Computational properties of hybrid methods with PSO and de

In this paper, we present a new type of hybrid methods for global optimization with particle swarm optimization (PSO) and differential evolution (DE), which have been attracting interest as heuristic and global optimization methods. Concretely, "p-best solutions" as the targets of PSO's particles are actuated by DE's evolutional mechanism in order to promote PSO's global searching ability. The presented hybrid method works effectively because PSO acts as a local optimizer and DE plays a role as a global optimizer. To evaluate performance of the hybridization, our method is applied to some benchmarks and is compared with the separated PSO and DE. Through computer simulations, it is demonstrated that the proposed hybrid method performs rather better than the component algorithms separately.