Title :
Time Ant Colony Algorithm with Genetic Algorithms
Author :
Zuo, Hong-hao ; Xiong, Fan-lun
Author_Institution :
Inst. of Intelligent Machines, Chinese Acad. of Sci., Beijing
Abstract :
Time ant colony algorithm has good effect on combinatorial optimization problems as well as that of the ant colony algorithm while it has the shortcoming of long convergence time. A new method combined with genetic algorithms is proposed. Firstly a genetic algorithms procedure is used to solve the problem in specifying time. Secondly the solution having gotten is used to distribute the original pheromone. At the last the time ant colony algorithm is used to search the optimal solution, which supposed that each ant´s velocity is the same and all ants are crawling in full time. The new method accelerates the convergence speed. It is testified by the experiment that the novel algorithm is better than before
Keywords :
computational complexity; convergence; genetic algorithms; combinatorial optimization problems; convergence time; genetic algorithms; time ant colony algorithm; Acceleration; Ant colony optimization; Cities and towns; Convergence; Feedback; Genetic algorithms; Helium; Scheduling algorithm; Testing; Traveling salesman problems; Time ant colony algorithm; genetic algorithms; traveling salesman problem;
Conference_Titel :
Information Acquisition, 2006 IEEE International Conference on
Conference_Location :
Weihai
Print_ISBN :
1-4244-0528-9
Electronic_ISBN :
1-4244-0529-7
DOI :
10.1109/ICIA.2006.305886