Land Use Allocation Based On Spatial Particle Swarm Optimization

Land resource is plentiful in total in China,whereas it is not the case in average.The land resource per capita is less than the most coutries all over the world,so the land resource is still inadequate in China.As ecomomic bloom since the reform and opening policy,the conflicts between the increasing needs of land and the finite land supply are worsing.In all of the economic activities,urbanization drives the most of land use changes.On one hand,a large amount of rural areas are occupied by construction usage,leading to land shortage.On the other hand,a lot of rural houses are abandoned because more and more rural population are migrating to urban areas,leading to land waste.So it is urgent to optimal land use allocation to blance the confict between land supply and demand and promote sustainable development.Land use allocation is a task of optimize the quantity and the spatial layout of land uses in order to balance land supply and demand and gain more social,economic,and ecological benefits.Land use allocation is a hot topic in research.The researchs on the optimization of the quantity structure of land use advance a big step,whereas the researches on the optimization of spatial layout are inadequate.As the performace of computers has increased surprisingly,intelligent algorithms have been applied in varous fields.Many land use allocation models based on intelligent algorithms are present by researchers in recent years.However,these models are not applied in the practice of land use planning because of some defects.These defects are:(1)not capable of operating at land use patch level because the models are not fully spatialized,(2)not fully integrated of domain knowledge to raise the effectiveness,and(3)hard to be applied in large scale region with high accuracy because of low efficiency.It would be necessary to research on how to make a spatialized,knowledge-based,and efficient land use allocation model,as the model will be capable of solving more complex land use allocation problems in practice.This paper is about the modeling of land use allocation based on particle swarm optimization.This reseach draws attention to solve the defects of spatial operations,domain knowledge integration,and efficiency exsisted in land use allocation models.The research would be helpful to support the decision-makings in land use planning and to improve the system of the theory and methology on land use allocation.The main contents of this research include:(1)Archtect the framework of a spatial particle swarm optimization model.This research transformed the particle swarm optimization model with spatial encoding,spatial operation unit,and spatial operators.The model uses a symbolized encoding technique to avoid the destabilization of traditional numbers based ecoding schemas.Choosing land use patch as the operation units can implement patch level optimization.Four operators including patch neighbor operator,patch edge operator,patch size operator,and patch shape operator are designed to control the size and the shape of land use parcels.Finally,the particle swarm optimization model are enhanced with the ability of solving spatial optimization problems.(2)Integrate the domain knowledge of land uses.The research extract land use knowledge from land suitability,land use locating,and land use policy,then construct a rule based land use knowledge model by knowledge reasoning.Another muti-agent system based land use knowledge model are proposed to extract the knowledge of stakeholders's preferences on land.The model was enhaced with the ability of solving land us allocation problem by integrating knowledge models.(3)Parallize the land use allocation model.The model was parallized by a sub-population strategy on particle swarm optimization.Two models including a master-slave and a peer to peer parallel model are constructed to raise the efficiency of land use allocation.To validate the model,two regions are used as used as the study area.Experiment 1 developed a town scale model to optimize the land use allocation of Gaoqiao Town,Hanzhou City,Zhejiang Province.This model was integrated with patch level operators and a rule based knowledge model.The results showed that the two improvements raise the effectiveness of particle swarm optimization model on both global scale and local scale,proving that the spatial particle swarm optimization model is capable of sovling town scale and multiple land use types related land use allocation problem.Experiment 2 developed a county scale model to optimize the spatila layout of rural settlements in Huangpi District,Wuhan City,Hubei Province.'This model was integrated a multi-agent system based rual sttlements knowledge model.The results showed the multi-agent system integrated model can generate better schemes,proving that the knowledge integrated particle swarm optimization model is capable of sovling county scale and single land use type related land use allocation problem.Experiment 3 parallelized the model of experiment 1 and tested the performance of the master-slave model and peer to peer model.The results showed the parallel particle swarm optimization model successfully descresed the times consumption from minutes to seconds,proving that the parallel model is capable of sovling large scale and high accuracy land use allocation problems.So,after the experiments,it is can be concluded that the spatial,knowledge integrated,and parallel particle swam optimization model can be used in muti-scale,multiple land use types,and high accuracy land use allocation.The model also can be a tool support decision-making in land use planning practices.