Optimization of Function by using a New MATLAB based Genetic Algorithm Procedure

Purohit, G. N.; Sherry, Arun Mohan; Saraswat, Manish

doi:10.5120/10001-4212

Cited by 20 publications

(11 citation statements)

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“…MATLAB is one of the most popular environments that are used in many research areas. Therefore, Purohit et al explored the prospect of GA for optimization using Rastrigin function inside MATLAB. The authors used the optimization tool in MATLAB where they selected the option ga—Genetic algorithm as a solver and rastriginsfcn as fitness function, and the diversity of population can be detected by measuring the average distance between individuals.…”

Section: Literature Reviewmentioning

confidence: 99%

“…GA depends on selecting the most effective solution among wide space of solutions, so in order to execute GA, a primary population should be created, where any individual in the population is considered as a chromosome, and this algorithm is inspired from evolution's theory by Darwin “survival of fittest.” The most adequate individual in any population has the best chance to be reproduced into the next generation. GAs are based on biological basics, which are easy to construct and their implementation does not need a huge amount of storage that is why GAs are an appropriate choice for optimization problems …”

Section: Introductionmentioning

confidence: 99%

“…GAs are based on biological basics, which are easy to construct and their implementation does not need a huge amount of storage that is why GAs are an appropriate choice for optimization problems. 5 Particle swarm optimization is a stochastic optimization algorithm invented by James Kennedy and Russell Eberhart in 1995; the algorithm was inspired by the social behavior of animal societies like insect, birds, fish, and other societies. 6 Many researches used particle swarm to solve many complex problems like software testing, job sequencing problems, and the traveling salesman problem.…”

Section: Introductionmentioning

confidence: 99%

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Software fault prediction using particle swarm algorithm with genetic algorithm and support vector machine classifier

Alsghaier

Akour

2020

Softw Pract Exp

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Summary Software fault prediction is a process of developing modules that are used by developers in order to help them to detect faulty classes or faulty modules in early phases of the development life cycle and to determine the modules that need more refactoring in the maintenance phase. Software reliability means the probability of failure has occurred during a period of time, so when we describe a system as not reliable, it means that it contains many errors, and these errors can be accepted in some systems, but it may lead to crucial problems in critical systems like aircraft, space shuttle, and medical systems. Therefore, locating faulty software modules is an essential step because it helps defining the modules that need more refactoring or more testing. In this article, an approach is developed by integrating genetics algorithm (GA) with support vector machine (SVM) classifier and particle swarm algorithm for software fault prediction as a stand though for better software fault prediction technique. The developed approach is applied into 24 datasets (12‐NASA MDP and 12‐Java open‐source projects), where NASA MDP is considered as a large‐scale dataset and Java open‐source projects are considered as a small‐scale dataset. Results indicate that integrating GA with SVM and particle swarm algorithm improves the performance of the software fault prediction process when it is applied into large‐scale and small‐scale datasets and overcome the limitations in the previous studies.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Software fault prediction using particle swarm algorithm with genetic algorithm and support vector machine classifier

Alsghaier

Akour

2020

Softw Pract Exp

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“…For most of optimizing problems in the real world, there are constraints to be satisfied. A particular difficulty in handling constrained optimization is that the solutions cannot be guaranteed to be feasible for all results during the searching process [12,13]. Hence evolutionary algorithms like Genetic Algorithm (GA) are incapable of optimizing the constrained problem directly, and some techniques must be introduced to the optimization under constraints [14,15].…”

Section: Introductionmentioning

confidence: 99%

A new constraint handling method for wind farm layout optimization with lands owned by different owners

Wang

Tan

et al. 2015

Renewable Energy

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a b s t r a c tFor wind farm optimizations with lands belonging to different owners, the traditional penalty method is highly dependent on the type of wind farm land division. The application of the traditional method can be cumbersome if the divisions are complex. To overcome this disadvantage, a new method is proposed in this paper for the first time. Unlike the penalty method which requires the addition of penalizing term when evaluating the fitness function, it is achieved through repairing the infeasible solutions before fitness evaluation. To assess the effectiveness of the proposed method on the optimization of wind farm, the optimizing results of different methods are compared for three different types of wind farm division. Different wind scenarios are also incorporated during optimization which includes (i) constant wind speed and wind direction; (ii) various wind speed and wind direction; and (iii) the more realistic Weibull distribution. Results show that the performance of the new method varies for different land plots in the tested cases. Nevertheless, it is found that optimum or at least close to optimum results can be obtained with sequential land plot study using the new method for all cases. It is concluded that satisfactory results can be achieved using the proposed method. In addition, it has the advantage of flexibility in managing the wind farm design, which not only frees users to define the penalty parameter but without limitations on the wind farm division.

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“…2,3 However, in order to enable further advances of this technology, it is essential to have a known and reliable relationship between the input parameters and their desired output results, such as material removal rate and kerf taper, so that the optimum process parameters can be identified. [4][5][6][7][8][9][10][11] The quality of surfaces generated by AJM has been discussed by many investigators seeking to estimate the effects of process parameters. Balasubramaniam et al [12][13][14] found the surface generated had a reverse bell-mouthed shape, with entry side diameter in the target material depending on the values of the process parameters.…”

Section: Introductionmentioning

confidence: 99%

Abrasive jet drilling of glass sheets: Effect and optimisation of process parameters on kerf taper

Nassef

Elkaseer

Abdelnasser

et al. 2018

Advances in Mechanical Engineering

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This article reports an investigation of the influence of process parameters on the obtainable dimensional accuracy when drilling glass using abrasive jet machining. In particular, holes were drilled out of glass sheets, and the effects of standoff distance, nozzle diameter, particle grain size and applied pressure on the kerf taper were examined. An artificial neural network technique was used to establish a precise model of kerf taper as a function of the process parameters. The proposed model was then optimised, and the conditions to minimise the kerf taper were identified using a genetic algorithm. The results revealed that standoff distance has a major effect on kerf taper, and it proved possible to substantially reduce the kerf taper by applying an axial feed to the nozzle so that the standoff distance is kept constant during the machining process.

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Optimization of Function by using a New MATLAB based Genetic Algorithm Procedure

Cited by 20 publications

References 1 publication

Software fault prediction using particle swarm algorithm with genetic algorithm and support vector machine classifier

Software fault prediction using particle swarm algorithm with genetic algorithm and support vector machine classifier

A new constraint handling method for wind farm layout optimization with lands owned by different owners

Abrasive jet drilling of glass sheets: Effect and optimisation of process parameters on kerf taper

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