Name
Abstract | ||
|---|---|---|
This paper studies a new version of growing particle swarm optimizers. In the algorithm, a new particle is born if a particle exploring the optimum is stagnated and the swarm can grow depending on problem complexity. The particle velocity is controlled by an acceleration parameter that can attenuate depending on the number of particles and can vibrate depending on the time. The parameter plays important role to reduce the computation cost and to increase the success rate. The algorithm efficiency is confirmed by numerical experiments of typical benchmarks. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-10684-2_26 | ICONIP |
Keywords | Field | DocType |
numerical experiment,important role,particle swarm optimizers,paper study,particle velocity,population-dependent parameter,new version,computation cost,algorithm efficiency,new particle,acceleration parameter | Particle swarm optimization,Particle number,Population,Mathematical optimization,Swarm behaviour,Particle velocity,Computer science,Multi-swarm optimization,Acceleration,Artificial intelligence,Machine learning,Particle | Conference |
Volume | ISSN | Citations |
5864 | 0302-9743 | 2 |
PageRank | References | Authors |
0.40 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chihiro Kurosu | 1 | 2 | 0.40 |
| Toshimichi Saito | 2 | 382 | 74.54 |
| Kenya Jin'No | 3 | 45 | 12.55 |