Software cost estimation model based on fuzzy C-means and improved self adaptive differential evolution algorithm

Gouda, Sunil Kumar; Mehta, Ashok Kumar

doi:10.1007/s41870-022-00882-4

Cited by 7 publications

(5 citation statements)

References 28 publications

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“…address optimization problems without delving into the internal intricacies of the problem's performance [1]. These algorithms are computationally simple but powerful, and they are not limited by restrictive assumptions regarding the search space.…”

Section: A New Approach For Software Cost Estimation With a Hybrid Ta...mentioning

confidence: 99%

“…In this section, we discuss artificial intelligence techniques and algorithmic models that have tested and analyzed software projects. SCE and SEE models are proposed based on fuzzy C-means clustering and metaheuristic algorithms [1]. Compared to previous algorithms, the proposed algorithm has demonstrated superior results in cost prediction.…”

Section: Related Workmentioning

confidence: 99%

“…A more comprehensive examination of a project's viability and more effective management of the software development process enable development organizations to significantly reduce project risks [1], [2]. Possessing an accurate and reliable software cost estimation (SCE), especially at the beginning of software projects, is considered a crucial factor for project success.…”

Section: Introductionmentioning

confidence: 99%

“…The accurate estimation of the production SCE gives the project manager strong support for making different decisions during the software life cycle. The amount of time and effort required to develop a software project should also be known by analysts, designers, programming teams, and other software production forces [1], [3].…”

Section: Introductionmentioning

confidence: 99%

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A New Approach for Software Cost Estimation with a Hybrid Tabu Search and Invasive Weed Optimization Algorithms

Mohamedyusf,

Sharif,

Ghareb

2024

UHD J SCI TECH

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Due to the ever-increasing progress of software projects and their widespread impact on all industries, models must be designed and implemented to analyze and estimate costs and time. Until now, most of the software cost estimation (SCE) has been based on the analyst’s experiences and similar projects and these models are often inaccurate and inappropriate. The project will not be finished in the specified time and will include additional costs. Algorithmic models such as COCOMO are not very accurate in SCE. They are linear and the appropriate value for effort factors is not considered. On the other hand, artificial intelligence models have made significant progress in the cost estimation modeling of software projects in the past three decades. These models determine the correct value for effort factors through iteration and training, providing a more accurate estimate compared to algorithmic models. This paper employs a hybrid model incorporating the Tabu Search (TS) algorithm and the Invasive Weed Optimization (IWO) algorithm for SCE. IWO algorithm solutions are improved using the TS algorithm. The NASA60, NASA63, NASA93, KEMERER, and MAXWELL datasets are used for the evaluation. The proposed model has been able to reduce the MMRE rate compared to the IWO algorithm and the TS algorithm. The proposed model on the NASA60, NASA63, NASA93, KEMERER, and MAXWELL datasets obtained values of MMRE of 15.43, 17.05, 28.75, 58.43, and 22.46, respectively.

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Section: A New Approach For Software Cost Estimation With a Hybrid Ta...mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Approach for Software Cost Estimation with a Hybrid Tabu Search and Invasive Weed Optimization Algorithms

Mohamedyusf,

Sharif,

Ghareb

2024

UHD J SCI TECH

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“…Subsequently, we employ the fuzzy C-means clustering algorithm with three cluster heads to group similar projects in the COCOMO-81 dataset. Furthermore, we utilize an improved self-adaptive differential evolution algorithm to optimize the parameters of the constructive cost model [44].…”

Section: Differential Evolutionmentioning

confidence: 99%

A Shadowed Type-2 Fuzzy Approach for Crossover Parameter Adaptation in Differential Evolution

et al. 2023

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The shadowed type-2 fuzzy systems are used more frequently today as they provide an alternative to classical fuzzy logic. The primary purpose of fuzzy logic is to simulate reasoning in a computer. This work aims to use shadowed type-2 fuzzy systems (ST2-FS) to dynamically adapt the crossing parameter of differential evolution (DE). To test the performance of the dynamic crossing parameter, the motor position control problem was used, which contains an interval type-2 fuzzy system (IT2-FS) for controlling the motor. A comparison is made between the original DE and the algorithm using shadowed type-2 fuzzy systems (DE-ST2-FS), as well as a comparison with the results of other state-of-the-art metaheuristics.

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Power Enterprise Talent Selection Method Based on FCM Algorithm

Zhu

2023

Lecture Notes on Data Engineering and Communications Technologies

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Software cost estimation model based on fuzzy C-means and improved self adaptive differential evolution algorithm

Cited by 7 publications

References 28 publications

A New Approach for Software Cost Estimation with a Hybrid Tabu Search and Invasive Weed Optimization Algorithms

A New Approach for Software Cost Estimation with a Hybrid Tabu Search and Invasive Weed Optimization Algorithms

A Shadowed Type-2 Fuzzy Approach for Crossover Parameter Adaptation in Differential Evolution

Power Enterprise Talent Selection Method Based on FCM Algorithm

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