Abstract
Peer-to-peer (P2P) topology has a significant influence on the performance, search efficiency and functionality, and scalability of the application. In this paper, we investigate a multi-swarm approach to the problem of Neighbor Selection (NS) in P2P networks. Particle swarm optimization algorithm share some common characteristics with P2P in a dynamic social environment. Each particle encodes the upper half of the peer-connection matrix through the undirected graph, which reduces the search space dimension. The synergetic performance is achieved by the adjustment to the velocity influenced by the individual's cognition, the group cognition from multi-swarms, and the social cognition from the whole swarm. The performance of the proposed approach is evaluated and compared with two other different algorithms. The results indicate that it usually required shorter time to obtain better results than the other considered methods, specially for large scale problems.
| Original language | English (US) |
|---|---|
| Title of host publication | 5th International Conference on Soft Computing as Transdisciplinary Science and Technology, CSTST '08 - Proceedings |
| Pages | 178-184 |
| Number of pages | 7 |
| DOIs | |
| State | Published - Dec 1 2008 |
| Event | 5th International Conference on Soft Computing As Transdisciplinary Science and Technology, CSTST '08 - Cergy-Pontoise, France Duration: Oct 28 2008 → Oct 31 2008 |
Publication series
| Name | 5th International Conference on Soft Computing as Transdisciplinary Science and Technology, CSTST '08 - Proceedings |
|---|
Conference
| Conference | 5th International Conference on Soft Computing As Transdisciplinary Science and Technology, CSTST '08 |
|---|---|
| Country | France |
| City | Cergy-Pontoise |
| Period | 10/28/08 → 10/31/08 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Computational Theory and Mathematics
- Theoretical Computer Science
Cite this
}
A multi-swarm approach for neighbor selection in peer-to-peer networks. / Abraham, Ajith; Badr, Youakim; Liu, Hongbo; Grosan, Crina.
5th International Conference on Soft Computing as Transdisciplinary Science and Technology, CSTST '08 - Proceedings. 2008. p. 178-184 (5th International Conference on Soft Computing as Transdisciplinary Science and Technology, CSTST '08 - Proceedings).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - A multi-swarm approach for neighbor selection in peer-to-peer networks
AU - Abraham, Ajith
AU - Badr, Youakim
AU - Liu, Hongbo
AU - Grosan, Crina
PY - 2008/12/1
Y1 - 2008/12/1
N2 - Peer-to-peer (P2P) topology has a significant influence on the performance, search efficiency and functionality, and scalability of the application. In this paper, we investigate a multi-swarm approach to the problem of Neighbor Selection (NS) in P2P networks. Particle swarm optimization algorithm share some common characteristics with P2P in a dynamic social environment. Each particle encodes the upper half of the peer-connection matrix through the undirected graph, which reduces the search space dimension. The synergetic performance is achieved by the adjustment to the velocity influenced by the individual's cognition, the group cognition from multi-swarms, and the social cognition from the whole swarm. The performance of the proposed approach is evaluated and compared with two other different algorithms. The results indicate that it usually required shorter time to obtain better results than the other considered methods, specially for large scale problems.
AB - Peer-to-peer (P2P) topology has a significant influence on the performance, search efficiency and functionality, and scalability of the application. In this paper, we investigate a multi-swarm approach to the problem of Neighbor Selection (NS) in P2P networks. Particle swarm optimization algorithm share some common characteristics with P2P in a dynamic social environment. Each particle encodes the upper half of the peer-connection matrix through the undirected graph, which reduces the search space dimension. The synergetic performance is achieved by the adjustment to the velocity influenced by the individual's cognition, the group cognition from multi-swarms, and the social cognition from the whole swarm. The performance of the proposed approach is evaluated and compared with two other different algorithms. The results indicate that it usually required shorter time to obtain better results than the other considered methods, specially for large scale problems.
UR - http://www.scopus.com/inward/record.url?scp=77951615686&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77951615686&partnerID=8YFLogxK
U2 - 10.1145/1456223.1456263
DO - 10.1145/1456223.1456263
M3 - Conference contribution
AN - SCOPUS:77951615686
SN - 9781605580463
T3 - 5th International Conference on Soft Computing as Transdisciplinary Science and Technology, CSTST '08 - Proceedings
SP - 178
EP - 184
BT - 5th International Conference on Soft Computing as Transdisciplinary Science and Technology, CSTST '08 - Proceedings
ER -