A robust parallel algorithm of the particle swarm optimization method for large dimensional engineering problems

The application of the Particle Swarm Optimization (PSO) method to large engineering problems is strongly limited by the required computational cost. This limitation comes from the large number of particles needed to optimize the many-variable function, the high computational cost of its evaluation and the lack of an adequate criteria to early detect the approach of the global optimum. The first two cost sources can be mitigated by an efficient parallel implementation of the PSO method but the last one need the development of a robust convergence criterion for the algorithm. This work develops an efficient and robust optimization method by using a new convergence criterion in an asynchronous parallel implementation of PSO. In the optimization of benchmark test functions, this method showed very good performance, with parallel efficiency between 80% and 100%, and excellent robustness, always detecting the global optimum. Finally, the method was successfully applied to an actual estimation problem with 81 parameters.