A particle swarm optimization algorithm based on diversity strategy
WU Hai-liana · Journal of Nanchang University · 2013
In this paper,based on perturbed particle swarm algorithm,a particle swarm optimization algorithm based on diversity strategy(ARPPSO) is developed.The new method introduces attraction and repulsion mechnisms into the velosity reform function,which is reformed by random perturbations global extremum;Considering that whichever particle moves out of the boundary in each dimension of solution space,the physical reflection theory is adopted to improve the efficient variety of the particle swarm.As a result,it effectively maintains the diversity of the population,through empirically testing and comparing with other published methods on benchmark functions.The experimental results illustrate that the proposed algorithm largely avoids premature convergence and improves convergence accuracy besides keeping good convergence performance.Especially it is very competitive for complex multimodal function optimization.