A particle swarm optimization with moderate disturbance strategy

In this paper, we first propose an attractor point to accelerate the convergence rate of particle swarm optimization (PSO). Second, for enhancing the global search ability of PSO, we introduce a new operator based Gaussian distribution function into PSO algorithm. It helps the particles not only have more exploration ability but also focus on searching on the local area of the attractor point. Nine benchmark functions are used to test the performance of the proposed PSO algorithm. The results show that MDPSO performs much better than the other algorithms in terms of the quality of solution.