Application of GIS and particle swarm optimization in rural substation locating

Considering the defects of Particle Swarm Optimization (PSO) in the optimization of rural substation location, which constringency speed of getting the global optimum is slow and that is easy to fall into local optimum, this study took development zone of one county as an example to optimize the substation location. The inertia weight dynamic adjustment strategy was utilized to balance effectually the global and local search ability, which greatly improved the capability of PSO algorithm. By considering the influence of Geographic Information System (GIS) on the substation site, the improved PSO was combined with graph problem. Results show that the iteration times of PSO is 48 and the iteration times of the improved PSO reduces to 26. The speed of getting global optimum has doubled. The visualization of the planning process was realized by GIS.