Name
Abstract | ||
|---|---|---|
This paper describes a thermodynamic particle swarm optimizer (TDPSO) based on the simple evolutionary equations. Inspired by the minimum free energy principle of the thermodynamic theoretics, a rating-based entropy (RE) and a component thermodynamic replacement (CTR) rule are implemented in the novel algorithm TDPSO. The concept of RE is utilized to systemically measure the fitness dispersal of the swarm with low computational cost. And the fitness range of all particles is divided into several ranks. Furthermore, the rule CTR is applied to control the optimal process with steeply fast convergence speed. It has the potential to maintain population diversity. Compared with the other improved PSO techniques, experimental results on some typical minimization problems show that the proposed technique outperforms other algorithms in terms of convergence speed and stability. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-89694-4_29 | SEAL |
Keywords | Field | DocType |
convergence speed,fitness range,component thermodynamic replacement,hybrid particle swarm optimization,improved pso technique,thermodynamic theoretics,thermodynamic mechanism,rule ctr,thermodynamic particle swarm optimizer,fitness dispersal,novel algorithm tdpso,thermodynamics | Particle swarm optimization,Convergence (routing),Mathematical optimization,Swarm behaviour,Computer science,Multi-swarm optimization,Population diversity,Minification,Particle swarm optimizer,Minimum free energy | Conference |
Volume | ISSN | Citations |
5361 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Wu | 1 | 420 | 63.58 |
| Yuanxiang Li | 2 | 245 | 51.20 |
| Xing Xu | 3 | 20 | 4.05 |
| Sheng Peng | 4 | 0 | 0.34 |