Research On Spatial Optimization Location Method Based On CKRPSO Algorithm

The problem of spatial optimization is to select one or more spatial locations for a spatial object in a certain geographical area,so as to achieve the maximum comprehensive index.It often involves high dimensional space,massive data,multiple conflicting objectives and multi constraints,which making it becomes a typical combinatorial optimization problem,the heuristic algorithm and the rise in recent years can solve this kind of problem effectively.This paper was improved by one of the heuristic algorithm of the particle swarm algorithm is proposed with a compression factor K means clustering random particle swarm optimization(CKRPSO)algorithm,combined with GIS spatial analysis and visualization functions of spatial location optimization.According to the particle swarm algorithm is easy to fall into local optimal point,considering multiple populations are more likely to find the global optimal solution,then combined with the K-Means clustering algorithm to enhance the diversity of the population,to further improve the efficiency of algorithm.The relationship between the global optimal solution,the local optimal solution and the optimal solution is obtained by adding the compression factor.Moreover,the best uniform mutation particle swarm optimization algorithm is introduced to enhance the searching ability of the algorithm,and finally the CKRPSO algorithm is formed.The experimental results show that the proposed algorithm is more robust,less time complexity and faster solution.Finally,I use MATLAB and C#to realize the CKRPSO algorithm by mixed programming,and apply it to the location of Jianghan District shopping mall in Wuhan.The application of PSO algorithm and CKRPSO algorithm shows that the CKRPSO algorithm has stronger global searching ability and faster solving speed,and it consumes less time.