Indian Journal of Science and Technology
DOI: 10.17485/ijst/2014/v7i6.5
Year: 2014, Volume: 7, Issue: 6, Pages: 795–803
Original Article
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
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
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