A hybrid method for geological and geophysical data with multi-peak distributions using the PSO-GRG algorithm

To allow peak searching and parameter estimation for geological and geophysical data with multi-peak distributions, we explore a hybrid method based on a combination of the particle swarm optimization (PSO) and generalized reduced gradient (GRG) algorithms. After characterizing peaks using the addit...

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Vydané v:Journal of geophysics and engineering Ročník 12; číslo 3; s. 283 - 291
Hlavní autori: Ge, Xinmin, Fan, Yiren, Cao, Yingchang, Wang, Yang, Cong, Yunhai, Liu, Lailei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London IOP Publishing 01.06.2015
Oxford University Press
Predmet:
Algorithms
backscattering electron image
Convergence
geological and geophysical data
Geology
Geophysics
global convergence
multi-peak distribution
Nonlinearity
Objective function
PSO-GRG
Sandstone
Searching
Simulated annealing
Swarm intelligence
geological and geophysical data
multi-peak distribution
backscattering electron image
global convergence
PSO–GRG
ISSN:1742-2132, 1742-2140
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Popis
Shrnutí:To allow peak searching and parameter estimation for geological and geophysical data with multi-peak distributions, we explore a hybrid method based on a combination of the particle swarm optimization (PSO) and generalized reduced gradient (GRG) algorithms. After characterizing peaks using the additive Gaussian function, a nonlinear objective function is established, which transforms our task into a search for optimal solutions. In this process, PSO is used to obtain the initial values, aiming for global convergence, while GRG is subsequently implemented for higher stability. Iterations are stopped when the convergence criteria are satisfied. Finally, grayscale histograms of backscattering electron images of sandstone show that the proposed algorithm performs much better than other methods such as PSO, GRG, simulated annealing and differential evolution, achieving a faster convergence speed and minimal variances.
Bibliografia:JGE-100313.R1
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SourceType-Scholarly Journals-1
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ISSN:1742-2132
1742-2140
DOI:10.1088/1742-2132/12/3/283