  Premature convergence in particle swarm optimization (PSO) algorithm usually
leads to gaining local optimum and preventing from surveying those regions of
solution space which have optimal points in. In this paper, by applying special
mechanisms, suitable regions were detected and then swarm was guided to them by
dispersing part of particles on proper times. This process is called dynamic
swarm dispersion in PSO (DSDPSO) algorithm. In order to specify the proper
times and to rein the evolutionary process alternating between exploring and
exploiting behaviors, we used a diversity measuring approach and implemented
the dispersion mechanism. To promote the performance of DSDPSO algorithm, three
different policies including particle relocation, velocity settings of
dispersed particles and parameters setting were applied. We compared the
promoted algorithm with similar new approaches and according to the numerical
results, the proposed algorithm outperformed the basic GPSO, LPSO, DMS-PSO,
CLPSO and APSO in most of the 12 standard benchmark problems with different
properties taken in this study.
