Parallelization of particle swarm optimization using message passing interfaces (MPIs)

Motivated by the growing demand of accuracy and low computational time in optimizing functions in various fields of engineering, an approach has been presented using the technique of parallel computing. The parallelization has been carried out on one of the simplest and flexible optimization algorithms, namely the particle swarm optimization (PSO) algorithm. PSO is a stochastic population global optimizer and the initial population may be provided with random values and later convergence may be achieved. The use of message passing interfaces (MPIs) for the parallelization of the asynchronous version of PSO is proposed. In this approach, initial population has been divided between the processors chosen at run time. Numerical values obtained using above approach are at last compared for standard test functions.