An improved grey wolf optimizer algorithm for the inversion of geoelectrical data

Li, S.-Y.; Wang, S.-M.; Wang, P.-F.; Su, X.-L.; Zhang, X.-S.; Dong, Z.-H.

doi:10.1007/s11600-018-0148-8

Artykuł - szczegóły

Tytuł artykułu

An improved grey wolf optimizer algorithm for the inversion of geoelectrical data

Autorzy

Li S.-Y. , Wang S.-M. , Wang P.-F. , Su X.-L. , Zhang X.-S. , Dong Z.-H.

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-018-0148-8

Warianty tytułu

Języki publikacji

Abstrakty

The grey wolf optimizer (GWO) is a novel bionics algorithm inspired by the social rank and prey-seeking behaviors of grey wolves. The GWO algorithm is easy to implement because of its basic concept, simple formula, and small number of parameters. This paper develops a GWO algorithm with a nonlinear convergence factor and an adaptive location updating strategy and applies this improved grey wolf optimizer (improved grey wolf optimizer, IGWO) algorithm to geophysical inversion problems using magnetotelluric (MT), DC resistivity and induced polarization (IP) methods. Numerical tests in MATLAB 2010b for the forward modeling data and the observed data show that the IGWO algorithm can find the global minimum and rarely sinks to the local minima. For further study, inverted results using the IGWO are contrasted with particle swarm optimization (PSO) and the simulated annealing (SA) algorithm. The outcomes of the comparison reveal that the IGWO and PSO similarly perform better in counterpoising exploration and exploitation with a given number of iterations than the SA.

Słowa kluczowe

geoelectrical methods inversion grey wolf optimizer improved grey wolf optimizer geoelectrical

metody geoelektryczne inwersja

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2018

Tom

Vol. 66, no. 4

Strony

607--621

Opis fizyczny

Bibliogr. 44 poz.

Twórcy

autor

Li S.-Y.

Hubei Subsurface Multi-scale Imaging Key Laboratory, Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China

autor

Wang S.-M.

whwsm@hotmail.com

Hubei Subsurface Multi-scale Imaging Key Laboratory, Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China

autor

Wang P.-F.

Hubei Subsurface Multi-scale Imaging Key Laboratory, Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China

autor

Su X.-L.

Hubei Subsurface Multi-scale Imaging Key Laboratory, Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China

autor

Zhang X.-S.

State Key Laboratory of Biogeology and Environmental Geology, School of Earth Sciences, China University of Geosciences, Wuhan 430074, China

autor

Dong Z.-H.

Hubei Subsurface Multi-scale Imaging Key Laboratory, Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China

Bibliografia

1. Chahar V, Kumar D (2017) An astrophysics-inspired Grey wolf algorithm for numerical optimization and its application to engineering design problems. Adv Eng Softw 112:231–254
2. Chen S, Wang S, Zhang Y (2005) Ant colony optimization for the seismic nonlinear inversion/SEG technical program expanded abstracts 2005. Soc Explor Geophys 24(1):1732–1734
3. Davis P (1993) Levenberg-marquart methods and nonlinear estimation. Siam News 26(6):1–12
4. Dorigo M, Stützle T (2003) The ant colony optimization metaheuristic: algorithms, applications, and advances. In: Glover F, Kochenberger GA (eds) Handbook of metaheuristics. International series in operations research & management science, vol 57. Springer, Boston, MA, pp 250–285 https://doi.org/10.1007/0-306-48056-5_9
5. Dos Santos Coelho L, Alotto P (2008) Global optimization of electromagnetic devices using an exponential quantum-behaved particle swarm optimizer. IEEE Trans Magn 44(6):1074–1077
6. Dosso SE, Oldenburg DW (1991) Magnetotelluric appraisal using simulated annealing. Geophys J Int 106(2):379–385
7. Heidari AA, Pahlavani P (2017) An efficient modified grey wolf optimizer with Lévy flight for optimization tasks. Appl Soft Comput 60:115–134
8. Jadhav AN, Gomathi N (2017) WGC: hybridization of exponential grey wolf optimizer with whale optimization for data clustering. Alex Eng J. https://doi.org/10.1016/j.aej.2017.04.013
9. Kamboj VK, Bath SK, Dhillon JS (2016) Solution of non-convex economic load dispatch problem using Grey Wolf Optimizer. Neural Comput Appl 27(5):1301–1316
10. Krishnanand KN (2007) Glowworm swarm optimization: a multimodal function optimization paradigm with applications to multiple signal source localization tasks/2013 international conference on computing, networking and communications (ICNC). IEEE Comput Soc 2:600–605
11. Krishnanand KN, Ghose D (2006) Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multiagent Grid Syst 2(3):209–222
12. Lines LR, Schultz AK, Treitel S (1988) Cooperative inversion of geophysical data. Geophysics 53(3):8–20
13. Mikki S, Kishk AA (2005) Investigation of the quantum particle swarm optimization technique for electromagnetic applications/antennas and propagation society international symposium. IEEE 2:45–48
14. Mirjalili S (2015a) The ant lion optimizer. Adv Eng Softw 83:80–98
15. Mirjalili S (2015b) How effective is the Grey Wolf optimizer in training multi-layer perceptrons. Appl Intell 43(1):150–161
16. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
17. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513
18. Mittal N, Singh U, Sohi BS (2016) Modified grey wolf optimizer for global engineering optimization. Appl Comput Intell Soft Comput 2016:1–16
19. Mo X, Li X, Zhang Q (2016) The variation step adaptive Glowworm swarm optimization algorithm in optimum log interpretation for reservoir with complicated lithology. In: 12th international conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD), 2016. IEEE, 1044–1050
20. Mohamed AAA, El-Gaafary AAM, Mohamed YS et al. (2015) Design static VAR compensator controller using artificial neural network optimized by modify grey wolf optimization. In International joint conference on neural networks. IEEE, 1–7
21. Muangkote N, Sunat K, Chiewchanwattana S., 2014. An improved grey wolf optimizer for training q-Gaussian radial basis functional-link nets. In: Computer science and engineering conference. IEEE, 209–214
22. Muro C, Escobedo R, Spector L et al (2011) Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations. Behav Proc 88(3):192–197
23. Nabighian MN, Asten MW (2002) Metalliferous mining geophysics—state of the art in the last decade of the 20th century and the beginning of the new millennium. Geophysics 67(3):964–978
24. Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26:69–74
25. Parolai S, Picozzi M, Richwalski SM et al. (2005) Joint inversion of phase velocity dispersion and H/V ratio curves from seismic noise recordings using a genetic algorithm, considering higher modes. Geophys Res Lett 32(1):67–106
26. Raiche AP (1985) The joint use of coincident loop transient electromagnetic and Schlumberger sounding to resolve layered structures. Geophysics 50(10):1618–1627
27. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248
28. Sen MK, Stoffa PL (1992) Rapid sampling of model space using genetic algorithms: examples from seismic waveform inversion. Geophys J Int 108(1):281–292
29. Shaw R, Srivastava S (2007) Particle swarm optimization: a new tool to invert geophysical data. Geophysics 72(2):F75–F83
30. Shi XM, Wang JY, Zhang SY et al (2000) Multiscale genetic algorithm and its application in magnetotelluric sounding data inversion. Chin J Geophys Chin Ed 43(1):122–130
31. Simpson F, Bahr K (2005) Practical magnetotellurics. Cambridge University Press, Cambridge
32. Smith ML, Franklin JN (1969) Geophysical application of generalized inverse theory. J Geophys Res 74(10):2783–2785
33. Song X, Tang L, Zhao S et al (2015) Grey wolf optimizer for parameter estimation in surface waves. Soil Dyn Earthq Eng 75:147–157
34. Sulaiman MH, Mustaffa Z, Mohamed MR et al (2015) Using the gray wolf optimizer for solving optimal reactive power dispatch problem. Appl Soft Comput 32:286–292
35. Vozoff K, Jupp DLB (1975) Joint inversion of geophysical data. Geophys J Roy Astron Soc 42(3):977–991
36. Wang J, Tan Y (2005) 2-D MT inversion using genetic algorithm. J Phys Conf Ser 12(1):165 (IOP Publishing)
37. Wang S, Liu Y, Wang J (2009) Lecture on non-linear inverse methods in geophysical data (9)—ant colony optimization. Chin J Eng Geophys 6(2):131–136
38. Wang R, Yin C, Wang M et al (2012) Simulated annealing for controlled-source audio-frequency magnetotelluric data inversion. Geophysics 77(2):E127–E133
39. Yang XS (2010) A new metaheuristic bat-inspired algorithm, nature inspired cooperative strategies for optimization (NISCO2010), vol 284. Springer, Berlin, pp 65–74
40. Yang XS (2011) Bat algorithm for multi-objective optimisation. Int J Bio Inspired Comput 3(5):267–274
41. Yang XS, Hossein Gandomi A (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483
42. Yang H, Wang JL, Wu JS et al (2002) Constrained joint inversion of magneto-telluric and seismic data using simulated annealing algorithm. Chin J Geophys 45(5):764–776
43. Zhdanov MS (2010) Electromagnetic geophysics: notes from the past and the road ahead. Geophysics 75(5):75A49–75A66
44. Zhu A, Xu C, Li Z et al (2015) Hybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC. J Syst Eng Electron 26(2):317–328

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Bibliografia

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