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Information Journal Paper

Title

A Hybrid Approach for Software Development Effort Estimation using Neural networks, Genetic Algorithm, Multiple Linear Regression and Imperialist Competitive Algorithm

Pages

  207-224

Keywords

Multiple Linear Regression (MLR) 
Genetic Algorithm (GA) 
Imperialist Competitive Algorithm (ICA) 

Abstract

 Nowadays, e ort estimation in software development is of great value and signi cance in project management. Accurate and appropriate cost estimation not only helps customers trust to invest but also has a signi cant role in logical decision making during project management. Di erent models of cost estimation are presented and employed to the date, but the models are application speci c. In this paper, a three-phase hybrid approach is proposed to overcome the problem. In the rst phase, features are selected using a combination of genetic algorithm and the perceptron Neural Network. In the second phase, impact factors are associated to each selected feature using multiple linear regression methods which act as coe cients of in uence for each feature. In the last and the third phase, the feature weights are optimized by Imperialist Competitive Algorithm. To compare the proposed model for e ort estimation with state-of-the-art models, three datasets are chosen as benchmark, namely COCOMO, Maxwell and Albrecht. The datasets are standard and publicly available for assessment. The experiments show promising results and average performance is improved by the proposed model for MMRE performance criterion on the datasets by 23%, 38% and 35%, respectively.

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    APA: Copy

    Khazaiepoor, Mahdi, Khatibi Bardsiri, Amid, & KEYNIA, FARSHID. (2020). A Hybrid Approach for Software Development Effort Estimation using Neural networks, Genetic Algorithm, Multiple Linear Regression and Imperialist Competitive Algorithm. INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS, 11(1), 207-224. SID. https://sid.ir/paper/340845/en

    Vancouver: Copy

    Khazaiepoor Mahdi, Khatibi Bardsiri Amid, KEYNIA FARSHID. A Hybrid Approach for Software Development Effort Estimation using Neural networks, Genetic Algorithm, Multiple Linear Regression and Imperialist Competitive Algorithm. INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS[Internet]. 2020;11(1):207-224. Available from: https://sid.ir/paper/340845/en

    IEEE: Copy

    Mahdi Khazaiepoor, Amid Khatibi Bardsiri, and FARSHID KEYNIA, “A Hybrid Approach for Software Development Effort Estimation using Neural networks, Genetic Algorithm, Multiple Linear Regression and Imperialist Competitive Algorithm,” INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS, vol. 11, no. 1, pp. 207–224, 2020, [Online]. Available: https://sid.ir/paper/340845/en

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