Ant-like agents for load balancing in telecommunications networks
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Routing in telecommunications networks with ant-like agents
IATA '98 Proceedings of the second international workshop on Intelligent agents for telecommunication applications
Ad Hoc Wireless Networks: Protocols and Systems
Ad Hoc Wireless Networks: Protocols and Systems
ARA - The Ant-Colony Based Routing Algorithm for MANETs
ICPPW '02 Proceedings of the 2002 International Conference on Parallel Processing Workshops
Ant Agents for Hybrid Multipath Routing in Mobile Ad Hoc Networks
WONS '05 Proceedings of the Second Annual Conference on Wireless On-demand Network Systems and Services
Properties and mechanisms of self-organizing MANET and P2P systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Computer Networks: The International Journal of Computer and Telecommunications Networking
PACONET: imProved Ant Colony Optimization Routing Algorithm for Mobile Ad Hoc NETworks
AINA '08 Proceedings of the 22nd International Conference on Advanced Information Networking and Applications
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
A taxonomy of biologically inspired research in computer networking
Computer Networks: The International Journal of Computer and Telecommunications Networking
Smart data packet ad hoc routing protocol
Computer Networks: The International Journal of Computer and Telecommunications Networking
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Mobile ad-hoc networks (MANETs) consist of special kind of wireless mobile nodes which form a temporary network without using any infrastructure or centralized administration. MANETs can be used in wide range of future applications as they have the capability to establish networks at anytime, anywhere without aid of any established infrastructure. It is a challenging task to find most efficient routing due to the changing topology and the dynamic behavior of the nodes in MANET. It has been found that ant colony optimization (ACO) algorithms can give better results as they are having characterization of Swarm Intelligence (SI) which is highly suitable for finding the adaptive routing for such type of volatile network. ACO algorithms are inspired by a foraging behavior of group of ants which are able to find optimal path based upon some defined metric which is evaluated during the motion of ants. ACO routing algorithms use simple agents called artificial ants which establish optimum paths between source and destination that communicate indirectly with each other by means of stigmergy. Keeping in view of the above, in this paper we provide a taxonomy of various ant colony algorithms with advantages and disadvantages of each others with respect to various metrics.