Title :
Pheromone-based ant colony clustering algorithm in multi-agent cooperation
Author :
Tang Xian-lun ; Li Ya-nan ; Fan Zheng ; Wang Guan-xiang ; Cai Lin-qing
Author_Institution :
Key Lab. of Network Control & Intell. Instrum., Chongqing Univ. of Posts & Telecommun., Chongqing, China
Abstract :
Cooperation is a key issue of multi-agent system (MAS), pursuit problem as a test platform of multi-agent system and learning algorithm is widely used for testing the performance of cooperation, learning method and communication of MAS. To reduce the large computation of current pursuit methods such as the shortest distance first and contract net protocol, a method of applying pheromone-based ant colony clustering algorithm in multi-agent pursuit problem is proposed. Experiment results show that the proposed method can speed up the pursuit process compared with other algorithms, when increasing the number of predators the proposed method shows its superiority on efficiency and can be applied in multi-agent cooperation effectively.
Keywords :
learning (artificial intelligence); multi-agent systems; optimisation; pattern clustering; contract net protocol; learning algorithm; multiagent cooperation; multiagent system; pheromone-based ant colony clustering algorithm; pursuit process; shortest distance first; Algorithm design and analysis; Automation; Clustering algorithms; Conferences; Contracts; Java; Multiagent systems; ant colony clustering; cooperation; multi-agent; pursuit;
Conference_Titel :
Electric Information and Control Engineering (ICEICE), 2011 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-8036-4
DOI :
10.1109/ICEICE.2011.5778228