Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The ant colony optimization meta-heuristic
New ideas in optimization
Search and replication in unstructured peer-to-peer networks
ICS '02 Proceedings of the 16th international conference on Supercomputing
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Modern Information Retrieval
Anthill: A Framework for the Development of Agent-Based Peer-to-Peer Systems
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
[15] Peer-to-Peer Architecture Case Study: Gnutella Network
P2P '01 Proceedings of the First International Conference on Peer-to-Peer Computing
Ant Algorithms for Search in Unstructured Peer-to-Peer Networks
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Messor: load-balancing through a swarm of autonomous agents
AP2PC'02 Proceedings of the 1st international conference on Agents and peer-to-peer computing
Proposal for self-organizing information distribution in peer-to-peer networks
IWSOS'06/EuroNGI'06 Proceedings of the First international conference, and Proceedings of the Third international conference on New Trends in Network Architectures and Services conference on Self-Organising Systems
A pheromone-based coordination mechanism applied in peer-to-peer
AP2PC'03 Proceedings of the Second international conference on Agents and Peer-to-Peer Computing
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Using taxonomies for content-based routing with ants
Computer Networks: The International Journal of Computer and Telecommunications Networking
Self-adaptive and reconfigurable distributed computing systems
Applied Soft Computing
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This paper presents the design and evaluation of an ant-based approach to query routing in peer-to-peer networks. After pointing out how to employ the ant metaphor in peer-to-peer networks, we conduct a thorough evaluation of the impact of different settings for the configurable parameters present in ant algorithms on the performance values. In particular, the focus is on the effects of setting the ratio between (1) ants exploiting the option currently known as the best one and (2) ants exploring the search space with the aim of finding improved options. We show that the exploitation-exploration dilemma can be avoided by an adequate design of the exploring option.