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Research on Ant Colony Algorithm and Application of Cluster

Author: LiangJunLing
Tutor: WangXiangDongï¼›XiaoRenYue
School: South China University of Technology
Course: Computational Mathematics
Keywords: Ant Colony Algorithm Clustering algorithm Ant Colony Clustering Algorithm Adaptive
CLC: TP301.6
Type: Master's thesis
Year: 2011
Downloads: 67
Quote: 1
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Abstract


The ant colony algorithm is a simulated ants foraging heuristic bionic algorithm , it is derived from the simulation of the real ant populations reflects the collaborative process of real ant colony . Distributed computer system ant colony algorithm , strong adaptability , easy to combine with other algorithms , etc. , has shown good performance and great potential in solving many complex optimization problems in recent years to attract many scholars at home and abroad were extensively studied . Cluster analysis is a research data on the logical or physical relationship by certain rules of the data set is divided into several classes of similar data points in nature . The results of the cluster analysis can reveal not only the intrinsic relationship between the data and also provides important data by source for further data analysis and knowledge discovery . This paper first introduces the current ant colony algorithm , the basic principles of the model and the advantages and disadvantages , clustering analysis of the status quo , the model and the main method , and then analyzes the principle of ant colony clustering algorithm . Ant colony clustering algorithm is divided into two categories , one is based on the the ant heap principle of ant colony clustering algorithm , a class of ant colony clustering algorithm based on the foraging principles . BM model and the AM model , as well as the basic ant colony clustering algorithm LF algorithm . An improved adaptive ant colony clustering algorithm (Improved Adaptive Ant Clustering, IAAC) The algorithm to improve the AM model , and proposed a grid mobile strategy to improve the randomness of ants moving on this basis , the ants have more consciousness to the mode of regional mobile greatly reduced invalid mobile ants , so ants quickly find the right position to put down mode , and proposed an adaptive method to adjust the ant movement threshold in order to simplify the parameter selection algorithm to constantly adjust the threshold value based on the current clustering better clustering results . Kite grass (Iris), wine (Wine) and glass (Glass) data set experiments as well as in the economic sentiment indicator is divided show , IAAC algorithm has a high operating efficiency , simple and self-adaptive parameter selection .

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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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