Research On The Hot Spot Of Cultivated Land Fragmentation Based On Improved Particle Swarm Optimization Algorithm

The existence of the cultivated land fragmentation affects farmers' production life and the development of agricultural production, Its hot spots is there are many negative impacts. In order to study the spatial distribution of the hot spots of cultivated land fragmentation, This article through to in hot spot study all aspects of the literature reading, summarize the theory method, research hot spot analysis principle; grasp the running mechanism of swarm intelligence algorithm, read the basic theory of particle swarm optimization and improvement methods carefully, mining and hot spot research, the correlation of the algorithm to find suitable for hot spot in the improvement factor; Determine the evaluation method of cultivated land fragmentation, selected the suitable for crushing degree of factor analysis. Finally using the improved particle swarm algorithm for hot spot detection of cultivated land fragmentation. This paper studies the main contents are:(1) In this paper, on the basis of the research hot spots of cultivated land fragmentation theory, using swarm intelligence algorithm for hot spot detection, research the algorithm principle and model are determined.(2) Confirm the evaluation method of cultivated land fragmentation, from the perspective of landscape ecology, establish the evaluation factors of cultivated land fragmentation, choose the patch density, edge density, average area and shape of area weighted index, area weighted fractal dimension five factors to make a comprehensive analysis about the present situation of cultivated land fragmentation.(3) Using the particle swarm algorithm for detecting the hot spots of cultivated land fragmentation, according to the characteristics of hot issues in the particle swarm optimization algorithm combining its own characteristics, and according to the reference object before and after the algorithm focuses on different joined the two weighting factor, dynamically changing inertia weight, and introducing disturbance factor to balance the global. Establish the objective function of the hot research by the size of the fitness function value judgment to update the speed and position of particles in the algorithm, finally, through the concentration of particle to judgment the hot spots of cultivated land fragmentation.(4) Selected Baise as the study area, using GIS technology to the research data acquisition and processing, and spatial correlation analysis was carried out on the evaluation factors of cultivated land fragmentation, build space vector database, using the improved particle swarm algorithm analysis the hot spot of cultivated land fragmentation in Baise from 2001 to 2013,and analyze the result of the hot spots.(5) To compare the improved particle swarm algorithm with the standard particle swarm algorithm in precision and performance analysis, found that the improved algorithm is more efficient operation, the results is better, at the same time also proved that the improved particle swarm algorithm can be used to research hot spot as well.In conclusion, introduce two dynamic inertia weight factors and disturbance factor into the particle swarm algorithm, establish improvement mechanism, simulated hot spot detection process, by Arc GIS platform structures and the Matlab technology, build the model, coding algorithm, finally using this smart swarm optimization algorithms to realize the research in the hot spot of cultivated land fragmentation.