Name
Title | ||
|---|---|---|
Using synchronous and asynchronous parallel Differential Evolution for calibrating a second-order traffic flow model. |
Abstract | ||
|---|---|---|
•A synchronous and asynchronous parallel Differential Evolution algorithm is presented.•Two Artificial Neural Networks are used as surrogate models.•The algorithm is used to calibrate a second-order traffic flow model.•The synchronous and asynchronous versions are compared.•The results demonstrate the accuracy and efficiency of the procedure. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.advengsoft.2018.08.011 | Advances in Engineering Software |
Keywords | Field | DocType |
Parallel Differential Evolution,Synchronous implementation,Asynchronous implementation,Surrogate models,Artificial Neural Networks,Macroscopic traffic flow modeling | Mathematical optimization,Radial basis function network,Traffic flow,Computer science,Algorithm,Microscopic traffic flow model,Differential evolution,Message Passing Interface,Artificial neural network,Perceptron,Constrained optimization | Journal |
Volume | ISSN | Citations |
125 | 0965-9978 | 2 |
PageRank | References | Authors |
0.40 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giorgos A. Strofylas | 1 | 4 | 0.78 |
| Kallirroi N. Porfyri | 2 | 2 | 0.74 |
| Ioannis K. Nikolos | 3 | 18 | 5.81 |
| Anargiros I. Delis | 4 | 2 | 1.08 |
| Markos Papageorgiou | 5 | 653 | 165.20 |