GDL

Journal from gdlhub / 2022-02-14 15:23:32
Oleh : Shailendra Pratap Singh, Vibhav Prakash Singh, Ashok Kumar Mehta, King Saud University
Dibuat : 2022-02-14, dengan 0 file

Keyword : Homeostasis adaptation, Optimization, Evolutionary algorithm, Software cost estimation
Url : http://https://www.sciencedirect.com/science/article/pii/S1319157818300910
Sumber pengambilan dokumen : web

Among meta-heuristic algorithms, differential evolution (DE) is one of the most powerful nature-inspired algorithm used to solve the complex problems in various application areas. In DE algorithm at higher generations, there is an increase in the computational cost because existing mutation operator may not provide more diversity. In this paper, a new variant of DE has been proposed by incorporating the homeostasis adaption based mutation operator (HABMO), which maintains the diversity when it stuck to the local optimum problem. This operator with DE is applied for the cost estimation in software development, where proposed optimization technique is used with constructive cost model (COCOMO) for optimizing the tuning parameters. The main objective of this work is accurate prediction and minimization of the error like MMRE, MMER, MSE and RMSE in less number of iteration, for COCOMO model. Further, the proposed variant of DE has been compared with different versions of DE and it has been concluded that the proposed HABDE is able to improve the performance of DE algorithm.

Deskripsi Alternatif :

Among meta-heuristic algorithms, differential evolution (DE) is one of the most powerful nature-inspired algorithm used to solve the complex problems in various application areas. In DE algorithm at higher generations, there is an increase in the computational cost because existing mutation operator may not provide more diversity. In this paper, a new variant of DE has been proposed by incorporating the homeostasis adaption based mutation operator (HABMO), which maintains the diversity when it stuck to the local optimum problem. This operator with DE is applied for the cost estimation in software development, where proposed optimization technique is used with constructive cost model (COCOMO) for optimizing the tuning parameters. The main objective of this work is accurate prediction and minimization of the error like MMRE, MMER, MSE and RMSE in less number of iteration, for COCOMO model. Further, the proposed variant of DE has been compared with different versions of DE and it has been concluded that the proposed HABDE is able to improve the performance of DE algorithm.

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