This document proposes using an artificial bee colony algorithm to improve a genetic algorithm. It does this by generating the initial population for the genetic algorithm rather than using random generation. The proposed method is tested on random number generation and the travelling salesman problem. For random number generation, five statistical tests are used to evaluate fitness, with the goal of generating random numbers that pass all tests. For the travelling salesman problem, fitness is based on minimizing the total distance travelled. The results show the proposed method performs better than the traditional genetic algorithm in terms of mean iterations, execution time, error rate, and finding the shortest route.
The document proposes a hybrid algorithm combining genetic algorithm and cuckoo search optimization to solve job shop scheduling problems. It aims to minimize makespan (completion time of all jobs) by scheduling jobs on machines. The genetic algorithm is used to explore the search space but can get trapped in local optima. Cuckoo search optimization performs local search faster than genetic algorithm and helps avoid local optima. Experimental results on benchmark problems show the hybrid algorithm yields better solutions in terms of makespan and runtime compared to genetic algorithm and ant colony optimization algorithms.
The document discusses using genetic algorithms for financial forecasting. It begins with an abstract that notes genetic algorithms have been used extensively in various domains including finance to generate profitable trading rules. The document then provides background on genetic algorithms and their basic functions like selection, crossover and mutation. It explains how genetic algorithms can be used to develop a model for financial forecasting by evaluating trading rules based on historical data to determine which rules would have yielded the highest returns.
A Genetic Algorithm Based Approach for Solving Optimal Power Flow ProblemShubhashis Shil
This document describes a study that uses a genetic algorithm to solve the optimal power flow problem. The optimal power flow problem aims to minimize operating costs in a power system by optimizing generator outputs while meeting demand and constraints. The study develops a genetic algorithm approach and compares its results and computation time to traditional derivative-based methods on some example power flow cases. It finds that the genetic algorithm approach produces nearly equivalent results to traditional methods, but requires significantly less computation time to solve the optimal power flow problem, especially as more constraints are added.
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 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
An efficient and powerful advanced algorithm for solving real coded numerica...IOSR Journals
This document discusses an artificial bee colony algorithm with crossover operators for solving numerical optimization problems. The algorithm is based on the intelligent behavior of honey bee swarms. It introduces crossover operations between individual food source positions to generate new offspring. The offspring replace parents if they have better fitness. The algorithm is tested on standard benchmark functions like Griewank and Rosenbrock and results compared to an X-ABC algorithm. Results show the ABC with crossover performs better with fewer parameters.
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.
Particle Swarm Optimization based K-Prototype Clustering Algorithm iosrjce
This document summarizes a research paper that proposes a new Particle Swarm Optimization (PSO) based K-Prototype clustering algorithm to cluster mixed numeric and categorical data. It begins with background information on clustering algorithms like K-Means, K-Modes, and K-Prototype. It then describes the K-Prototype algorithm, PSO, and discrete binary PSO. Related work integrating PSO with other clustering algorithms is also reviewed. The proposed approach uses binary PSO to select improved initial prototypes for K-Prototype clustering in order to obtain better clustering results than traditional K-Prototype and avoid local optima.
The document proposes a hybrid algorithm combining genetic algorithm and cuckoo search optimization to solve job shop scheduling problems. It aims to minimize makespan (completion time of all jobs) by scheduling jobs on machines. The genetic algorithm is used to explore the search space but can get trapped in local optima. Cuckoo search optimization performs local search faster than genetic algorithm and helps avoid local optima. Experimental results on benchmark problems show the hybrid algorithm yields better solutions in terms of makespan and runtime compared to genetic algorithm and ant colony optimization algorithms.
The document discusses using genetic algorithms for financial forecasting. It begins with an abstract that notes genetic algorithms have been used extensively in various domains including finance to generate profitable trading rules. The document then provides background on genetic algorithms and their basic functions like selection, crossover and mutation. It explains how genetic algorithms can be used to develop a model for financial forecasting by evaluating trading rules based on historical data to determine which rules would have yielded the highest returns.
A Genetic Algorithm Based Approach for Solving Optimal Power Flow ProblemShubhashis Shil
This document describes a study that uses a genetic algorithm to solve the optimal power flow problem. The optimal power flow problem aims to minimize operating costs in a power system by optimizing generator outputs while meeting demand and constraints. The study develops a genetic algorithm approach and compares its results and computation time to traditional derivative-based methods on some example power flow cases. It finds that the genetic algorithm approach produces nearly equivalent results to traditional methods, but requires significantly less computation time to solve the optimal power flow problem, especially as more constraints are added.
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 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
An efficient and powerful advanced algorithm for solving real coded numerica...IOSR Journals
This document discusses an artificial bee colony algorithm with crossover operators for solving numerical optimization problems. The algorithm is based on the intelligent behavior of honey bee swarms. It introduces crossover operations between individual food source positions to generate new offspring. The offspring replace parents if they have better fitness. The algorithm is tested on standard benchmark functions like Griewank and Rosenbrock and results compared to an X-ABC algorithm. Results show the ABC with crossover performs better with fewer parameters.
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.
Particle Swarm Optimization based K-Prototype Clustering Algorithm iosrjce
This document summarizes a research paper that proposes a new Particle Swarm Optimization (PSO) based K-Prototype clustering algorithm to cluster mixed numeric and categorical data. It begins with background information on clustering algorithms like K-Means, K-Modes, and K-Prototype. It then describes the K-Prototype algorithm, PSO, and discrete binary PSO. Related work integrating PSO with other clustering algorithms is also reviewed. The proposed approach uses binary PSO to select improved initial prototypes for K-Prototype clustering in order to obtain better clustering results than traditional K-Prototype and avoid local optima.
This document summarizes a study that uses a genetic algorithm to optimize imputing missing cost data for fans used in road tunnels in Sweden. The genetic algorithm is used to impute the missing cost data by optimizing the valid data period used. The results show highly correlated data (R-squared of 0.99) after imputing the missing values, indicating the genetic algorithm provides an effective way to optimize imputing and create complete data that can then be used for forecasting and life cycle cost analysis. The document also reviews other methods for data imputation and discusses experimental results comparing the proposed two-stage approach using K-means clustering and multilayer perceptron on several datasets.
Hybrid Methods of Some Evolutionary Computations AndKalman Filter on Option P...IJMERJOURNAL
ABSTRACT: The search for a better option price continues within the financial institution. In pricing a put option, holders of the underlying stock always want to make the best decision by maximizing profit. We present an optimal hybrid model among the following combinations: Kalman Filter-Genetic Programming(KF-GP), Kalman Filter-Evolutionary Strategy(KF-ES) and Evolutionary Strategy -Genetic Programming(ES- GP). Our results indicate that the hybrid method involving Kalman Filter-Evolutionary Strategy(KF-ES) is the best model for any investor. Sensitivity analysis was conducted on the model parameters to ascertain the rigidity of the model.
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.
Biology-Derived Algorithms in Engineering OptimizationXin-She Yang
The document discusses biology-derived algorithms and their applications in engineering optimization. It describes several biology-inspired algorithms including genetic algorithms, photosynthetic algorithms, neural networks, and cellular automata. Genetic algorithms and photosynthetic algorithms are discussed in more detail. The document also provides examples of how these algorithms can be applied to problems in engineering optimization, such as parameter estimation and inverse analysis.
A Non-Revisiting Genetic Algorithm for Optimizing Numeric Multi-Dimensional F...ijcsa
The document describes a non-revisiting genetic algorithm with adaptive mutation for optimizing multi-dimensional numeric functions. The proposed algorithm avoids revisiting previously evaluated solutions by replacing any duplicates with a mutated version of either the best or random individual. The mutation rate adapts over generations, starting with more exploration and ending with more exploitation. Experimental results on 9 benchmark functions in 2 and 4 dimensions show the proposed algorithm achieves better best and average fitness than a standard genetic algorithm, with improvements ranging from 10-98% depending on the function.
A COMBINATION OF PALMER ALGORITHM AND GUPTA ALGORITHM FOR SCHEDULING PROBLEM ...ijfls
The apparel industry is a class of textile industry. Generally, the production scheduling problem in the apparel industry belongs to Flow Shop Scheduling Problems (FSSP). There are many algorithms/techniques/heuristics for solving FSSP. Two of them are the Palmer Algorithm and the Gupta Algorithm. Hyper-heuristic is a class of heuristics that enables to combine of some heuristics to produce a new heuristic. GPHH is a hyper-heuristic that is based on genetic programming that is proposed to solve FSSP [1]. This paper presents the development of a computer program that implements the GPHH. Some experiments have been conducted for measuring the performance of GPHH. From the experimental results, GPHH has shown a better performance than the Palmer Algorithm and Gupta Algorithm.
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.
Resolving Multi Objective Stock Portfolio Optimization Problem Using Genetic ...Hok Lie
This document summarizes a research paper that proposes using a genetic algorithm to solve a multi-objective stock portfolio optimization problem. It aims to generate a portfolio with the highest expected return and lowest risk. The document first discusses modern portfolio theory and defines the optimization problem. It then describes using a genetic algorithm with real number encoding to evolve portfolio weight solutions. The algorithm is verified using historical stock data, where expected returns and risk are estimated and a fitness function is developed to maximize return and minimize risk. The results show the genetic algorithm converges to better solutions than random search.
The document proposes a particle swarm inspired cuckoo search algorithm to improve the balance of exploration and exploitation in the standard cuckoo search algorithm. It adds neighborhood information from the global best solutions to increase diversity and uses two new search strategies with a random probability rule to balance exploration and exploitation. The algorithm is tested on 30 benchmark functions and two real-world problems, and shows better performance than the standard cuckoo search, particle swarm optimization, and other state-of-the-art algorithms.
The document analyzes crop yield data from spatial locations in Guntur District, Andhra Pradesh, India using hybrid data mining techniques. It first applies k-means clustering to the dataset, producing 5 clusters. It then applies the J48 classification algorithm to the clustered data, resulting in a decision tree that predicts cluster membership based on attributes like crop type, irrigated area, and latitude. Analysis found irrigated areas of cotton and chilies increased from 2007-2008 to 2011-2012. Association rule mining on the clustered data also found relationships between productivity and location attributes. The hybrid approach of clustering followed by classification effectively analyzed the spatial agricultural data.
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.
This document summarizes a research article from the International Journal of Electronics and Communication Engineering & Technology. The article compares the performance of three genetic algorithm crossover operators - PMX, OX, and CX - for solving the Traveling Salesman Problem (TSP). It finds that the PMX operator enables achieving a better solution than the other two operators tested. The document provides background on genetic algorithms and describes the TSP optimization problem, literature on using genetic algorithms for TSP, and proposes a new PMX crossover scheme to resolve TSP more efficiently.
PERFORMANCE ANALYSIS OF HYBRID FORECASTING MODEL IN STOCK MARKET FORECASTINGIJMIT JOURNAL
This document describes a study that analyzed the performance of a hybrid forecasting model for stock markets. The hybrid model uses measures of concordance like Kendall's Tau to identify patterns in past stock market data that resemble present patterns. Genetic programming is then used to match past trends to present trends and estimate future trends. The model was tested on S&P 500 and NASDAQ index data and found to more accurately forecast prices and outperform an ARIMA model based on lower error metrics like MAPE and RMSE. The hybrid model also achieved better results than another previously proposed model when applied to Apple, IBM, and Dell stock data.
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.
This document describes a genetic algorithm approach for automatically generating fuzzy rules for classification problems. The genetic algorithm uses rule importance as the fitness criteria, calculated as the rule's support for each class. The algorithm encodes rules using fuzzy membership set numbers for antecedents and consequents. It iterates for a set number of generations or until a minimum number of rules are fired, selecting high-fitness rules to generate offspring via crossover. The offspring replace lower-fitness rules, and the new rule population is evaluated in the next generation. The approach aims to consistently generate optimal fuzzy rules for classification using genetic search.
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.
An Efficient Genetic Algorithm for Solving Knapsack Problem.pdfNancy Ideker
The document describes using a genetic algorithm to solve the knapsack problem. It proposes a new fitness function that reduces the number of iterations needed to find the optimal solution, improving computation time by 77%. It outlines the genetic algorithm process, including coding scheme representation, fitness function, selection, crossover and mutation operators. For the sample knapsack problem of 14 items, the enhanced fitness function finds the optimal solution in an average of 29 iterations compared to 126 iterations using the traditional fitness function.
A hybrid optimization algorithm based on genetic algorithm and ant colony opt...ijaia
In optimization problem, Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) have
been known as good alternative techniques. GA is designed by adopting the natural evolution process,
while ACO is inspired by the foraging behaviour of ant species. This paper presents a hybrid GA-ACO for
Travelling Salesman Problem (TSP), called Genetic Ant Colony Optimization (GACO). In this method, GA
will observe and preserve the fittest ant in each cycle in every generation and only unvisited cities will be
assessed by ACO. From experimental result, GACO performance is significantly improved and its time
complexity is fairly equal compared to the GA and ACO.
FPGA Optimized Fuzzy Controller Design for Magnetic Ball Levitation using Gen...IDES Editor
This paper presents an optimum approach for
designing of fuzzy controller for nonlinear system using
FPGA technology with Genetic Algorithms (GA) optimization
tool. A magnetic levitation system is considered as a case study
and the fuzzy controller is designed to keep a magnetic object
suspended in the air counteracting the weight of the object.
Fuzzy controller will be implemented using FPGA chip.
Genetic Algorithm (GA) is used in this paper as optimization
method that optimizes the membership, output gain and inputs
gains of the fuzzy controllers. The design will use a highlevel
programming language HDL for implementing the fuzzy
logic controller using the Xfuzzy tools to implement the fuzzy
logic controller into HDL code. This paper, advocates a novel
approach to implement the fuzzy logic controller for magnetic
ball levitation system by using FPGA with GA.
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
AUTOMATED TEST CASE GENERATION AND OPTIMIZATION: A COMPARATIVE REVIEWijcsit
Software testing is the primary phase, which is performed during software development and it is carried by a sequence of instructions of test inputs followed by expected output. Evolutionary algorithms are most popular in the computational field based on population. The test case generation process is used to identify
test cases with resources and also identifies critical domain requirements. The behavior of bees is based on
population and evolutionary method. Bee Colony algorithm (BCA) has gained superiority in comparison to other algorithms in the field of computation. The Harmony Search (HS) algorithm is based on the enhancement process of music. When musicians compose the harmony through different possible combinations of the music, at that time the pitches are stored in the harmony memory and the optimization
can be done by adjusting the input pitches and generate the perfect harmony. Particle Swarm Optimization (PSO) is an intelligence based meta-heuristic algorithm where each particle can locate their source of food at different position.. In this algorithm, the particles will search for a better food source position in the hope of getting a better result. In this paper, the role of Artificial Bee Colony, particle swarm optimization
and harmony search algorithms are analyzed in generating random test data and optimized those test data.
Test case generation and optimization through bee colony, PSO and harmony search (HS) algorithms which are applied through a case study, i.e., withdrawal operation in Bank ATM and it is observed that these algorithms are able to generate suitable automated test cases or test data in a client manner. This
section further gives the brief details and compares between HS, PSO, and Bee Colony (BC) Optimization
methods which are used for test case or test data generation and optimization.
This document summarizes a study that uses a genetic algorithm to optimize imputing missing cost data for fans used in road tunnels in Sweden. The genetic algorithm is used to impute the missing cost data by optimizing the valid data period used. The results show highly correlated data (R-squared of 0.99) after imputing the missing values, indicating the genetic algorithm provides an effective way to optimize imputing and create complete data that can then be used for forecasting and life cycle cost analysis. The document also reviews other methods for data imputation and discusses experimental results comparing the proposed two-stage approach using K-means clustering and multilayer perceptron on several datasets.
Hybrid Methods of Some Evolutionary Computations AndKalman Filter on Option P...IJMERJOURNAL
ABSTRACT: The search for a better option price continues within the financial institution. In pricing a put option, holders of the underlying stock always want to make the best decision by maximizing profit. We present an optimal hybrid model among the following combinations: Kalman Filter-Genetic Programming(KF-GP), Kalman Filter-Evolutionary Strategy(KF-ES) and Evolutionary Strategy -Genetic Programming(ES- GP). Our results indicate that the hybrid method involving Kalman Filter-Evolutionary Strategy(KF-ES) is the best model for any investor. Sensitivity analysis was conducted on the model parameters to ascertain the rigidity of the model.
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.
Biology-Derived Algorithms in Engineering OptimizationXin-She Yang
The document discusses biology-derived algorithms and their applications in engineering optimization. It describes several biology-inspired algorithms including genetic algorithms, photosynthetic algorithms, neural networks, and cellular automata. Genetic algorithms and photosynthetic algorithms are discussed in more detail. The document also provides examples of how these algorithms can be applied to problems in engineering optimization, such as parameter estimation and inverse analysis.
A Non-Revisiting Genetic Algorithm for Optimizing Numeric Multi-Dimensional F...ijcsa
The document describes a non-revisiting genetic algorithm with adaptive mutation for optimizing multi-dimensional numeric functions. The proposed algorithm avoids revisiting previously evaluated solutions by replacing any duplicates with a mutated version of either the best or random individual. The mutation rate adapts over generations, starting with more exploration and ending with more exploitation. Experimental results on 9 benchmark functions in 2 and 4 dimensions show the proposed algorithm achieves better best and average fitness than a standard genetic algorithm, with improvements ranging from 10-98% depending on the function.
A COMBINATION OF PALMER ALGORITHM AND GUPTA ALGORITHM FOR SCHEDULING PROBLEM ...ijfls
The apparel industry is a class of textile industry. Generally, the production scheduling problem in the apparel industry belongs to Flow Shop Scheduling Problems (FSSP). There are many algorithms/techniques/heuristics for solving FSSP. Two of them are the Palmer Algorithm and the Gupta Algorithm. Hyper-heuristic is a class of heuristics that enables to combine of some heuristics to produce a new heuristic. GPHH is a hyper-heuristic that is based on genetic programming that is proposed to solve FSSP [1]. This paper presents the development of a computer program that implements the GPHH. Some experiments have been conducted for measuring the performance of GPHH. From the experimental results, GPHH has shown a better performance than the Palmer Algorithm and Gupta Algorithm.
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.
Resolving Multi Objective Stock Portfolio Optimization Problem Using Genetic ...Hok Lie
This document summarizes a research paper that proposes using a genetic algorithm to solve a multi-objective stock portfolio optimization problem. It aims to generate a portfolio with the highest expected return and lowest risk. The document first discusses modern portfolio theory and defines the optimization problem. It then describes using a genetic algorithm with real number encoding to evolve portfolio weight solutions. The algorithm is verified using historical stock data, where expected returns and risk are estimated and a fitness function is developed to maximize return and minimize risk. The results show the genetic algorithm converges to better solutions than random search.
The document proposes a particle swarm inspired cuckoo search algorithm to improve the balance of exploration and exploitation in the standard cuckoo search algorithm. It adds neighborhood information from the global best solutions to increase diversity and uses two new search strategies with a random probability rule to balance exploration and exploitation. The algorithm is tested on 30 benchmark functions and two real-world problems, and shows better performance than the standard cuckoo search, particle swarm optimization, and other state-of-the-art algorithms.
The document analyzes crop yield data from spatial locations in Guntur District, Andhra Pradesh, India using hybrid data mining techniques. It first applies k-means clustering to the dataset, producing 5 clusters. It then applies the J48 classification algorithm to the clustered data, resulting in a decision tree that predicts cluster membership based on attributes like crop type, irrigated area, and latitude. Analysis found irrigated areas of cotton and chilies increased from 2007-2008 to 2011-2012. Association rule mining on the clustered data also found relationships between productivity and location attributes. The hybrid approach of clustering followed by classification effectively analyzed the spatial agricultural data.
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.
This document summarizes a research article from the International Journal of Electronics and Communication Engineering & Technology. The article compares the performance of three genetic algorithm crossover operators - PMX, OX, and CX - for solving the Traveling Salesman Problem (TSP). It finds that the PMX operator enables achieving a better solution than the other two operators tested. The document provides background on genetic algorithms and describes the TSP optimization problem, literature on using genetic algorithms for TSP, and proposes a new PMX crossover scheme to resolve TSP more efficiently.
PERFORMANCE ANALYSIS OF HYBRID FORECASTING MODEL IN STOCK MARKET FORECASTINGIJMIT JOURNAL
This document describes a study that analyzed the performance of a hybrid forecasting model for stock markets. The hybrid model uses measures of concordance like Kendall's Tau to identify patterns in past stock market data that resemble present patterns. Genetic programming is then used to match past trends to present trends and estimate future trends. The model was tested on S&P 500 and NASDAQ index data and found to more accurately forecast prices and outperform an ARIMA model based on lower error metrics like MAPE and RMSE. The hybrid model also achieved better results than another previously proposed model when applied to Apple, IBM, and Dell stock data.
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.
This document describes a genetic algorithm approach for automatically generating fuzzy rules for classification problems. The genetic algorithm uses rule importance as the fitness criteria, calculated as the rule's support for each class. The algorithm encodes rules using fuzzy membership set numbers for antecedents and consequents. It iterates for a set number of generations or until a minimum number of rules are fired, selecting high-fitness rules to generate offspring via crossover. The offspring replace lower-fitness rules, and the new rule population is evaluated in the next generation. The approach aims to consistently generate optimal fuzzy rules for classification using genetic search.
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.
An Efficient Genetic Algorithm for Solving Knapsack Problem.pdfNancy Ideker
The document describes using a genetic algorithm to solve the knapsack problem. It proposes a new fitness function that reduces the number of iterations needed to find the optimal solution, improving computation time by 77%. It outlines the genetic algorithm process, including coding scheme representation, fitness function, selection, crossover and mutation operators. For the sample knapsack problem of 14 items, the enhanced fitness function finds the optimal solution in an average of 29 iterations compared to 126 iterations using the traditional fitness function.
A hybrid optimization algorithm based on genetic algorithm and ant colony opt...ijaia
In optimization problem, Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) have
been known as good alternative techniques. GA is designed by adopting the natural evolution process,
while ACO is inspired by the foraging behaviour of ant species. This paper presents a hybrid GA-ACO for
Travelling Salesman Problem (TSP), called Genetic Ant Colony Optimization (GACO). In this method, GA
will observe and preserve the fittest ant in each cycle in every generation and only unvisited cities will be
assessed by ACO. From experimental result, GACO performance is significantly improved and its time
complexity is fairly equal compared to the GA and ACO.
FPGA Optimized Fuzzy Controller Design for Magnetic Ball Levitation using Gen...IDES Editor
This paper presents an optimum approach for
designing of fuzzy controller for nonlinear system using
FPGA technology with Genetic Algorithms (GA) optimization
tool. A magnetic levitation system is considered as a case study
and the fuzzy controller is designed to keep a magnetic object
suspended in the air counteracting the weight of the object.
Fuzzy controller will be implemented using FPGA chip.
Genetic Algorithm (GA) is used in this paper as optimization
method that optimizes the membership, output gain and inputs
gains of the fuzzy controllers. The design will use a highlevel
programming language HDL for implementing the fuzzy
logic controller using the Xfuzzy tools to implement the fuzzy
logic controller into HDL code. This paper, advocates a novel
approach to implement the fuzzy logic controller for magnetic
ball levitation system by using FPGA with GA.
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
AUTOMATED TEST CASE GENERATION AND OPTIMIZATION: A COMPARATIVE REVIEWijcsit
Software testing is the primary phase, which is performed during software development and it is carried by a sequence of instructions of test inputs followed by expected output. Evolutionary algorithms are most popular in the computational field based on population. The test case generation process is used to identify
test cases with resources and also identifies critical domain requirements. The behavior of bees is based on
population and evolutionary method. Bee Colony algorithm (BCA) has gained superiority in comparison to other algorithms in the field of computation. The Harmony Search (HS) algorithm is based on the enhancement process of music. When musicians compose the harmony through different possible combinations of the music, at that time the pitches are stored in the harmony memory and the optimization
can be done by adjusting the input pitches and generate the perfect harmony. Particle Swarm Optimization (PSO) is an intelligence based meta-heuristic algorithm where each particle can locate their source of food at different position.. In this algorithm, the particles will search for a better food source position in the hope of getting a better result. In this paper, the role of Artificial Bee Colony, particle swarm optimization
and harmony search algorithms are analyzed in generating random test data and optimized those test data.
Test case generation and optimization through bee colony, PSO and harmony search (HS) algorithms which are applied through a case study, i.e., withdrawal operation in Bank ATM and it is observed that these algorithms are able to generate suitable automated test cases or test data in a client manner. This
section further gives the brief details and compares between HS, PSO, and Bee Colony (BC) Optimization
methods which are used for test case or test data generation and optimization.
The Evaluated Measurement of a Combined Genetic Algorithm and Artificial Immu...IJECEIAES
This paper demonstrates a hybrid between two optimization methods which are the Artificial Immune System (AIS) and Genetic Algorithm (GA). The novel algorithm called the immune genetic algorithm (IGA), provides improvement to the results that enable GA and AIS to work separately which is the main objective of this hybrid. Negative selection which is one of the techniques in the AIS, was employed to determine the input variables (populations) of the system. In order to illustrate the effectiveness of the IGA, the comparison with a steady-state GA, AIS, and PSO were also investigated. The testing of the performance was conducted by mathematical testing, problems were divided into single and multiple objectives. The five single objectives were then used to test the modified algorithm, the results showed that IGA performed better than all of the other methods. The DTLZ multi-objective testing functions were then used. The result also illustrated that the modified approach still had the best performance.
Single parent mating in genetic algorithm for real robotic system identificationIAESIJAI
System identification (SI) is a method of determining a mathematical model
for a system given a set of input-output data. A representation is made using
a mathematical model based on certain specified assumptions. In SI, model
structure selection is a step where a model structure perceived as an adequate
system representation is selected. A typical rule is that the final model must
have a good balance between parsimony and accuracy. As a popular search
method, genetic algorithm (GA) is used for selecting a model structure.
However, the optimality of the final model depends much on the
effectiveness of GA operators. This paper presents a mating technique
named single parent mating (SPM) in GA for use in a real robotic SI. This
technique is based on the chromosome structure of the parents such that a
single parent is sufficient in achieving mating that eases the search for the
optimal model. The results show that using three different objective
functions (Akaike information criterion, Bayesian information criterion and
parameter magnitude–based information criterion 2) respectively, GA with
the mating technique is able to find more optimal models than without the
mating technique. Validations show that the selected models using the
mating technique are acceptable.
Improving the effectiveness of information retrieval system using adaptive ge...ijcsit
The document describes research into improving the effectiveness of information retrieval systems using an adaptive genetic algorithm. A genetic algorithm with variable crossover and mutation probabilities (adaptive GA) is investigated. The adaptive GA is tested on 242 Arabic abstracts using three information retrieval models: vector space model, extended Boolean model, and language model. Results show the adaptive GA approach improves retrieval effectiveness over traditional genetic algorithms and baseline information retrieval systems, as measured by average recall and precision. Key aspects of the adaptive GA used include variable crossover and mutation probabilities tuned during the search process, and fitness functions based on document retrieval order.
The document presents a particle swarm inspired cuckoo search algorithm for real parameter optimization. It combines two algorithms: cuckoo search and particle swarm optimization. In cuckoo search, agents find new solutions using levy flights. The proposed algorithm adds the global best solution from particle swarm optimization to enhance exploitation. It balances exploration and exploitation through two new search strategies with random probabilities. The algorithm is tested on benchmark functions and two real-world problems, showing better performance than other algorithms.
AUTOMATIC GENERATION AND OPTIMIZATION OF TEST DATA USING HARMONY SEARCH ALGOR...cscpconf
Software testing is the primary phase, which is performed during software development and it is
carried by a sequence of instructions of test inputs followed by expected output. The Harmony
Search (HS) algorithm is based on the improvisation process of music. In comparison to other
algorithms, the HSA has gain popularity and superiority in the field of evolutionary
computation. When musicians compose the harmony through different possible combinations of
the music, at that time the pitches are stored in the harmony memory and the optimization can
be done by adjusting the input pitches and generate the perfect harmony. The test case
generation process is used to identify test cases with resources and also identifies critical
domain requirements. In this paper, the role of Harmony search meta-heuristic search
technique is analyzed in generating random test data and optimized those test data. Test data
are generated and optimized by applying in a case study i.e. a withdrawal task in Bank ATM
through Harmony search. It is observed that this algorithm generates suitable test cases as well
as test data and gives brief details about the Harmony search method. It is used for test data
generation and optimization.
AUTOMATIC GENERATION AND OPTIMIZATION OF TEST DATA USING HARMONY SEARCH ALGOR...csandit
Software testing is the primary phase, which is performed during software development and it is
carried by a sequence of instructions of test inputs followed by expected output. The Harmony
Search (HS) algorithm is based on the improvisation process of music. In comparison to other
algorithms, the HSA has gain popularity and superiority in the field of evolutionary
computation. When musicians compose the harmony through different possible combinations of
the music, at that time the pitches are stored in the harmony memory and the optimization can
be done by adjusting the input pitches and generate the perfect harmony. The test case
generation process is used to identify test cases with resources and also identifies critical
domain requirements. In this paper, the role of Harmony search meta-heuristic search
technique is analyzed in generating random test data and optimized those test data. Test data
are generated and optimized by applying in a case study i.e. a withdrawal task in Bank ATM
through Harmony search. It is observed that this algorithm generates suitable test cases as well
as test data and gives brief details about the Harmony search method. It is used for test data
generation and optimization
This document discusses using genetic algorithms to solve a multi-objective traveling salesman problem (MOTSP) that considers both cost and CO2 emissions. It provides background on genetic algorithms and the traveling salesman problem. The study aims to identify how tuning genetic operators and parameters can improve the efficiency of genetic algorithms in solving the MOTSP with CO2 emissions. Empirical results show that performance improves with some combinations of parameters and operators.
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.
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.
Multiple Criteria Decision Making for Hospital Location Allocation Based on I...IRJET Journal
This document summarizes a research paper that uses an improved genetic algorithm (IGA) to determine optimal locations for new hospital centers. The IGA is compared to standard genetic algorithms and particle swarm optimization algorithms. GIS and AHP analyses are used to identify 675 potential sites from the study area of Tehran District 2. The IGA then selects the best combination of 6 sites out of the potential sites to maximize coverage based on distance minimization. The IGA incorporates activity and impact rates to select parents for crossover in a way that improves algorithm performance and convergence compared to standard genetic algorithms. The results suggest the IGA performs better than the other algorithms in terms of convergence speed, safety, reproducibility, and runtime.
The document discusses adapting a genetic algorithm to schedule variants of manufacturing shop models. It presents two examples - a flow shop and a shop with alternative machine routing - to demonstrate how genetic algorithms can be applied to schedule jobs for more complex shop models. The genetic algorithm is implemented using standard spreadsheet software, showing how simple it is to apply genetic algorithms to production scheduling problems.
Comparison of Cell formation techniques in Cellular manufacturing using three...IJERA Editor
In the present era of globalization and competitive market, cellular manufacturing has become a vital tool for
meeting the challenges of improving productivity, which is the way to sustain growth. Getting best results of
cellular manufacturing depends on the formation of the machine cells and part families. This paper examines
advantages of ART method of cell formation over array based clustering algorithms, namely ROC-2 and DCA.
The comparison and evaluation of the cell formation methods has been carried out in the study. The most
appropriate approach is selected and used to form the cellular manufacturing system. The comparison and
evaluation is done on the basis of performance measure as grouping efficiency and improvements over the
existing cellular manufacturing system is presented.
Comparison of Cell formation techniques in Cellular manufacturing using three...IJERA Editor
This document compares three cell formation techniques for cellular manufacturing: Rank Order Clustering 2 (ROC-2), Direct Clustering Analysis (DCA), and Adaptive Resonance Theory (ART). It evaluates the performance of each using grouping efficiency and the number of exceptional elements. The key findings are that ART outperforms the other two techniques, providing faster computation and the ability to handle large industrial problems. ART is an artificial neural network approach that can dynamically adapt machine-part cells. The document concludes ART is an effective method for machine-part cell formation in cellular manufacturing.
Performance Analysis of Genetic Algorithm as a Stochastic Optimization Tool i...paperpublications3
Abstract: Engineering design problems are complex by nature because of their critical objective functions involving many variables and Constraints. Engineers have to ensure the compatibility with the imposed specifications keeping the manufacturing costs low. Moreover, the methodology may vary according to the design problem.
The main issue is to choose the proper tool for optimization. In the earlier days, a design problem was optimized by some of the conventional optimization techniques like gradient Search, evolutionary optimization, random search etc. These are known as classical methods.
The method is to be properly Chosen depending on the nature of the problem- an incorrect choice may sometimes fail to give the optimal solution. So the methods are less robust.
Now-a-days soft-computing techniques are being widely used for optimizing a function. These are more robust. Genetic algorithm is one such method. It is an effective tool in the realm of stochastic optimization (non-classical). The algorithm produces many strings and generation to reach the optimal point.
The main objective of the paper is to optimize engineering design problems using Genetic Algorithm and to analyze how the algorithm reaches the optima effectively and closely. We choose a mathematical expression for the objective function in terms of the design variables and optimize the same under given constraints using GA.
Similar to Improvement of genetic algorithm using artificial bee colony (20)
Square transposition: an approach to the transposition process in block cipherjournalBEEI
The transposition process is needed in cryptography to create a diffusion effect on data encryption standard (DES) and advanced encryption standard (AES) algorithms as standard information security algorithms by the National Institute of Standards and Technology. The problem with DES and AES algorithms is that their transposition index values form patterns and do not form random values. This condition will certainly make it easier for a cryptanalyst to look for a relationship between ciphertexts because some processes are predictable. This research designs a transposition algorithm called square transposition. Each process uses square 8 × 8 as a place to insert and retrieve 64-bits. The determination of the pairing of the input scheme and the retrieval scheme that have unequal flow is an important factor in producing a good transposition. The square transposition can generate random and non-pattern indices so that transposition can be done better than DES and AES.
Hyper-parameter optimization of convolutional neural network based on particl...journalBEEI
The document proposes using a particle swarm optimization (PSO) algorithm to optimize the hyperparameters of a convolutional neural network (CNN) for image classification. The PSO algorithm is used to find optimal values for CNN hyperparameters like the number and size of convolutional filters. In experiments on the MNIST handwritten digit dataset, the optimized CNN achieved a testing error rate of 0.87%, which is competitive with state-of-the-art models. The proposed approach finds optimized CNN architectures automatically without requiring manual design or encoding strategies during training.
Supervised machine learning based liver disease prediction approach with LASS...journalBEEI
In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.
A secure and energy saving protocol for wireless sensor networksjournalBEEI
The research domain for wireless sensor networks (WSN) has been extensively conducted due to innovative technologies and research directions that have come up addressing the usability of WSN under various schemes. This domain permits dependable tracking of a diversity of environments for both military and civil applications. The key management mechanism is a primary protocol for keeping the privacy and confidentiality of the data transmitted among different sensor nodes in WSNs. Since node's size is small; they are intrinsically limited by inadequate resources such as battery life-time and memory capacity. The proposed secure and energy saving protocol (SESP) for wireless sensor networks) has a significant impact on the overall network life-time and energy dissipation. To encrypt sent messsages, the SESP uses the public-key cryptography’s concept. It depends on sensor nodes' identities (IDs) to prevent the messages repeated; making security goals- authentication, confidentiality, integrity, availability, and freshness to be achieved. Finally, simulation results show that the proposed approach produced better energy consumption and network life-time compared to LEACH protocol; sensors are dead after 900 rounds in the proposed SESP protocol. While, in the low-energy adaptive clustering hierarchy (LEACH) scheme, the sensors are dead after 750 rounds.
Plant leaf identification system using convolutional neural networkjournalBEEI
This paper proposes a leaf identification system using convolutional neural network (CNN). This proposed system can identify five types of local Malaysia leaf which were acacia, papaya, cherry, mango and rambutan. By using CNN from deep learning, the network is trained from the database that acquired from leaf images captured by mobile phone for image classification. ResNet-50 was the architecture has been used for neural networks image classification and training the network for leaf identification. The recognition of photographs leaves requested several numbers of steps, starting with image pre-processing, feature extraction, plant identification, matching and testing, and finally extracting the results achieved in MATLAB. Testing sets of the system consists of 3 types of images which were white background, and noise added and random background images. Finally, interfaces for the leaf identification system have developed as the end software product using MATLAB app designer. As a result, the accuracy achieved for each training sets on five leaf classes are recorded above 98%, thus recognition process was successfully implemented.
Customized moodle-based learning management system for socially disadvantaged...journalBEEI
This study aims to develop Moodle-based LMS with customized learning content and modified user interface to facilitate pedagogical processes during covid-19 pandemic and investigate how teachers of socially disadvantaged schools perceived usability and technology acceptance. Co-design process was conducted with two activities: 1) need assessment phase using an online survey and interview session with the teachers and 2) the development phase of the LMS. The system was evaluated by 30 teachers from socially disadvantaged schools for relevance to their distance learning activities. We employed computer software usability questionnaire (CSUQ) to measure perceived usability and the technology acceptance model (TAM) with insertion of 3 original variables (i.e., perceived usefulness, perceived ease of use, and intention to use) and 5 external variables (i.e., attitude toward the system, perceived interaction, self-efficacy, user interface design, and course design). The average CSUQ rating exceeded 5.0 of 7 point-scale, indicated that teachers agreed that the information quality, interaction quality, and user interface quality were clear and easy to understand. TAM results concluded that the LMS design was judged to be usable, interactive, and well-developed. Teachers reported an effective user interface that allows effective teaching operations and lead to the system adoption in immediate time.
Understanding the role of individual learner in adaptive and personalized e-l...journalBEEI
Dynamic learning environment has emerged as a powerful platform in a modern e-learning system. The learning situation that constantly changing has forced the learning platform to adapt and personalize its learning resources for students. Evidence suggested that adaptation and personalization of e-learning systems (APLS) can be achieved by utilizing learner modeling, domain modeling, and instructional modeling. In the literature of APLS, questions have been raised about the role of individual characteristics that are relevant for adaptation. With several options, a new problem has been raised where the attributes of students in APLS often overlap and are not related between studies. Therefore, this study proposed a list of learner model attributes in dynamic learning to support adaptation and personalization. The study was conducted by exploring concepts from the literature selected based on the best criteria. Then, we described the results of important concepts in student modeling and provided definitions and examples of data values that researchers have used. Besides, we also discussed the implementation of the selected learner model in providing adaptation in dynamic learning.
Prototype mobile contactless transaction system in traditional markets to sup...journalBEEI
1) Researchers developed a prototype contactless transaction system using QR codes and digital payments to support physical distancing during the COVID-19 pandemic in traditional markets.
2) The system allows sellers and buyers in traditional markets to conduct fast, secure transactions via smartphones without direct cash exchange. Buyers scan sellers' QR codes to view product details and make e-wallet payments.
3) Testing showed the system's functions worked properly and users found it easy to use and useful for supporting contactless transactions and digital transformation of traditional markets. However, further development is needed to increase trust in digital payments for users unfamiliar with the technology.
Wireless HART stack using multiprocessor technique with laxity algorithmjournalBEEI
The use of a real-time operating system is required for the demarcation of industrial wireless sensor network (IWSN) stacks (RTOS). In the industrial world, a vast number of sensors are utilised to gather various types of data. The data gathered by the sensors cannot be prioritised ahead of time. Because all of the information is equally essential. As a result, a protocol stack is employed to guarantee that data is acquired and processed fairly. In IWSN, the protocol stack is implemented using RTOS. The data collected from IWSN sensor nodes is processed using non-preemptive scheduling and the protocol stack, and then sent in parallel to the IWSN's central controller. The real-time operating system (RTOS) is a process that occurs between hardware and software. Packets must be sent at a certain time. It's possible that some packets may collide during transmission. We're going to undertake this project to get around this collision. As a prototype, this project is divided into two parts. The first uses RTOS and the LPC2148 as a master node, while the second serves as a standard data collection node to which sensors are attached. Any controller may be used in the second part, depending on the situation. Wireless HART allows two nodes to communicate with each other.
Implementation of double-layer loaded on octagon microstrip yagi antennajournalBEEI
This document describes the implementation of a double-layer structure on an octagon microstrip yagi antenna (OMYA) to improve its performance at 5.8 GHz. The double-layer consists of two double positive (DPS) substrates placed above the OMYA. Simulation and experimental results show that the double-layer configuration increases the gain of the OMYA by 2.5 dB compared to without the double-layer. The measured bandwidth of the OMYA with double-layer is 14.6%, indicating the double-layer can increase both the gain and bandwidth of the OMYA.
The calculation of the field of an antenna located near the human headjournalBEEI
In this work, a numerical calculation was carried out in one of the universal programs for automatic electro-dynamic design. The calculation is aimed at obtaining numerical values for specific absorbed power (SAR). It is the SAR value that can be used to determine the effect of the antenna of a wireless device on biological objects; the dipole parameters will be selected for GSM1800. Investigation of the influence of distance to a cell phone on radiation shows that absorbed in the head of a person the effect of electromagnetic radiation on the brain decreases by three times this is a very important result the SAR value has decreased by almost three times it is acceptable results.
Exact secure outage probability performance of uplinkdownlink multiple access...journalBEEI
In this paper, we study uplink-downlink non-orthogonal multiple access (NOMA) systems by considering the secure performance at the physical layer. In the considered system model, the base station acts a relay to allow two users at the left side communicate with two users at the right side. By considering imperfect channel state information (CSI), the secure performance need be studied since an eavesdropper wants to overhear signals processed at the downlink. To provide secure performance metric, we derive exact expressions of secrecy outage probability (SOP) and and evaluating the impacts of main parameters on SOP metric. The important finding is that we can achieve the higher secrecy performance at high signal to noise ratio (SNR). Moreover, the numerical results demonstrate that the SOP tends to a constant at high SNR. Finally, our results show that the power allocation factors, target rates are main factors affecting to the secrecy performance of considered uplink-downlink NOMA systems.
Design of a dual-band antenna for energy harvesting applicationjournalBEEI
This report presents an investigation on how to improve the current dual-band antenna to enhance the better result of the antenna parameters for energy harvesting application. Besides that, to develop a new design and validate the antenna frequencies that will operate at 2.4 GHz and 5.4 GHz. At 5.4 GHz, more data can be transmitted compare to 2.4 GHz. However, 2.4 GHz has long distance of radiation, so it can be used when far away from the antenna module compare to 5 GHz that has short distance in radiation. The development of this project includes the scope of designing and testing of antenna using computer simulation technology (CST) 2018 software and vector network analyzer (VNA) equipment. In the process of designing, fundamental parameters of antenna are being measured and validated, in purpose to identify the better antenna performance.
Transforming data-centric eXtensible markup language into relational database...journalBEEI
eXtensible markup language (XML) appeared internationally as the format for data representation over the web. Yet, most organizations are still utilising relational databases as their database solutions. As such, it is crucial to provide seamless integration via effective transformation between these database infrastructures. In this paper, we propose XML-REG to bridge these two technologies based on node-based and path-based approaches. The node-based approach is good to annotate each positional node uniquely, while the path-based approach provides summarised path information to join the nodes. On top of that, a new range labelling is also proposed to annotate nodes uniquely by ensuring the structural relationships are maintained between nodes. If a new node is to be added to the document, re-labelling is not required as the new label will be assigned to the node via the new proposed labelling scheme. Experimental evaluations indicated that the performance of XML-REG exceeded XMap, XRecursive, XAncestor and Mini-XML concerning storing time, query retrieval time and scalability. This research produces a core framework for XML to relational databases (RDB) mapping, which could be adopted in various industries.
Key performance requirement of future next wireless networks (6G)journalBEEI
The document provides an overview of the key performance indicators (KPIs) for 6G wireless networks compared to 5G networks. Some of the major KPIs discussed for 6G include: achieving data rates of up to 1 Tbps and individual user data rates up to 100 Gbps; reducing latency below 10 milliseconds; supporting up to 10 million connected devices per square kilometer; improving spectral efficiency by up to 100 times through technologies like terahertz communications and smart surfaces; and achieving an energy efficiency of 1 pico-joule per bit transmitted through techniques like wireless power transmission and energy harvesting. The document outlines how 6G aims to integrate terrestrial, aerial and maritime communications into a single network to provide ubiquitous connectivity with higher
Noise resistance territorial intensity-based optical flow using inverse confi...journalBEEI
This paper presents the use of the inverse confidential technique on bilateral function with the territorial intensity-based optical flow to prove the effectiveness in noise resistance environment. In general, the image’s motion vector is coded by the technique called optical flow where the sequences of the image are used to determine the motion vector. But, the accuracy rate of the motion vector is reduced when the source of image sequences is interfered by noises. This work proved that the inverse confidential technique on bilateral function can increase the percentage of accuracy in the motion vector determination by the territorial intensity-based optical flow under the noisy environment. We performed the testing with several kinds of non-Gaussian noises at several patterns of standard image sequences by analyzing the result of the motion vector in a form of the error vector magnitude (EVM) and compared it with several noise resistance techniques in territorial intensity-based optical flow method.
Modeling climate phenomenon with software grids analysis and display system i...journalBEEI
This study aims to model climate change based on rainfall, air temperature, pressure, humidity and wind with grADS software and create a global warming module. This research uses 3D model, define, design, and develop. The results of the modeling of the five climate elements consist of the annual average temperature in Indonesia in 2009-2015 which is between 29oC to 30.1oC, the horizontal distribution of the annual average pressure in Indonesia in 2009-2018 is between 800 mBar to 1000 mBar, the horizontal distribution the average annual humidity in Indonesia in 2009 and 2011 ranged between 27-57, in 2012-2015, 2017 and 2018 it ranged between 30-60, during the East Monsoon, the wind circulation moved from northern Indonesia to the southern region Indonesia. During the west monsoon, the wind circulation moves from the southern part of Indonesia to the northern part of Indonesia. The global warming module for SMA/MA produced is feasible to use, this is in accordance with the value given by the validate of 69 which is in the appropriate category and the response of teachers and students through a 91% questionnaire.
An approach of re-organizing input dataset to enhance the quality of emotion ...journalBEEI
The purpose of this paper is to propose an approach of re-organizing input data to recognize emotion based on short signal segments and increase the quality of emotional recognition using physiological signals. MIT's long physiological signal set was divided into two new datasets, with shorter and overlapped segments. Three different classification methods (support vector machine, random forest, and multilayer perceptron) were implemented to identify eight emotional states based on statistical features of each segment in these two datasets. By re-organizing the input dataset, the quality of recognition results was enhanced. The random forest shows the best classification result among three implemented classification methods, with an accuracy of 97.72% for eight emotional states, on the overlapped dataset. This approach shows that, by re-organizing the input dataset, the high accuracy of recognition results can be achieved without the use of EEG and ECG signals.
Parking detection system using background subtraction and HSV color segmentationjournalBEEI
Manual system vehicle parking makes finding vacant parking lots difficult, so it has to check directly to the vacant space. If many people do parking, then the time needed for it is very much or requires many people to handle it. This research develops a real-time parking system to detect parking. The system is designed using the HSV color segmentation method in determining the background image. In addition, the detection process uses the background subtraction method. Applying these two methods requires image preprocessing using several methods such as grayscaling, blurring (low-pass filter). In addition, it is followed by a thresholding and filtering process to get the best image in the detection process. In the process, there is a determination of the ROI to determine the focus area of the object identified as empty parking. The parking detection process produces the best average accuracy of 95.76%. The minimum threshold value of 255 pixels is 0.4. This value is the best value from 33 test data in several criteria, such as the time of capture, composition and color of the vehicle, the shape of the shadow of the object’s environment, and the intensity of light. This parking detection system can be implemented in real-time to determine the position of an empty place.
Quality of service performances of video and voice transmission in universal ...journalBEEI
The universal mobile telecommunications system (UMTS) has distinct benefits in that it supports a wide range of quality of service (QoS) criteria that users require in order to fulfill their requirements. The transmission of video and audio in real-time applications places a high demand on the cellular network, therefore QoS is a major problem in these applications. The ability to provide QoS in the UMTS backbone network necessitates an active QoS mechanism in order to maintain the necessary level of convenience on UMTS networks. For UMTS networks, investigation models for end-to-end QoS, total transmitted and received data, packet loss, and throughput providing techniques are run and assessed and the simulation results are examined. According to the results, appropriate QoS adaption allows for specific voice and video transmission. Finally, by analyzing existing QoS parameters, the QoS performance of 4G/UMTS networks may be improved.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
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Improvement of genetic algorithm using artificial bee colony
1. Bulletin of Electrical Engineering and Informatics
Vol. 9, No. 5, October 2020, pp. 2125~2133
ISSN: 2302-9285, DOI: 10.11591/eei.v9i5.2233 2125
Journal homepage: http://beei.org
Improvement of genetic algorithm using artificial bee colony
Ali Abdul Kadhim Taher, Suhad Malallah Kadhim
Computer Science department, University of Technology, Baghdad, Iraq
Article Info ABSTRACT
Article history:
Received Nov 13, 2019
Revised Jan 4, 2020
Accepted Feb 8, 2020
Genetic algorithm (GA) is a part of evolutionary computing that simulates
the theory of evolution and natural selection, where this technique depends
on a heuristic random search. This algorithm reflects the operation of natural
selection, where the fittest individuals are chosen for reproduction so that
they produce offspring of the next generation. This paper proposes a method
to improve GA using artificial bee colony (GABC). This proposed algorithm
was applied to random number generation (RNG), and travelling salesman
problem (TSP). The proposed method used to generate initial populations for
GA rather than the random generation that used in traditional GA. The results
of testing on RNG show that the proposed GABC was better than traditional
GA in the mean iteration and the execution time. The results of testing TSP
show the superiority of GABC on the traditional GA. The superiority
of the GABC is clear in terms of the percentage of error rate, the average
length route, and obtaining the shortest route. The programming language
Python3 was used in programming the proposed methods.
Keywords:
Artificial bee colony
Genetic algorithm
Random number generation
Travelling salesman problem
This is an open access article under the CC BY-SA license.
Corresponding Author:
Ali Abdul Kadhim Taher,
Computer Science Department,
University of Technology, Baghdad, Iraq.
Email: 0110114@student.uotechnology.edu.iq
1. INTRODUCTION
Artificial intelligence (AI) has been shown great performance in several tasks [1-4]. Genetic
algorithm (GA) is an AI algorithm that is a metaheuristic method invented in 1975 by John Holland [5].
GA has a good global search capability through its operation strategy but has a slow convergence speed [6].
GA shows high performance in complex areas because it works repetitively as the research is likely to focus
on typical representative area areas found to produce good behavior [7]. Artificial bee colony (ABC)
algorithm was proposed by Karaboga in 2005, which is simple in concept and requires very few initialization
parameters to adjust. It has properties of fast convergence speed, good quality of solutions and good
robustness, etc., but it also has the disadvantages of early convergence and ease of falling into the
local optimum [8].
Important problems that test the efficiency of the algorithm are RNG and TSP, which will be
adopted in this thesis as a case study, which will be explained later. Random numbers are numbers generated
by an operation that unpredictable it's an outcome and cannot be sequentially reliably reproduced.
Random numbers are necessary for a variety of applications. For instance, a popular cryptosystem used keys
that should be produced in a random mode [9]. TSP is one of the problems known as NP-hard and is a well-
known problem in the field of computer science and mathematics. This problem has been used in many fields
such as semiconductor manufacturing, logistics, and transportation [10].
The rest of the paper is organized as follows: section 2 presented some related work. In section 3,
background theories were displayed. In section 4, described the design of the proposed methods. In section 5,
experimental results were evaluated, and in the last section, a brief conclusion was presented.
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2. RELATED WORK
There are many pieces of research focusing on improving GA. In Abu-Almash [9] genetic algorithm
used to propose a new approach to generate keys. The Five basic tests applied as a fitness function in this
experiment for each chromosome. The results proved that the competence of a genetic algorithm for key
generation. The optimal representation for the genetic algorithm may be seen by the initial population, size of
population, crossover, and mutation. The large population size, little mutation probability, and a high
crossover probability.
Alkafaween [11] has presented a new mutation called “IRGIBNNM”, that was a hybrid
of two mutations which were a random-based mutation and a knowledge-based mutation. The proposed
mutation was used to improve the select best mutation (SBM) strategy. The proposed mutation performance
was compared with three mutations, additionally to the SBM. The performance of the proposed mutation
and the SBM strategy was evaluated on several experiments on twelve TSP instances were conducted. Those
instances were taken from the TSPLIB. The experiential results showed the efficiency of the "IRGIBNNM"
mutation and the SBM strength where both methods have benefited from the randomization and knowledge
provided by the nearest neighbor approach.
Hassanat et al. [12], studied GA was improved by dividing the large-scale TSP problem into small
subsets based on regression. This was achieved by using the regression line and its perpendicular line, where
cities were grouped into four sub-problems and frequently, each site locates the group in which the city
belongs. This process was repeated until the size of the problem became very small or reached a size that
cannot be divided. Some famous TSP instances that are derived from TSPLIB were used in experiments.
The experiments showed that the proposed method outperformed the other methods using population seeding
techniques such as the nearest neighbor based techniques and random techniques regarding mean
convergence and error rate.
Alkharji et al. [13] describe a method for using GA to generate random keys for a fully
homomorphic encryption scheme (FHE) and check its efficiency. The five statistical tests used in
this experiment. This paper explains that the powerful, high-quality, non-repetitive random keys generated
by GA will increase the security of FEH schemes, and making it difficult for analysts to break the data.
Akter et al. [14] have presented a new crossover operator for solving the TSP. The new crossover consisted
of choosing two crossover points and creating new offspring by comparing the cost. The proposed crossover
was performed with two traditional crossover operators a sequential crossover operator (SCX) and a triple
crossover operator (TCO), and used eight benchmark instances taken from the TSPLIB. The experimental
results among the proposed crossover operator, SCX, and TCO showed that the proposed operator has
outperformed other operators via providing a solution in less iteration that significantly reducing time
and memory.
Gupta et al. [10] have focused on the development of a heuristic technique by combining two
common optimization methods: particle swarm (PSO) and GA for TSP. The main inspiration was used to
improve a hybrid GA-PSO algorithm to take advantage of PSO such as the self-improvement of individuals
and high convergence rate, which compensated the weakness of GA. In the GA-PSO hybrid algorithm, new
individuals were generated through the PSO mechanism as well as crossover and mutation operators.
The performance of the GA-PSO hybrid algorithm against GA and PSO was used ten TSP standards in terms
of finding optimal and execution time. Superior GA-PSO mixed performance between GA and PSO for TSP
with concern to the standard, i.e. error rate and average computational time.
3. PRELIMINARIES
3.1. Genetic algorithm
GA is a non-linear, discrete random process that does not require mathematical formulation. Optima
are evolving from generation to another [15]. It has grown as important in solving large complex problems
that require a long time according to traditional programming techniques, compared to GA, which has a huge
amount of alternative solutions, where the solution is often optimal or near to optimal, over time [16].
At first, GA selects parents from an initial population of chromosomes, and then generates offspring
using crossover and mutation. It evaluates all chromosomes to determine their fitness using a fitness function.
Then, fitness values were used to determine if chromosomes are retained or disposed of. The least adapted
chromosomes are eliminated and the most adapted chromosomes are retained in generating new populations
according to the principle of survival of the fittest. The new population replaces the old. The processes
are repeated while a specific termination condition satisfied [17]. A simple GA is illustrated in Figure 1.
3. Bulletin of Electr Eng & Inf ISSN: 2302-9285
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Figure 1. Flow chart of a typical GA
3.2. Artificial bee colony (ABC)
ABC is a method of improving heuristic population-based. The populations in the algorithm
represent bees. Every bee searches for the best solution (food resource). It is assumed that each solution is
a food resource and the amount of nectar for each resource represents the quality of each solution [18]. ABC
is a population-based improvement algorithm that aims to obtain a global minimum [19].
There are two types of bees in the ABC algorithms that are employed and unemployed bees that
be consisting of the onlooker bees and the scout bees. The population of the colony usually composed two
parts employed that form the first half and the rest include onlooker bees. At first, employed bees exploit
the sources of nectar that have been explored, and then provide information to the onlooker bees in
the beehive about the location of the food sources they exploit.
After completing the search process by the employed bees, they share information about food
sources and their location with the onlooker bees in the dance space. The onlooker bees evaluate information
from employed bees and choose the source of food with the probability of nectar. Scouts randomly examine
the environment to identify a new food source based on potential external evidence or internal motivation.
Algorithm 1 represents the main algorithm of ABC.
Algorithm (1): Formal Model of ABC
INPUT: Initial population
OUTPUT: the Best solution
Begin
Initialize phase
Repeat
Employed Bees Phase1
Onlooker Bees Phase1
Scouts Bees1 Phase1
Memorize the best solutions attained till now
Until (Cycle= Maximum cycle number or a Maximize run time )
End
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3.3. Travelling salesman problem
TSP is considered an optimization problem [20]. It is a problem that is an easy description tricky
solution and classified as a problem that has not been solved in polynomial time. This problem belongs to
NP-hard [21]. The major purpose of this problem is to detect the shortest route through a group of cities
(starting from city N and ending in the same city) so that each city is visited only once [22].
3.4. Random number generators
Random numbers are a wide field with a strong theoretical and empirical basis. There are many
methods used to generate random numbers such as published random number tables and throwing dice.
The expansion of digital computers has opened up a new area of inquiry, such as measurement of physical
parameters, random numbers of real generators, etc [23]. A valid RNG will generate a sequential series
of distributed numbers, at the interval [0, 1]. Using any type of computer algorithm, if we know the initial
state of the algorithm which be also called the seed, the sequence of numbers can be determined [24].
3.5. Statistical tests
This section presents several tests aimed at measuring the quality of a generator called a random bit
generator. The process is done by taking a sampling of the sequence and presenting it to several statistical
tests. Every one of the five tests determined whether a particular attribute sequence is likely to actually
display a random sequence [25]. Let s=s0, s1, s2…sn−1 is a binary sequence of lengths n. The statistical tests
that are used in this paper are [26]:
‒ The frequency (mono-bit) test
For a random series of length n, the number 0's should be approximately equal to the number of 1's.
The statistic test used in (1):
(1)
where: s0, s1 indicates the numbers of 0’s and 1’s in s, respectively.
‒ Serial (two-bit) test
This test determined if the frequency count is 00, 10, 01 and 11 are roughly the same. A statistic test
used in (2):
(2)
where s00, s10, s01 and s11 indicate the number of "00", "01", "10", and "11" in s respectively.
‒ Poker test
Suppose p is a positive integer where [s/p]≥5, (2p
), and suppose D=[s/p]. Divide the sequence s into
non-overlapping segments D whose length is p, and assume that si is the number of sequence-type
appearances of length p, 1≤I≤2p. In this test, we determine whether the length p sequence shows
approximately the same number of times in seconds, as expected for the random sequence. The statistic test
used in (3):
[∑ ] (3)
‒ Run test
The purpose of this test is to determine whether the number is either zeros or numbers from a series
of different lengths in the S sequence as expected in a random sequence. The expected number of gaps
(or blocks) of length i in a random sequence of length s is ⁄ . Suppose L is equals to
the maximum integer i was ei≥5. Suppose Bi and Gi are the numbers of block and gap respectively, of length i
in n for each i, 1≤i ≤L. The statistic test is calculated by (4):
∑ ∑ (4)
‒ Autocorrelation test
In this test, we investigate the relationships between the sequence and its transmitted versions.
Suppose d is a constant number, 1≤d≤s/2. The number of bits in s not equals to their d-shifts
is A (d)=∑ , where indicates the XOR operator. Which follows roughly the distribution
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of N (0, 1) if n-d≥10. Since the small values of A (d) are not as predictable as the large values of A (d),
we should use a test on both sides. The statistic test is calculated by (5):
[ ] √
⁄ (5)
4. THE PROPOSED METHOD
The proposed method has employed an ABC algorithm to improve the population of GA. TSP
and RNG are used as a case study. For RNG problem the five statistical tests were used to compute
the fitness (each time any test was failed the counter of fitness incremented), such that the random numbers
with minimum fitness was token, and the termination criterion of the proposed algorithm has reached
the maximum cycle number or all the population is random numbers passed the tests. In TSP, the fitness
function in this work was the minimum distance between cities. Total distance for each TSP route for cities n
was calculated according to the coordinates of the cities as follows:
If the coordinates of any two cities i and k are (xi, yi) and (xk, yk), the distance between them
was calculated according to the Euclidean distance equation:
√ (6)
Total tour length f can be expressed as follows:
∑ (7)
where n is the total number of cities. The termination criterion of the proposed algorithm has reached
a maximum cycle number.
4.1. Improving GA population using ABC for RNG
This method consists of two stages where the first stage focuses on generating random numbers
using ABC while the second stage used GA on the random numbers that are generated in the first stage.
Previous hybridization work was improved by generating an initial population by ABC then GA was applied
to the initial population. At last, the population produced from GA was compared with the initial population
and selects the best population (replaces the original parental population) as the new population to the next
generation. Reproduction imitated natural selection. ABC generated numbers randomly within the boundaries
of the parameters by using the following equation:
(8)
where; i=1. . .SN, j=1. . .D. Where SN is the solution number and D is the number of optimization parameters
The created number was converted to binary. After this, the fitness function in the proposed algorithm was
that the five statistical tests might be assigned to the Xij solution by (9), in Algorithm 2.
Fitnessi ∑ (9)
where if it the counter value of the solution Xij.
Algorithm 2: GABC for RNG
INPUT: key's length, population size
OUTPUT: binary keys (k)
Process:
Step1: Generate random numbers using Eq. 8 to be the initial population (I). /* using
ABC*/
Step2: Evaluate (I) using the five statistical tests to compute their fitness using Eq.9.
Repeat
Step3: Call algorithm (3) that takes I and produce a new solution (S) using GA.
Step4: Evaluate S using the five statistical tests to compute their fitness using Eq. 9.
Step5: Select the best populations between S and I to be a new population (K) according
to their fitness. /*Reproduction operation*/
Step6: Until the termination condition is met. /* reached the maximum cycle number or all
the population is random numbers passed the tests*/
Step7: Return K.
End.
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Based on the initial population that was generated from the ABC stage GA performed genetic
operators to generate new offspring. The genetic process was performed iteratively. The new population was
sorted according to its fitness value; the roulette wheel operation was used in the proposed algorithm.
An ordered crossover and uniform mutation were used in Algorithm 3.
Algorithm 3: GA stage for RNG
INPUT: initial population (I)
OUTPUT: New solutions(S)
Process:
Step1: Sort I according to their fitness value. /* ascending order */
Step2: Select from I the best solutions depending on their fitness value (BS). /* Using
roulette wheel selection method*/
Step3: Perform crossover operation between BS to produce new solutions (NS). /* ordered
crossover is used, with a crossover rate of 0.8*/
Step 4: If there is a mutation rate, perform mutation operation and update NS. /* uniform
mutation was used, with a mutation rate of 0.1*/
Step5: Return NS.
End.
4.2. Improving GA population using ABC for TSP
This method consists of two stages in the first stage ABC generates a new route by using (8), and in
the second stage, the population generated in the first stage will be using GA operators to find the best route.
ABC generated random routes by using the index of cities in the default route within the boundaries
of the indices by using (8). After this, (7) was used to calculate the distance for each route. The proposed
method to find the shortest path is shown in Algorithm 4.
Algorithm 4: GABC for TSP
INPUT: Number of cities, default route(D)
OUTPUT: Best solution(NS)
Process:
Step1: Generate new routes (NR) using Equation (8) in ABC.
Step2: Evaluate the new routes using equation (7).
Repeat
Step3: Call algorithm (5) that takes NR and produces NS using GA.
Step 4: Evaluate NS using equation (7).
Step5: Until the termination condition is met. /* Maximum cycle number*/
Step6: Return NS.
End.
At the GA stage, the population produced from the ABC stage was sorted depending on their fitness
value. Roulette wheel operation ordered crossover and swap mutation were used in the proposed algorithm
to generate a new gene. The GA stage is shown in Algorithm 5.
Algorithm 5: GA stage for TSP
INPUT: Initial population (NR)
OUTPUT: New solutions(NS)
Process:
Step1: Sort the NR according to their fitness value. /* ascending order */
Step2: Select from NR the best solutions depending on their fitness value (BS). /* Using
roulette wheel selection method*/
Step3: Perform crossover operation between BS to produce new solutions (NS). /* ordered
Crossover is used, with a crossover rate of 0.85 */
Step4: If there is a mutation rate, perform the mutation operation/*swap mutation, with a
mutation rate of 0.01*/
Step5: Return NS.
5. EXPERIMENTAL RESULTS AND DISCUSSION
The proposed method was evaluated in this section. Two experiments were carried out where
the first experiment aimed to solve RNG while the second problem aimed to solve TSP. Then the solutions
were compared with the traditional GA. All computational experiments were conducted on Intel Core
i7-4600U 2.70 GHz machine and coded using Python 3 programming language.
5.1. RNG experiment parameters
The proposed method tested on data comprises 64, 120,128,192,256 and 512 keys' length
respectively. The process was executed 5 times with various recommended parameters for the three
variations of data. The population size was 10, 20, and 30, respectively, and the maximum iteration number
7. Bulletin of Electr Eng & Inf ISSN: 2302-9285
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2131
(MIN) was 100. The rate of the crossover was 0.8 and the rate of mutation was 0.1. The goal was that all keys
were random according to the five statistical tests. It is based on the number of iterations to achieve the best
solution (7), and the average time execution.
Mean= ∑ (10)
Where the rate is the number of iterations to achieve stop condition, and n is the number of run times.
The summary of the simulation is presented in Tables 1-3. Through the tables below, the proposed
algorithm showed better results than the traditional GA, as generated random numbers in the least number
of iterations and at the lowest execution time, which is what was required to get better solutions in the least
time.
Table 1. Comparison of three methods on the main iteration (population size=10)
Key size Mean by using GA Time Mean by using GABC Time
64 5.6 0.0316 2 0.012
120 15.2 0.1328 6.2 0.0602
128 13 0.1322 10.2 0.0926
192 24.8 0.3172 15.6 0.214
256 31 0.5766 22.2 0.3784
512 56.6 1.8448 44.2 1.4486
Table 2. Comparison of three methods on the main iteration (population size=20)
Key size Mean by using GA Time Mean by using GABC Time
64 5 0.0424 2.4 0.0304
120 11 0.1612 7 0.1212
128 11.8 0.189 8.2 0.1424
192 23.2 0.5502 17 0.4386
256 29.8 0.9176 22.2 0.724
512 49.2 3.1378 39.2 2.5222
Table 3. Comparison of three methods on main iteration (population size=30)
Key size Mean by using GA Time Mean by using GABC Time
64 4.6 0.0654 2.6 0.047
120 12.8 0.3098 6.8 0.1826
128 10.6 0.2456 7.8 0.2034
192 19.8 0.6904 16 0.5942
256 33 1.5206 21.2 1.0122
512 53.8 5.115 38.2 3.6634
5.2. TSP experiment parameters
Experiments were performed to calculate the percentage of relative error, the best tour,
and the average of the tour. The rate of crossover and rate of mutation was 0.85 and 0.01 respectively.
The population size equaled the number of cities, and the number of generations was 5000. The simulation
was performed 5 times with various recommended parameters. The percentage of relative error (%) was
calculated using (11).
(11)
These algorithms were tested using five real TSP problems taken from the TSPLIB which include
eil51, st70, pr76, eil76, and rd100 (the numbers attached to the problem names represent the number of
cities). The default tour is taken from the library of TSPLIB for each instance. Through a Table 4,
the proposed algorithm showed better results than the traditional GA in all five instances, which was what is
required to get a speed convergence to an optimal solution in the least time. When comparing the proposed
algorithm with the GA-PSO algorithm [6] and IRGIBNNM algorithm [7] it is showed that the results
obtained from the proposed algorithm were better than the previous two algorithms. Table 5 showed
the comparison among the algorithms.
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Table 4. The summary of the simulation
Type Optimal solution No. of cities algorithm Best tour Worst tour Average of tour Error% Average time
eil51 426 51 GA 441.809 461.636 448.544 0.053 81.743
GABC 427.050 441.311 435.394 0.002 80.509
st70 675 70 GA 697.434 742.412 718.210 0.033 158.934
GABC 690.627 720.178 702.590 0.023 154.873
eil76 538 76 GA 578.734 593.785 588.499 0.076 199.709
GABC 557.911 589.515 569.278 0.037 183.925
pr76 108,159 76 GA 114861.3 119446.8 118085.3 0.062 185.530
GABC 109087.5 111842.6 110608 0.008 185.837
rd100 7910 100 GA 8922.557 9220.022 9070.083 0.128 287.97
GABC 8171.62 8954.866 8284.771 0.033 281.483
Table 5. The summary of the simulation
Instance GABC GA-PSO IRGIBNNM
Best tour Average of tour Best tour Average of tour Best tour Average of tour
eil51 427.050 435.394 428 437 448 455.3
st70 690.627 702.590 690 703 733 753.4
eil76 557.911 569.278 ---- ---- ---- ----
pr76 109087.5 110,068 109,383 110,522 ---- ----
rd100 8171.62 8284.771 8238 8266 ---- ----
6. CONCLUSION
In this paper, GA was improved using ABC (GABC) by using ABC to generate an initial population
rather than randomly generation that used in traditional GA. Through experimental results ,
it was concluded
that it using a hybrid between GA with ABC has improved GA in RNG by minimizing the number
of iterations in less execution time. And using the five statistical tests has provided a good fitness function for
the RNG problem. The final generated keys that were unique (not repeated), random and cryptographically
strong (successfully passed the five statistical tests). The result of the proposed method in TSP was compared
with traditional GA based on the percentage of relative error rate and the average of the tour's average.
Results showed GABC was better than traditional GA with a lower error rate and high convergence rate.
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BIOGRAPHIES OF AUTHORS
Ali AbdulKadhim Taher received his B.E degree in computer science from University of
Technology in 2010, received his M.S.C degree in computer science from University of
Technology in 2020. He is currently working in the Ministry of Higher Education & Scientific
Research, University of Wasit/ Iraq-Wasit
Suhad Malallah Kadhim received her B.E degree in computer science from University of
Technology in 1994, received her M.S.C degree in computer science from University of
Technology in 1997, and received her Ph.D. degree in computer science from University of
Technology in 2003. She is currently working in University of Technology/Iraq-Baghdad.