Abstract: Success of project based on an accurate SCE is one of the most significant issues in project management. Based on the conducted statistical studies, it has been revealed that a majority of the software projects were not completed within the cost and time schedule on delivery time which leads to dissatisfaction of customers. Previously, a combination of Genetic Algorithm (GA) and Artificial Immune System (AIS) utilized in evaluating metrics and software criteria in SCE. The low accuracy and non-reliable structures of the algorithmic models led to high risks of software projects. So, it is needed to estimate the cost of the project annually and compare it to the other techniques. The Meta-Heuristic algorithms have been developed well lately in software fields and SCE. Meta-heuristic and Particle swarm optimization (PSO) and ABC with SVM solve the problems according to the optimization of the problems and are very efficient in optimizing the algorithmic models and the effective factors in cost estimation. In this paper we have proposed a hybrid model based on GA and ABC for optimization of the effective factors’ weight in NASA dataset software projects.
Keywords: ABC and Ann for software cost estimation, Artificial Immune System (AIS), novel hybrid PSO.
Title: A Novel Hybrid PSO with ABC and ANN for Software Cost Estimation
Author: S. Sunitha, S. Sam Dhanasekar
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
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