Cauchy particle swarm optimization with dynamic adaptation applied to inverse heat transfer problem

The particle swarm optimization (PSO) algorithm is a member of the wide category of swarm intelligence methods for solving global optimization problems. Its basic idea is the simulation of simplified animal social behaviors such as fish schooling and bird flocking. PSO algorithms are attracting attentions in recent years, due to their ability of keeping good balance between convergence and diversity maintenance. Several attempts have been made to improve the performance of the original PSO algorithm. In this paper, a modified version of the original PSO based on Cauchy distribution and dynamic adaptation of inertia factor, named modified PSO (MPSO), is proposed. to estimate the unknown variables of an inverse heat transfer problem. To validate the optimization performance of the proposed MPSO, an inverse heat transfer problem is illustrated and the algorithm has to estimate its unknown variables. The results testify that the MPSO can perform well in an inverse heat transfer problem.