A stochastic scattering particle swarm optimizer
The particle swarm optimization (PSO) algorithm is a swarm intelligence technique, which has exhibited good performance on finding optimal regions of complex search spaces. However, the basic PSO (bPSO) suffers from the premature convergence in multi-modal optimization. This is due to a decease of swarm diversity that leads to the global implosion and stagnation. It is an acceptable hypothesis that maintaining a high diversity produces a good effect on the search performance of the PSO algorithms. In this paper, we propose a novel optimizer, called the stochastic scattering particle swarm optimizer (SSPSO), which tries to overcome the premature convergence through scattering the swarm stochastically, with a new and simple diversity measure. The performance of the SSPSO is compared with the bPSO on a set of benchmark functions. Experimental results show that, the SSPSO not only prevents the premature convergence to a high degree, but also keeps a rapid convergence rate. Thus, it is clearly a better substitute for the bPSO and other repulsion-based PSO algorithms.