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Application Research of Improved Ant Colony Clustering Algorithm in Forest Fire Forecasting

Author: LiuFang
Tutor: LiYiJie
School: Liaoning Technical University
Course: Computer Software and Theory
Keywords: Clustering Ant Colony Algorithm Forest fires Forecast
CLC: TP301.6
Type: Master's thesis
Year: 2009
Downloads: 76
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Abstract


In this paper, the K-means algorithm is prone to a local optimum drawback , the introduction of an improved ant colony algorithm of the population classified ICACA (Improved Character-base Ant Colony Algorithm) to optimize them . The ant colony algorithm uses the principle of positive feedback , and an essentially parallel algorithm , has a strong ability to find better solutions to the not easy to fall into local optimum , which can effectively compensate for K-means algorithm is easy to fall into local optimal defect . The ICACA algorithm proposed in this paper is the basic ant colony algorithm , the introduction of the the sensory perceptual characteristics of the population classified and ants , making ant colony possible to find the optimal solution , effectively avoid the possibility of a local optimum . This improved ant colony algorithm to optimize the K-means algorithm , improved ant colony clustering model , and simulation experiments prove that the algorithm can effectively prevent the stagnation of the K-means algorithm in the clustering process . local optimum phenomenon in order to better achieve the purpose of the global optimum , to optimize the overall performance of the clustering . The improved algorithm is applied to the prediction of the forest fire , forest fire data dimension , the data value , the calculation is more complex . K-means algorithm implementation process , the time complexity of the algorithm is low , and is the preferred method of forest fire prediction . Improved ant colony clustering algorithm , a good balance between the time complexity and clustering accuracy reaches more suitable for the analysis of forest fire prediction . Finally , through the establishment of the fire data mining model of fire data clustering , to achieve the purpose of the fire data classification for forest fire forecast to provide scientific , reliable and objective basis .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > General issues > Theories, methods > Algorithm Theory
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