1-D DC Resistivity Modeling and Interpretation in Anisotropic Media Using Particle Swarm Optimization

Pekşen, Ertan; Yas, Türker; Kıyak, Alper

doi:10.1007/s00024-014-0802-2

Cited by 39 publications

(22 citation statements)

References 33 publications

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“…Feng et al [223] employed orthogonal signal correction and PSO in order to detect wound infection by and improve the performance of electronic nose. Pekşen et al [224] proposed a PSO method for estimating the model parameters of a layered anisotropic earth model such as horizontal and vertical resistivities and thickness. The result was promising and the proposed method could be used for evaluating one-dimensional direct current data in anisotropic media.…”

Section: Electrical and Electronicmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

690

380

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Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

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Section: Electrical and Electronicmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

690

380

View full text Add to dashboard Cite

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“…Multiobjective global optimization methods with a probabilistic approach that has random distribution with intelligent algorithms, such as the genetic algorithm (Parolai et al, 2006;Dal Moro and Pipan, 2007;Picozzi and Albarello, 2007;Dal Moro, 2010;Boxberger et al, 2011;Akca et al, 2014;Kuo et al, 2016), and particle swarm optimization (Song et al, 2012;Peksen et al, 2014), have recently been favored to overcome the aforementioned difficulties. However, either combining the objective functions or giving subjective weighting to different objective functions may still cause a deceiving solution because sensitivities differ for RWD and HVSR curves.…”

Section: Introductionmentioning

confidence: 99%

Joint modeling of Rayleigh wave dispersion and H/V spectral ratio using Pareto-based multiobjective particle swarm optimization

Buyuk¹,

Zor²,

Karaman³

2020

Turkish J Earth Sci

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Estimating the shear wave velocity and thickness of geologic units in a sedimentary structure is important for quantifying the local site effect caused by an earthquake. Inversion of the Rayleigh wave dispersion alone is sensitive to the absolute average shear wave velocity, while the H/V spectral ratio is sensitive to velocity contrasts. The solution of these models in a joint system using conventional inversion techniques suffers from difficulties while evaluating partial derivatives, dependencies to the initial model that is sometimes difficult to estimate, and trapping at a local minimum. Herein, a joint model using the Pareto optimality technique with multiobjective particle swarm optimization was constructed as an effective global optimization method to decrease weakness and increase performance while estimating the depths and velocities. The presented approach is a multiobjective optimization method that does not require weighting, and allows evaluations of the individual objective function separately in a joint system. This study presents 2 synthetic joint system examples to account for a gradient-type and sharp velocity contrast cases, and an application from field data obtained from the Bursa Basin in Turkey. With these examples, an automated parameter search space was demonstrated to account for the abrupt velocity changes and a number of additional key points that are essential for a reasonable optimal solution.

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“…These parameters are vital for the success of the optimization and their selection depends on the nature of the problem under consideration. Therefore, parameter tuning studies should be performed before the parameter estimations performed by global optimization algorithms (Fernandez-Martinez et al, 2010;Pekşen et al, 2014;Ekinci et al, 2016Ekinci et al, , 2017Balkaya et al, 2017;Alkan and Balkaya, 2018) even though they are time-consuming (Eiben and Smith, 2011).…”

Section: Tuning and Parameter Estimations Through Synthetic Datamentioning

confidence: 99%

Parameter Estimations from Gravity and Magnetic Anomalies Due to Deep-Seated Faults: Differential Evolution versus Particle Swarm Optimization

2019

Turkish J Earth Sci

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Introduction Due to the mathematical nature of the gravity and magnetic methods in geophysics, numerous data processing techniques are easily performed to analyze their anomalies obtained from different types of investigations changing in a wide range of varieties. Based on the objectives of the investigations, the most commonly used techniques are generally separated into two different groups. The first group techniques, involving linear transformations, directional derivative-based techniques, image enhancement procedures, spectral methods, filtering operators, etc., generally aim at exploring and locating the geological source structures causing the potential field anomalies (

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1-D DC Resistivity Modeling and Interpretation in Anisotropic Media Using Particle Swarm Optimization

Cited by 39 publications

References 33 publications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Joint modeling of Rayleigh wave dispersion and H/V spectral ratio using Pareto-based multiobjective particle swarm optimization

Parameter Estimations from Gravity and Magnetic Anomalies Due to Deep-Seated Faults: Differential Evolution versus Particle Swarm Optimization

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