Modified particle swarm optimization method to solve one-dimensional IHCP

Particle swarm optimization is one of the most promising methods for solving the inverse heat conduction problem. However, it needs a high computing capacity and a long time to find optimal solutions for large scale problems (large swarm populations). This paper presents a further improvement of our already published GPU-based method. Our new method extends the original algorithm with an initial temporary swarm (which contains significantly more participants than the simulated swarm to better cover the search space). The results are promising, the average fitness values are usually better when using this technique, and (thanks to the well-parallelized graphics accelerator based implementation) the runtime is quite similar.