Study Of Grain Storages Lacation Model Based On Quantum-based Particle Swarm Optimization

Food is the nation’s blood, basic guarantee of people’s lives. National grain storageconstruction provides a critical infrastructure construction for national food security, thescientific and reasonable selection of sites for grain storage is the basis and premise of grainstorage construction. The reasonable selection of sites for grain storage can not only prolongthe service life of grain storages, but save storing grain and allocation costs, keeping the graincirculation freely.The selection of sites for grain storage is in a certain area locate the new grain storage,and optimize the target (such as investment and operating costs), which is a typical nonlinearprogramming problem with constraints. Extensive literature shows that intelligentoptimization algorithm is an effective way to solve such problems. Being a new intelligentoptimization algorithm, quantum-based particle swarm optimization (QPSO) algorithm isproposed in2004. The QPSO is global convergence, simple to realized, and has lessparameters, with a more advanced swarm intelligence.According to the principle and requirements of sites selection, this paper focuses onbuilding a sites selection model for grain storages, with the method of a spatial clusteringalgorithm with obstacle constraints based on QPSO (QPSCOC), and example analysis underthe application background of the sites selection for Henan grain storage. The main contentsof this paper are presented as follows:Firstly, the steps and development of QPSO algorithm is discussed in terms of efficiencyand practicability, and the QPSO algorithm is compared with particle swarm optimization(PSO) algorithm.Secondly, considering that K-Mediods algorithm is prone to local minimization andsensitive to initial value, this paper proposes a spatial clustering algorithm with obstacleconstraints based on QPSO. Thus such an algorithm not only inherits the global convergencefrom QPSO, but has the same ability of quick solution as K-Mediods. The experiment resultsshow that this algorithm has many advantages such as good convergence performance, simpleoperation and high practicability.Thirdly, based on the theory of facility location, this paper analyzes grain storage’s mainfunction and principle of site selection, and then builds the sites selection model. The algorithm to solution this sites selection model is the spatial clustering algorithm withobstacle constraints based on QPSO.Fourthly, the sites selection model for grain storage being optimized by the QPSCOCalgorithm is introduced. Also, WebGIS is used to analysis the example of the sites selectionfor Henan grain storage. The visual results improve the scientificalness of the sites selectionfor grain storage and provide guidance for actual application.