Name
Abstract | ||
|---|---|---|
Artificial bee colony (ABC) algorithm is applied to invert surface wave phase velocities. The ABC algorithm, one of swarm intelligence-based algorithms, is inspired from the particular intelligent foraging behavior of a honeybee swarm in nature. To facilitate convergence to an optimal solution, global exploration and local exploitation processes are carried out simultaneously in a robust ABC search process. Using synthetic and observed Rayleigh wave data, we examined the effectiveness and applicability of the ABC scheme in deducing an S-wave velocity profile for near-surface applications. Furthermore, we compared the performance of ABC to those of genetic algorithm (GA) and particle swarm optimization (PSO). We demonstrated that the ABC algorithm outperforms the standard binary-coded GA and the basic PSO, and it can be effectively used to interpret surface wave dispersion data with the great advantage of employing fewer control parameters. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.cageo.2015.07.010 | Computers & Geosciences |
Keywords | Field | DocType |
Artificial bee colony algorithm,Genetic algorithm,Particle swarm optimization,Surface waves,Dispersion curves | Particle swarm optimization,Convergence (routing),Artificial bee colony algorithm,Mathematical optimization,Rayleigh wave,Swarm behaviour,Computer science,Swarm intelligence,Surface wave,Genetic algorithm | Journal |
Volume | Issue | ISSN |
83 | C | 0098-3004 |
Citations | PageRank | References |
3 | 0.40 | 10 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianhai Song | 1 | 12 | 2.05 |
| Hanming Gu | 2 | 3 | 2.09 |
| Tang Li | 3 | 60 | 6.04 |
| Sutao Zhao | 4 | 3 | 0.40 |
| Xueqiang Zhang | 5 | 26 | 3.87 |
| Lei Li | 6 | 3 | 2.77 |
| Jianquan Huang | 7 | 4 | 1.15 |