Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects

Sheta, Alaa

doi:10.3844/jcssp.2006.118.123

Cited by 123 publications

(73 citation statements)

References 19 publications

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“…The presented model contains five parameters a, b, c, d and e. This model is slightly different than the model that is proposed in (Sheta, 2006). II.…”

Section: Effort Estimation Model That Is Used In This Studymentioning

confidence: 94%

“…Optimization algorithm have been applied on NASA software project data like (Shin and G o el, 2000) and (Sheta , 2006). The data set consist of two independent variables , Lines Of Code (LOC) and the Methodology (ME) and one dependent variable , effort.…”

Section: Resultsmentioning

confidence: 99%

“…In the recent studies , evolutionary algorithms and genetic algorithms are widely used to estimate the optimal parameters of the software cost models. Typical examples are (Sheta , 2006) and (Huang and C hiu , 2006). …”

Section: Introductionmentioning

confidence: 99%

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Estimation of the Effort Component of the Software Projects Using Heuristic Algorithms

Uysal¹

2010

New Trends in Technologies

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In this study, a multivariate interpolation model was developed to estimate the effort component of the software projects. A COCOMO based equation was used to represent the effort function. The data set that was used consists of two independent variables, first is Lines of Code (LOC) and second is Methodology (ME) and one dependent variable Effort (CE). Data set is taken from NASA projects and the results that are obtained in this work are compared with the results of A.F.Sheta who is produced a similar model for estimating the effort component of software projects. In this paper, it has been shown that Simulated Annealing algorithm can be used to estimate the optimal parameters of the effort components of software projects. The upper and lower bounds of the search space should be considerately given by designer or be cited from other reference papers, if possible.

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“…The presented model contains five parameters a, b, c, d and e. This model is slightly different than the model that is proposed in (Sheta, 2006). II.…”

Section: Effort Estimation Model That Is Used In This Studymentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

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Estimation of the Effort Component of the Software Projects Using Heuristic Algorithms

Uysal¹

2010

New Trends in Technologies

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“…The way of selecting the fitness function is a very significant matter in designing the proposed clustering algorithm, since the solution optimization and the performance of the algorithm count mainly on this fitness function (Alsmadi et al, 2012;Sheta, 2006). Thus; the solutions will be ordered in ascending way after measuring their fitness function based on their fitness value.…”

Section: Objective Functionmentioning

confidence: 99%

A Hybrid Firefly Algorithm With Fuzzy-C Mean Algorithm for Mri Brain Segmentation

Alsmadi¹

2014

American Journal of Applied Sciences

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Image processing is one of the essential tasks to extract suspicious region and robust features from the Magnetic Resonance Imaging (MRI). A numbers of the segmentation algorithms were developed in order to satisfy and increasing the accuracy of brain tumor detection. In the medical image processing brain image segmentation is considered as a complex and challenging part. Fuzzy c-means is unsupervised method that has been implemented for clustering of the MRI and different purposes such as recognition of the pattern of interest and image segmentation. However; fuzzy c-means algorithm still suffers many drawbacks, such as low convergence rate, getting stuck in the local minima and vulnerable to initialization sensitivity. Firefly algorithm is a new population-based optimization method that has been used successfully for solving many complex problems. This paper proposed a new dynamic and intelligent clustering method for brain tumor segmentation using the hybridization of Firefly Algorithm (FA) with Fuzzy C-Means algorithm (FCM). In order to automatically segment MRI brain images and improve the capability of the FCM to automatically elicit the proper number and location of cluster centres and the number of pixels in each cluster in the abnormal (multiple sclerosis lesions) MRI images. The experimental results proved the effectiveness of the proposed FAFCM in enhancing the performance of the traditional FCM clustering. Moreover; the superiority of the FAFCM with other state-of-the-art segmentation methods is shown qualitatively and quantitatively. Conclusion: A novel efficient and reliable clustering algorithm presented in this work, which is called FAFCM based on the hybridization of the firefly algorithm with fuzzy c-mean clustering algorithm. Automatically; the hybridized algorithm has the capability to cluster and segment MRI brain images.

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“…Different authors have applied the various metaheuristic algorithm to improve the performance of the COCOMO model. The author of [6] has applied a genetic algorithm to optimize the parameters of COCOMO model while the author of [1] has applied differential evolution algorithm for the same. The author of [7] has applied BAT algorithm to improve the performance of cost estimation by COCOMO.…”

Section: Introductionmentioning

confidence: 99%

Software Cost Estimation by Optimizing COCOMO Model Using Hybrid BATGSA Algorithm

Nandal¹,

Sangwan²

2018

IJIES

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This paper estimates the effort for software by optimizing the COnstructive COst MOdel (COCOMO) model parameters using hybrid BATGSA (Bat inspired Gravitational Search Algorithm) algorithm. The performance of the COCOMO model completely depends upon its parameters which can be optimized by using meta-heuristic algorithms. This paper uses hybrid BATGSA algorithm which hybrids the improved bat algorithm with the gravitation search algorithm (GSA) to optimize the COCOMO model. The bat algorithm demonstrates the hunting and routing behavior of the bat which is improved by using a random walk in the exploration phase. The exploration phase is further improved by using GSA as gravitation force affects the velocity of the bat. The algorithm has been analyzed on four NASA datasets downloaded from promise repository. The comparison of the algorithm has been made with existing three states of art techniques i.e. COCOMO model, BAT algorithm, Improved BAT(IBAT) algorithm by using normalized error as a parameter. The reduction in error ranges from 2% to 10% on different dataset as compared other state of art algorithms proves the significance of proposed algorithm.

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Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects

Cited by 123 publications

References 19 publications

Estimation of the Effort Component of the Software Projects Using Heuristic Algorithms

Estimation of the Effort Component of the Software Projects Using Heuristic Algorithms

A Hybrid Firefly Algorithm With Fuzzy-C Mean Algorithm for Mri Brain Segmentation

Software Cost Estimation by Optimizing COCOMO Model Using Hybrid BATGSA Algorithm

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