Computer Science – Neural and Evolutionary Computing
Scientific paper
2009-07-06
Computer Science
Neural and Evolutionary Computing
21 pages, 5figures
Scientific paper
Ant Colony Optimization (ACO) has time complexity O(t*m*N*N), and its typical application is to solve Traveling Salesman Problem (TSP), where t, m, and N denotes the iteration number, number of ants, number of cities respectively. Cutting down running time is one of study focuses, and one way is to decrease parameter t and N, especially N. For this focus, the following method is presented in this paper. Firstly, design a novel clustering algorithm named Special Local Clustering algorithm (SLC), then apply it to classify all cities into compact classes, where compact class is the class that all cities in this class cluster tightly in a small region. Secondly, let ACO act on every class to get a local TSP route. Thirdly, all local TSP routes are jointed to form solution. Fourthly, the inaccuracy of solution caused by clustering is eliminated. Simulation shows that the presented method improves the running speed of ACO by 200 factors at least. And this high speed is benefit from two factors. One is that class has small size and parameter N is cut down. The route length at every iteration step is convergent when ACO acts on compact class. The other factor is that, using the convergence of route length as termination criterion of ACO and parameter t is cut down.
Hu Be-Qiong
Hu Wei
Li Xia
Pang Chao-Yang
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