Name
Title | ||
|---|---|---|
Density estimation using a new dimension adaptive particle swarm optimization algorithm |
Abstract | ||
|---|---|---|
Current Particle Swarm Optimization (PSO) algorithms do not address problems with unknown dimensions, which arise in many
applications that would benefit from the use of PSO. In this paper, we propose a new algorithm, called Dimension Adaptive
Particle Swarm Optimization (DA-PSO) that can address problems with any number of dimensions. We also propose and compare
three other PSO-based methods with DA-PSO. We apply our algorithms to solve the Weibull mixture model density estimation problem
as an illustration. DA-PSO achieves better objective function values than other PSO-based algorithms on four simulated datasets
and a real dataset. We also compare DA-PSO with the recursive Expectation-Maximization (EM) estimator, which is a non-PSO-based
method, obtaining again very good results. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/s11721-009-0032-x | Swarm intelligence |
Keywords | Field | DocType |
Particle swarm optimization,Distribution estimation,Weibull mixture model | Density estimation,Computer science,Weibull distribution,Artificial intelligence,Imperialist competitive algorithm,Metaheuristic,Particle swarm optimization,Mathematical optimization,Algorithm,Multi-swarm optimization,Mixture model,Machine learning,Estimator | Journal |
Volume | Issue | Citations |
3 | 4 | 4 |
PageRank | References | Authors |
0.45 | 14 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanjun Yan | 1 | 30 | 9.73 |
| Lisa Ann Osadciw | 2 | 158 | 16.56 |