Particle Swarm Optimization algorithm based on Diversified Artificial Particles (PSO-DAP)

Speed of convergence in the PSO is very high, and this issue causes to the algorithm can't investigate search space truly, When diversity of the population decreasing, all the population start to liken together and the algorithm converges to local optimal swiftly. In this paper we implement a new idea for better control of the diversity and have a good control of the algorithm's behavior between exploration and exploitations phenomena to preventing premature convergence. In our approach we have control on diversity with generating diversified artificial particles (DAP) and injection them to the population by a particular mechanism when diversity lessening, named Particle Swarm Optimization algorithm based on Diversified Artificial Particles (PSO-DAP). The performance of this approach has been tested on the set of ten standard benchmark problems and the results are compared with the original PSO algorithm in two models, Local ring and Global star topology. The numerical results show that the proposed algorithm outperforms the basic PSO algorithms in all the test cases taken in this study