Application of artificial bee colony algorithm on surface wave data. (October 2015)

• Record Type: Journal Article
• Authors: Song, Xianhai; Gu, Hanming; Tang, Li; Zhao, Sutao; Zhang, Xueqiang; Li, Lei; Huang, Jianquan
• Is Part Of: Computers & geosciences. Volume 83(2015)
• Page Start: 219
• Page End: 230
• Publication Date: 2015-10
• Subjects: Artificial bee colony algorithm -- Genetic algorithm -- Particle swarm optimization -- Surface waves -- Dispersion curves; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2015.07.010
• Languages: English
• ISSNs: 0098-3004

Abstract

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. Highlights: Artificial bee colony (ABC) algorithm is applied to invert surface wave data. The ABC algorithm is benchmarked on noise-free, noisy and observed data. The performance of ABC is compared to those of the standard GA and the basic PSO. Results show ABC can be effectively used to invert surface wave dispersion data.