9 respectively. And, most importantly, the System Specification is given in Table
I.
Table I: - System Specification
Hardware Used 512 GB Hard Disk & 2 GB RAM
Processor Type Core2Duo Processor
CPU Speed 2.93 GHz.
Operating System and Software Used Windows 7 and Turbo C++
Table II: - Comparison of Obtained Fitness and Execution Time between GA and SA
No. of Queens GA Fitness GA Time (Sec) SA Fitness
SA Time (Sec)
10 45 0.219780 40 0.054945
20 190 0.769231 175 0.109890
30 428 1.373626 414 0.164835
40 775 2.087912 753 0.219780
Step 1: At first, place a queen in the top row.
Step 2: Then, place a queen in the next row down.
Step 3: Check, if it is sharing the same column or same diagonal with the first queen. If yes,
then place the queen in the next available position in that row. Otherwise, move on
to the next row to place the next queen.
Step 4: If no position is open in the next row, move back to the previous row and move the
queen over to the next available place in its row and the process starts over again
and it will continue, until having the solution.
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0
5000
10000
15000
20000
25000
10
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150
160
170
180
190
200
F
i
t
n
e
s
s
No. of Queens
GA and SA Fitness
GA Fitness
SA Fitness
Fig. 6:- The Graphical Representation of Comparison of Obtained Fitness between GA and SA
0
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i
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No. of Queens
GA and SA Time
GA Time
SA Time
Fig. 7:- The Graphical Representation of Comparison of Execution Time between GA and SA
Table III: - Comparison of Obtained No. of Solutions and Execution Time between BT and BF
No. of Queens
BT No. of
Solns.
BT Time (Sec)
BF No. of
Solns. BF Time (Sec)
1 1 0.000000 1 0.000000
2 0 0.000000 0 0.000000
3 0 0.000000 0 0.000000
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0
10
20
30
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70
1 2 3 4 5 6 7 8 9 10 11 12
BF Time
BT Time
Fig. 9:- The Graphical Representation of Comparison of Execution Times between BT and BF
9. ANALYSIS AND DISCUSSION
The Proposed Simulated Annealing Algorithm using GA takes less time than the Proposed
Genetic Algorithm, as it works on only one chromosome (solution) at a time and processes it to
get the result. The Proposed Genetic Algorithm (GA) gives better solution than the Proposed
Simulated Annealing (SA) Algorithm by GA, as it works on a population of chromosomes
(solutions) and processes them to get the result. Thus, it explores the search space, which helps to
get the solution close to the global optima. But, it is a very important and noticeable fact that,
Genetic and SA using GA Algorithms do not guarantee global optima, as these are random search
and optimization algorithms. Brute Force Search algorithm for N Queens Problem would
examine all possible arrangements of N Queens on the (N*N) matrix, and, for each arrangement,
check, whether each Queen can attack any other. It is simple to implement. It is typically used,
when the problem size is limited. This method is also used, when the simplicity of
implementation is more important than speed. It should not be confused with Backtracking,
where large sets of solutions can be discarded without being enumerated. So, it will take less time
than Brute Force Search Algorithm to solve N Queens Problem. Also, Brute Force (BF) Search
and Backtracking (BT) Algorithms provide exact result for the Queens [20, 21]. But, these two
algorithms cannot provide solutions in ample time, when N values are high. Therefore, these two
algorithms are not at all efficient and effective to solve N Queens Problem; whereas, Genetic
Algorithm (GA) and Simulated Annealing (SA) Algorithm by GA provide good solutions to
higher N Queen values.
10. CONCLUSION
In this paper, the performance of the Proposed Genetic Algorithm in terms of Fitness is enhanced,
except for some larger values of queens. The Proposed Genetic Algorithm has of score of
improvements over the Proposed Simulated Annealing Algorithm using GA and the other two
classical algorithms; i.e., Backtracking and Brute Force Search. Therefore, it can undoubtedly be
concluded that, the Proposed Genetic Algorithm is very much better than the Proposed Simulated
Annealing Algorithm by GA, w.r.t. Fitness of the Solutions obtained in the Result (8) Section, in
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which our entire concentration was focused upon. But, as per Execution Time is concerned, the
Proposed Simulated Annealing by GA performed better than the Proposed Genetic Algorithm.
Also, there is no confusion to conclude that, the Proposed Genetic Algorithm (GA) and the
Proposed Simulated Annealing (SA) Algorithm using GA are very much better than the Classical
Brute Force Search and Backtracking Algorithms. Finally, one thing we must say that, this paper
is entirely nothing but a review based work.
11. FUTURE WORKS
This paper work can be extended by adding a few algorithms like Dynamic Programming,
Greedy, Hill Climbing, Tabu Search, Ant Colony Optimization, Swarm Optimization etc. to solve
N Queens Problem and make a comparative study of these algorithms and thus, making the whole
task more efficient and effective. Also, the Proposed Genetic Algorithm can be well modified, so
that, it can provide solution to higher values of N. Besides these, in the Proposed Genetic
Algorithm, some other selection/crossover/mutation methods can also be applied and comparative
analysis can be made among those methods to check, which method is better and this also will be
an efficient and effective work. Finally, although several modifications will have to be made, yet
this approach can be tried to be applied to solve 3D Queens Problem also.
ACKNOWLEDGEMENT
Firstly, we, the authors of this paper, offer devotion to the lotus feet of God for giving us courage
and blessings for doing this work successfully. Also, we thank our Parents for giving us support.
We are very much grateful to our Colleges and Departmental Faculties for giving us opportunity,
enthusiasm, working environment and facilities to complete this work in time. We are also very
much thankful to the Authors of various Research Papers, which provided us good enough
concepts and confidence for performing this work. We also believe that, the different study
materials of the internet provided us good enough knowledge and concept about this topic and
thus, helped us in performing this work. Without these helps and supports, we couldn’t have
completed this paper successfully at all.
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