Research On The Village Land Space Optimization Model Based On Particle Swarm Optimization

The optimized land use allocation is to conservate the intensity of land resource and to realize the sustainable development of land resource. The space optimization of land use has always been the core of land use planning issues. Some of the existing optimization methods are mostly focused on the number of land use structure optimization, then based on the number of structural optimization to optimize the space according to the experience of the traditional optimal allocation of space, This method has not been scientific and operational. Spatial Optimization Decision-making is always complex and frequently asked questions in the GIS application, as relating to a large number of combinations, it is difficult to find the optimal solution by using the exhaustive method and other methods, so it is necessary to find a new theory and method to solve these problems.Particle Swarm Optimization is a new technology in recent years, it can resolve the space optimization decision-making issue by combinating with GIS. The optimal allocation of land use is facing with a goal of diversity and ambiguity, it is difficult to define the problem of optimal allocation in advance, there is no absolute best option, it is a through continuous optimization of the design, gradually closing to the best of such a process. This paper attempts to introduce the Particle Swarm Optimization and propose the Hybrid Synergy PSO algorithm by combinating the PSO with the Synergy theory, improving many aspects and coupling with GIS to solve multi-objective land-use space optimization problem with constraints.Currently, the rural settlements of Village-scale are distributed very messy, extensive agricultural land, the intensive degree of conservation is not high and have a low efficiency of agricultural land use, land degradation etc. In view of this situation, this article bring forward a set of index system aiming at land intensity evaluation of the village-scale, building a space optimization Model of land intensity, together with the model of space compactness、the planning model with the smallest cost as the goal of the space optimization, trying to play a better role for the space optimization configuration of land use. The main contents of this article include the following:(1) Firstly, The article introduced in detail the land-use theory of optimal allocation of space research, technology research, the progress of the swarm intelligence、the particle swarm optimization progress at home and abroad, of their research at home and abroad to conduct a detailed overview, bring forward the existing problems as well as ideas for improvement. This section also introduce the basic theories and methods related to this article:including the basic theory and methods of the space optimization configuration of land use, the theory and methods of multi-objective optimization, the theory and methods of the intensity, as well as the synergy theory、the intelligence optimization method, the Particle Swarm method of etc.(2) Secondly, The article Proposed a land-use intensity model for the space optimization, the specific content including:introducing the basic ideas, basic principles for constructing of the evaluation index system on the Villages-scale of land-intensity, the types of agricultural-towns and industrial-towns for intensity index system and evaluation model of intensive conservation. This article also introduced the minimum cost planning model and the compactness model based on spatial clustering, according to the three models to construct system for multi-objective optimization. Bring forward the constrains system for space optimization (including the number of constraint system and conversion bindings of land-use type), and analying detaily the conversion of land use types and making the general rules of land-use conversion system. Finally, this article design the spatial optimization integrated model of village-scale, combinating the effectiveness of the traditional goals with the objectives of the space layout, the number of binding constraints and the joint effect of spatial pattern to the overall purpose of optimization.(3) Thirdly, the article introduce detaily the optimization model on multi-objective of the Hybrid Synergy Particle Swarm (CPSO-HK) design. This article introduce the core idea of the CPSO-Hk model、the way about how to process constraints, and descript detaily the principle of multi-objective Particle Swarm Optimization、the principle of the Particle Swarm Optimization mixed synergy (CPSO-HK). All parameters are given a detailed introduction in the CPSO-HK model. Including the size of particle swarm, adaptive inertia weight factor, accelerated coefficient as well as the particles’speed and the location; Subsequently, the proposed hybrid synergy PSO algorithm (CPSO-HK) is coupled with GIS to establish the spatial optimal allocation. This section design detaily the key link, including the coding framework of the particles, the initial formation of particles, the smallest evaluation of fitness function of the particles, as well as the selection method of the mixed collaborative particles.(4) Finally, this article carried out the applied research on the basis of the land-use optimization model. According to the JiaYu’land use planning revision as the data, selecting the agricultural-type town of PanJiawan and the industrial-type town of Yuyue as an experimental zone. First of all, the experiment evaluate the land use structure and spatial pattern as well as to evaluate the degree of intensive use. Combining the above results, according to the requirements of the experimental study of land use optimization model CPSO-Hk coupled with the GIS, Studying the specific objective function of the experimental zone for land use optimization model, Restrictive conditions, as well as the issue of data processing model. Finally, to compare the land-use optimization by the CPSO-HK model calculations with GA optimization program. To verify the validity of PSO-based method, and analyzing the optimization results of PanJiawan and Yueyue town, to verify the rationality of the classified index-based intensity system and the adaption of the improved particle swarm optimization, comparing the result with the traditional method, Validating the feasibility and advantages of the coupled model CPSO-Hk and GIS.