New Approach with Hybrid of Artificial Neural Network and Ant Colony Optimization in Software Cost Estimation

Nowadays, software cost estimation (SCE) with machine learning techniques are more performance than other traditional techniques which were based on algorithmic techniques. In this paper, we present a new hybrid model of multi-layer perceptron (MLP) artificial neural network (ANN) and ant colony optimization (ACO) algorithm for high accuracy in SCE called Multilayer Perceptron Ant Colony Optimization (MLPACO). Current research uses some of features for increasing accuracy of estimation among of the existing parameters has been considered for effort estimation in software projects, and then these selected features will be filtered by ACO algorithm in order to reach highest accuracy in estimation and optimization of MLP ANN method. The results show that this novel approach with high accuracy for more than 80% cases is better than algorithmic constructive cost model (COCOMO) in the majority cases. Also, the results of proposed algorithm show that mean magnitude of relative error (MMRE) in the proposed algorithm is lower than COCOMO model.