Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
The ant colony optimization meta-heuristic
New ideas in optimization
Ant algorithms for discrete optimization
Artificial Life
Ant Colony Optimization
Ants and reinforcement learning: a case study in routing in dynamic networks
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An overview of cooperative and competitive multiagent learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
| Hi-index | 0.00 |
Backup trees (BTs) are a promising approach to network protection in optical networks. BTs allow us to protect a group of working paths against single network failures, while reserving only a minimum amount of network capacity for backup purposes. The process of constructing a set of working paths together with a backup tree is computationally very expensive, however. In this paper we propose a multi-agent approach based on ant colony optimization (ACO) for solving this problem. ACO algorithms use a set of relatively simple agents that model the behavior of real ants. In our algorithm multiple types of ants are used. Ants of the same type collaborate, but are in competition with the ants of other types. The idea is to let each type find a path in the network that is disjoint with that of other types. We also demonstrate a preliminary version of this algorithm in a series of simple experiments.