Multi-objective particle swarm optimization applied to distribution fitting for industrial data

The paper proposes an algorithm for distribution fitting which is based on Particle Swarm Optimization. In particular the proposed approach allows to simultaneously estimate the distribution family and the optimal values of the parameters of the selected distribution. The algorithm has been tested on data coming from an industrial application related to the identification of outliers within the data provided by the Pulse Distribution Analysis — Optical Emission Spectrometry, with the final aim of characterizing the non-metallic inclusions in the steel samples.