An application of particle swarm optimization to identify the temperature-dependent heat capacity

An inverse analysis of estimating temperature-dependent heat capacity for a heat conduction problem is presented. The heat capacities are assumed to be temperature-dependent of first and second-order polynomial functions, respectively. A fitness function is minimized by implementing the particle swarm optimization algorithm for the inverse analysis. Computation results are provided to demonstrate the performance of the particle swarm optimization algorithm. The comparison to the modified genetic algorithm is also included.