A Novel PSO based Approach with Hybrid of Fuzzy C-Means and Learning Automata in Software Cost Estimation

Farhad Soleimanian Gharehchopogh; Laya Ebrahimi; Isa Maleki and Saman Joudati Gourabi

doi:10.17485/ijst/2014/v7i6.5

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A Novel PSO based Approach with Hybrid of Fuzzy C-Means and Learning Automata in Software Cost Estimation

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2014/v7i6.5

Year: 2014, Volume: 7, Issue: 6, Pages: 795–803

Original Article

A Novel PSO based Approach with Hybrid of Fuzzy C-Means and Learning Automata in Software Cost Estimation

Farhad Soleimanian Gharehchopogh^* , Laya Ebrahimi, Isa Maleki and Saman Joudati Gourabi

Department of Computer Engineering, Science and Research Branch, Islamic Azad University, West Azerbaijan, Iran; bonab.farhad@gmail.com, llaya.ebrahimi@gmail.com, maleki.misa@gmail.com, saman.jodati@gmail.com

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

We used Particle Swarm Optimization (PSO) algorithm hybrid with Fuzzy C-Means (FCM) and Learning Automata (LA) algorithms for Software Cost Estimation (SCE). In this paper we test and evaluate PSO-FCM and PSO-LA hybrid models on NASA dataset software projects. The obtained results showed that in the hybrid models the values of Magnitude of Relative Error (MRE) and Mean Magnitude of Relative Error (MMRE) were reduced compared with COCOMO model and also the accuracy of Percentage of Relative Error Deviation (PRED) was higher in the hybrid models.

Keywords: COCOMO Model, Fuzzy C-Means, Learning Automata, Particle Swarm Optimization, Software Cost Estimation