A Modified Sequential Particle Swarm Optimization Algorithm with Future Time Data For Solving Transient Inverse Heat Conduction Problems


The particle swarm optimization (PSO) method is modified and employed to solve the inverse heat conduction problem. Since the main drawback of PSO in solving inverse problems is its slow convergence, most of the modifications in this research are aimed at overcoming this downside. A sequential implementation and a multi-criteria optimization formulation are designed to accelerate the convergence in transient multi-sensor applications. The concept of future time steps is used to make the PSO-based inverse analyzer more stable in dealing with measurement noise. All these modifications are found to be effective in improving the behavior of the algorithm.