Application of the hybrid particle swarm optimization algorithms for simultaneous estimation of multi-parameters in a transient conduction–radiation problem

Abstract

A Simplex Bare-bones Particle Swarm Optimization (KSM-BBPSO) algorithm based on the K-means clustering was introduced, and on this basis, an improved hybrid Simplex-Particle Swarm Optimization algorithm based on K-means clustering (KSM-PSO) was developed to retrieve the multi-parameters of the semi-transparent media simultaneously in a transient conduction–radiation problem. The conduction–radiation parameter, scattering albedo, and boundary emissivity in a one-dimensional (1-D) homogenous semitransparent slab were estimated simultaneously to illustrate the performances of the KSM-BBPSO and KSM-PSO algorithms. The transient temperature responses on both sides of the medium boundaries exposed to the pulse laser irradiation, which was simulated directly by Finite Volume Method (FVM), were served as input for the inverse analysis. By the KSM-BBPSO algorithm introduced and KSM-PSO algorithm developed, all the thermophysical parameters could be estimated with reasonable accuracy, even with noisy temperature measurements. The KSM-PSO algorithm was proved to be fast, accurate, and robust, while the KSM-BBPSO algorithm has better versatility.