Name
Abstract | ||
|---|---|---|
Particle swarm optimization (PSO) is an evolutionary algorithm used extensively. This paper presented a new particle swarm optimizer based on stochastic evolutionary dynamics (SED-PSO). The stochastic evolutionary dynamics is used to speed up the researching process of the particles because stochastic factor plays a very important role in the researching process of optimal algorithm. Each particle in the swarm is also associated with a process of reproduction. We use a stochastic process with frequency dependent fitness to deal with the reproduction process. Experiments results show that SED-PSO algorithm has great performance of convergence property over traditional PSO in terms of iteration with only a slight precision dropdown. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICNC.2008.103 | ICNC |
Keywords | Field | DocType |
traditional pso,evolutionary algorithm,stochastic processes,evolutionary computation,new particle swarm optimizer,particle swarm optimisation,convergence property,particle swarm optimization,stochastic factor,stochastic evolutionary dynamic,stochastic process,stochastic evolutionary dynamics,sed-pso algorithm,particle swarm algorithm,optimal algorithm,reproduction process,convergence,particle swarm,evolutionary dynamics,artificial neural networks | Particle swarm optimization,Mathematical optimization,Evolutionary algorithm,Swarm behaviour,Computer science,Evolutionary computation,Stochastic process,Multi-swarm optimization,Artificial intelligence,Evolutionary dynamics,Artificial neural network,Machine learning | Conference |
Volume | ISBN | Citations |
7 | 978-0-7695-3304-9 | 0 |
PageRank | References | Authors |
0.34 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhi-jie Li | 1 | 5 | 1.26 |
| Xiang-dong Liu | 2 | 11 | 1.83 |
| Duan Xiaodong | 3 | 85 | 16.18 |