Ant-based load balancing in telecommunications networks
Adaptive Behavior
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
An Ant Colony Optimization Approach to the Probabilistic Traveling Salesman Problem
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Ad-hoc On-Demand Distance Vector Routing
WMCSA '99 Proceedings of the Second IEEE Workshop on Mobile Computer Systems and Applications
Ant-routing-algorithm for mobile multi-hop ad-hoc networks
Network control and engineering for Qos, security and mobility II
Ad Hoc Wireless Networks: Architectures and Protocols
Ad Hoc Wireless Networks: Architectures and Protocols
Computer and Communication Networks
Computer and Communication Networks
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
IEEE Computational Intelligence Magazine
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Ant algorithms and swarm intelligence systems have been offered as a novel computational approach that replaces the traditional emphasis on control, preprogramming and centralization with designs featuring autonomy, emergence and distributed functioning. These designs provide scalable, flexible and robust, able to adapt quickly changes to changing environments and to continue functioning even when individual elements fail. These properties make swarm intelligence very attractive for mobile ad hoc networks. These algorithms also provide potential advantages for conventional routing algorithms. Ant Colony Optimization is popular among other Swarm Intelligence Techniques.In this paper a detailed comparison of different Ant based algorithms is presented. The comparative results will help the researchers to understand the basic differences among various existing Ant colony based routing algorithms.