Concentric spatial extension based particle swarm optimization inspired by brood sorting in ant colonies

In this paper, a concentric spatial extension based particle swarm optimization (CSE-PSO) is proposed by combining the spatial extension with the brood sorting in ant colonies, which leads to a concentric spatial extension scheme for the PSO. The brood sorting in ant colonies endows the particles in PSO with different radii adaptively according their distances to the best position of the swarm. In such a way, the search space in the CSE-PSO is not only enlarged greatly but also the diversity of the swarm in the CSE-PSO is increased accordingly. Meanwhile, a better trade-off between exploration and exploitation in the PSO is achieved by the concentric spatial extension. Simulation results on the fifteen benchmark test functions announced in IEEE CEC´2005 show that the proposed CSE-PSO is not only capable of speeding up the convergence but also improving the performance of global optimizer greatly on all the fifteen benchmark test functions.