Abstract
The widespread and increasing application of Particle Swarm Optimizer (PSO) algorithms in both theoretical and practical fields leads to further considerations and new developments for improving its efficiency. To achieve this purpose in this paper a new method is introduced to enhance the convergence rate and reduce the computational time of PSO by combining the PSO including mutation concept (MPSO) and the Hierarchical Particle Swarm Optimizer (HPSO). Therefore the new approach is called MHPSO: a composition of MPSO and HPSO which act simultaneously in the optimization process. In addition some benchmark examples are analyzed using the presented method; consequently, the results are compared to other procedures which illustrate better outcomes and high performance of MHPSO.
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