A Novel PSO Algorithm Based on the Spatial Distribution of Fitness

In order to solve the defects of particle swarm optimization (PSO) algorithm such as to be easily trapped in local extremum, converge slowly and optimize poorly at the final evolution stage, an improved PSO (WPSO) is proposed in this paper. Based on the spatial distribution of fitness, the population is divided into three sub-groups. For each sub-group, different strategies are used to maintain the diversity of inertia weight, thus global and local optimization can be ensured at the same time. And three classic test functions are adopted in simulation, comparing with linear decreasing inertia weight PSO algorithm, the results indicate that: the proposed method effectively avoids the premature convergence, significantly improves convergence rate and search capability, and has good robustness.