An Improved Multilayer Perceptron Artificial Neural Network with Genetic Algorithm for Software Cost Estimation

Nowadays, Software Cost Estimation (SCE) with high precision has been one of the challenging main complex issues for software companies and their executives in software engineering. In the past several decades, the use of Artificial Neural Network (ANN) models in this field has been more efficient compared to traditional techniques which are based on algorithmic methods. ANN models which work on the basis of the data obtained from the previous projects have the advantage of increasing the accuracy of estimation when faced with similar projects during the steps of software life cycle. In this article, we have used perceptron which is a multilayer perceptron (MLP) ANN model for better estimation; in order to optimize this model, we have utilized a strong method called Genetic Algorithm (GA) and the simulation results indicate that the proposed model is more optimal than Algorithmic COCOMOII and MLP ANN models.