Title :
Hybrid Ant Colony Algorithm Based on Scale Compression
Author :
Yan, Jian-Feng ; Li, Na ; Li, Wei-Hua ; Shi, Hao-Bin
Author_Institution :
Northwestern Polytech. Univ., Shannxi
Abstract :
To improve performance of ant colony algorithm when solving large-scale TSP problem, a hybrid ant colony algorithm based on scale compression is proposed. First we use genetic algorithm to generate a suboptimal solution set and calculate their intersection. By eliminating all cities mapped by the elements among the intersection in the primal TSP problem, we convert the original problem into a new one with smaller scale. In addition, we design a new optimal state transition rule based on regional characteristic of optimal solutions to accelerate convergence speed. Simulation results show our approach possess high searching ability and excellent convergence performance.
Keywords :
genetic algorithms; travelling salesman problems; genetic algorithm; hybrid ant colony algorithm; optimal state transition rule; scale compression; suboptimal solution set; Ant colony optimization; Cities and towns; Computer science; Cybernetics; Evolutionary computation; Genetic algorithms; Iterative algorithms; Large-scale systems; Machine learning; Machine learning algorithms; Ant Colony; Intersection; Scale compression; State transition rule; TSP;
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
DOI :
10.1109/ICMLC.2007.4370267