Resumen

BODERO POVEDA, Elba  y  LEGUIZAMON, Guillermo. Effect of the PSO acceleration coefficients on the performance of an Artificial Neural Network applied to the Cost Estimation. Novasinergia [online]. 2018, vol.1, n.1, pp.33-40.  Epub 01-Jun-2021. ISSN 2631-2654.  https://doi.org/10.37135/unach.ns.001.01.04.

The particle metaheuristics Particle Swarm Optimization (PSO) since its appearance has proven to be efficient in solving optimization problems, the variation of its parameters has allowed to improve its efficiency. The present work is focused on performing a comparative study of the effect of the acceleration coefficients c1 and c2, on the performance of PSO to solve a problem of cost estimation, through an Artificial Neural Network (ANN) sigmoidal feedforward. A range of values ​​was evaluated in the acceleration coefficients, the other parameters, in this case inertial factor and the swarm size were worked with fixed values. The validation of the solution was carried out by means of a pipeline data set for fluid transfer used in the industry, coming from a real case, with information related to weight, welding type, diameter and the corresponding cost. The objective function used is the Mean Square Error (MSE), calculated between the observed values ​​and the values ​​estimated by the ANN. From the results it can be seen that very small values ​​of c1 and c2 obtain low accuracy in the estimation of pipe manufacturing costs, while the best accuracy is achieved by means of acceleration coefficients with values ​​greater than or equal to 0.5.

Palabras clave : PSO Acceleration Coefficients; Estimation of Costs; Population Metaheuristics; Particle Swarm Optimization; Artificial Neural Network.

        · resumen en Español     · texto en Español     · Español ( pdf )