Abstract
This paper proposes a novel particle swarm optimization (PSO) model focused on updating the global best solution (gbest). It is fundamentally different from the standard PSO model that gbest subordinates particles and locates them in the proposed one. Particles are generally distributed around gbest so as to search locally; however, when gbest almost stops at some point, they are forcibly moved farther away from it. The above search characteristics of the proposed model is achieved by the introduction of non-uniformly distributed random numbers and the adjustment of the distance how far to spread particles from gbest according to its velocity. It is also of advantage that the functionality of the proposed PSO model is essentially independent of the choice of its parameters. Numerical results for benchmark problems verify that for most cases the proposed model outperforms the standard PSO with the aid of Linearly Decreasing Inertia Weight Approach (LDIWA). Results also show that the proposed model is likely to work better for higher dimensional problems.
