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Premature convergence in particle swarm optimization (PSO) algorithm usuallyleads to gaining local optimum and preventing from surveying those regions ofsolution space which have optimal points in. In this paper, by applying specialmechanisms, suitable regions were detected and then swarm was guided to them bydispersing part of particles on proper times. This process is called dynamicswarm dispersion in PSO (DSDPSO) algorithm. In order to specify the propertimes and to rein the evolutionary process alternating between exploring andexploiting behaviors, we used a diversity measuring approach and implementedthe dispersion mechanism. To promote the performance of DSDPSO algorithm, threedifferent policies including particle relocation, velocity settings ofdispersed particles and parameters setting were applied. We compared thepromoted algorithm with similar new approaches and according to the numericalresults, the proposed algorithm outperformed the basic GPSO, LPSO, DMS-PSO,CLPSO and APSO in most of the 12 standard benchmark problems with differentproperties taken in this study.

Dynamic Swarm Dispersion in Particle Swarm Optimization for Mining Unsearched Area in Solution Space (DSDPSO)