Title :
An Improved Ant Colony Algorithm
Author :
Zhang Xin ; Zhou Yu-zhong ; Fang Ping
Author_Institution :
Dept. of Mathematic, South China Agriultural Univ., Guangzhou
Abstract :
Artificial ant colony algorithm is new in the evolution computing. The primary study shows it is a better algorithm with robust based population, but it has some shortcomings such as its slow computing speed, and it is easy to fall in local peak in large scale problem. To overcome these deficiencies, an improved ant colony algorithm is designed through abstracting the advantages of particle swarm optimization (PSO).
Keywords :
evolutionary computation; particle swarm optimisation; artificial ant colony algorithm; evolution computing; particle swarm optimization; Ant colony optimization; Cities and towns; Educational institutions; Finishing; Information technology; Mathematics; Multimedia computing; Particle swarm optimization; Robustness; Traveling salesman problems; Ant colony algorithmx; global optimization; local optimum; particle swarm optimination;
Conference_Titel :
MultiMedia and Information Technology, 2008. MMIT '08. International Conference on
Conference_Location :
Three Gorges
Print_ISBN :
978-0-7695-3556-2
DOI :
10.1109/MMIT.2008.157
