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Cost estimation model using enhance-based differential evolution algorithm

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Abstract

In the present scenario, the cost estimation is one of the challenging problems of software engineering. The problem of cost estimation has increased in the maintenance phase of software development life cycle. This type of problem occurs to solve the optimization algorithm like metaheuristic approach used to reduce the effort and error. This approach is used to reduce the error rate for the initial phase of the software maintenance process. In this paper, new mutation strategies are proposed to improve the convergence rate of differential evolution (DE) algorithm. This algorithm enhances the accuracy of the semidetached model. This model optimizes the parameters using the enhance-based differential evolution algorithm (EABMO). The proposed approach does a better performance of most of the benchmark function (f1–f24). Further, this approach applied the real application of the software industry for reducing the cost estimation and error measurement. The proposed approach minimizes the error comparing performance of semidetached model (project) like magnitude of relative error, mean magnitude relative error and mean squared error. The result verifies that our proposed EABMO algorithm performs better than the semidetached model based DE, GA and PSO algorithm.

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  1. Department Computer Science and Engineering, Bundelkhand Institute of Engineering and Technology Jhansi, Jhansi, UP, India

    Shailendra Pratap Singh

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  1. Shailendra Pratap Singh
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Correspondence to Shailendra Pratap Singh.

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Singh, S.P. Cost estimation model using enhance-based differential evolution algorithm. Iran J Comput Sci 3, 115–126 (2020). https://doi.org/10.1007/s42044-019-00049-8

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