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Geotechnical and Geological Engineering

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04.09.2017 | State-of-the-Art Review

Applications of Particle Swarm Optimization in Geotechnical Engineering: A Comprehensive Review

verfasst von: M. Hajihassani, D. Jahed Armaghani, R. Kalatehjari

Erschienen in: Geotechnical and Geological Engineering | Ausgabe 2/2018

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Abstract

Particle swarm optimization (PSO) is an evolutionary computation approach to solve nonlinear global optimization problems. The PSO idea was made based on simulation of a simplified social system, the graceful but unpredictable choreography of birds flock. This system is initialized with a population of random solutions that are updated during iterations. Over the last few years, PSO has been extensively applied in various geotechnical engineering aspects such as slope stability analysis, pile and foundation engineering, rock and soil mechanics, and tunneling and underground space design. A review on the literature shows that PSO has utilized more widely in geotechnical engineering compared with other civil engineering disciplines. This is due to comprehensive uncertainty and complexity of problems in geotechnical engineering which can be solved by using the PSO abilities in solving the complex and multi-dimensional problems. This paper provides a comprehensive review on the applicability, advantages and limitation of PSO in different disciplines of geotechnical engineering to provide an insight to an alternative and superior optimization method compared with the conventional optimization techniques for geotechnical engineers.

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Annan J, Zhiwu W (2011) Optimizing supporting parameters of metro tunnel based on improved particle swarm optimization arithmetic. Procedia Eng 15:4857–4861. doi:10.1016/j.proeng.2011.08.906 CrossRef

Antoniou P, Pitsillides A, Blackwell T, Engelbrecht A (2009) Employing the flocking behavior of birds for controlling congestion in autonomous decentralized networks. In: 2009 IEEE congress on evolutionary computation, pp 1753–1761

Antoniou P, Pitsillides A, Blackwell T, Engelbrecht A, Michael L (2013) Congestion control in wireless sensor networks based on bird flocking behavior. Comput Netw 57(5):1167–1191. doi:10.1016/j.comnet.2012.12.008 CrossRef

Armaghani DJ, Hajihassani M, Yazdani Bejarbaneh B, Marto A, Mohamad ET (2014a) Indirect measure of shale shear strength parameters by means of rock index tests through an optimized artificial neural network. Measurement 55:487–498. doi:10.1016/j.measurement.2014.06.001 CrossRef

Armaghani DJ, Hajihassani M, Mohamad ET, Marto A, Noorani SA (2014b) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci 7(12):5383–5396. doi:10.1007/s12517-013-1174-0 CrossRef

Armaghani DJ, Raja RSNSB, Faizi K, Rashid ASA (2017a) Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Comput Appl 28(2):391–405CrossRef

Armaghani DJ, Mohamad ET, Narayanasamy MS, Narita N, Yagiz S (2017b) Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunn Undergr Space Technol 63:29–43CrossRef

Babanouri N, Nasab SK, Sarafrazi S (2013) A hybrid particle swarm optimization and multi-layer perceptron algorithm for bivariate fractal analysis of rock fractures roughness. Int J Rock Mech Min Sci 60:66–74. doi:10.1016/j.ijrmms.2012.12.028

Bolton H, Heymann G, Groenwold A (2003) Global search for critical failure surface in slope stability analysis. Eng Optim 35(1):51–65CrossRef

Chen Y, Wei X, Li Y (2006) Locating non-circular critical slip surfaces by particle swarm optimization algorithm. Chin J Rock Mech Eng 25(7):1443–1449

Chen J, Yang LR, Su MC (2009) Comparison of SOM-based optimization and particle swarm optimization for minimizing the construction time of a secant pile wall. Autom Constr 18(6):844–848. doi:10.1016/j.autcon.2009.03.008 CrossRef

Chen D, Zhao C, Zhang H (2011) An improved cooperative particle swarm optimization and its application. Neural Comput Appl 20(2):171–182. doi:10.1007/s00521-010-0503-4 CrossRef

Chen WW, Shen ZP, Wang JA, Tsai F (2015a) Scripting STABL with PSO for analysis of slope stability. Neurocomputing 148:167–174CrossRef

Chen WW, Shen ZP, Wang JA, Tsai F (2015b) Scripting STABL with PSO for analysis of slope stability. Neurocomputing 148:167–174CrossRef

Cheng YM, Liu HT, Wei WB, Au SK (2005) Location of critical three-dimensional non-spherical failure surface by NURBS functions ellipsoid with applications to highway slopes. Comput Geotech 32(6):387–399. doi:10.1016/j.compgeo.2005.07.004 CrossRef

Cheng YM, Li L, Chi SC (2007) Performance studies on six heuristic global optimization methods in the location of critical slip surface. Comput Geotech 34(6):462–484. doi:10.1016/j.compgeo.2007.01.004 CrossRef

Cheng YM, Li DZ, Li L, Sun YJ, Baker R, Yang Y (2011) Limit equilibrium method based on an approximate lower bound method with a variable factor of safety that can consider residual strength. Comput Geotech 38(5):623–637. doi:10.1016/j.compgeo.2011.02.010 CrossRef

Cheng YM, Li L, Sun YJ, Au SK (2012) A coupled particle swarm harmony search optimization algorithm for difficult geotechnical problems. Struct Multidiscip Optim 45(4):489–501. doi:10.1007/s00158-011-0694-z CrossRef

Choobbasti AJ, Tavakoli H, Kutanaei SS (2014) Modeling optimization of a trench layer location around a pipeline using artificial neural networks particle swarm optimization algorithm. Tunn Undergr Space Technol 40:192–202. doi:10.1016/j.tust.2013.10.003 CrossRef

Ciuprina G, Ioan D, Munteanu I (2002) Use of intelligent particle swarm optimization in electromagnetics. IEEE Trans Magn 38:1037–1040CrossRef

Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73CrossRef

Cui Z, Gao X (2012) Theory applications of swarm intelligence. Neural Comput Appl 21(2):205–206. doi:10.1007/s00521-011-0523-8 CrossRef

Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theoret Comput Sci 344(2):243–278. doi:10.1016/j.tcs.2005.05.020 CrossRef

Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceeding of the Sixth International Symposium on Micro Machine Human Science, IEEE, Nagoya, Japan, pp 39–43

Eberhart RC, Shi Y (2000) Comparing inertia weights constriction factors in particle swarm optimization. In: Proceeding of Congress Evolutionary Computation, NJ: IEEE Press, pp 84–88

Engelbrecht AP (2007) Computational intelligence: an introduction. Wiley, LondonCrossRef

Feng XT, Chen BR, Yang C, Zhou H, Ding X (2006) Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm. Int J Rock Mech Min Sci 43(5):789–801. doi:10.1016/j.ijrmms.2005.12.010 CrossRef

Fontan M, Ndiaye A, Breysse D, Bos F, Fernez C (2011) Soil-structure interaction: Parameters identification using particle swarm optimization. Comput Struct 89(17):1602–1614. doi:10.1016/j.compstruc.2011.05.002 CrossRef

Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845CrossRef

Gholizadeh S, Salajegheh E (2009) Optimal design of structures subjected to time history loading by swarm intelligence an advanced metamodel. Comput Methods Appl Mech Eng 198(37):2936–2949. doi:10.1016/j.cma.2009.04.010 CrossRef

Gordan B, Armaghani DJ, Hajihassani M, Monjezi M (2016) Prediction of seismic slope stability through combination of particle swarm optimization neural network. Eng Comput 32:85–97CrossRef

Hajihassani M (2013) Tunneling-induced ground movement and building damage prediction using hybrid artificial neural networks. Dissertation, Universiti Teknologi Malaysia

Hajihassani M, Armaghani DJ, Sohaei H, Mohamad ET, Marto A (2014) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network particle swarm optimization. Appl Acoust 80:57–67. doi:10.1016/j.apacoust.2014.01.005 CrossRef

Hajihassani M, Armaghani DJ, Monjezi M, Mohamad ET, Marto A (2015) Blast-induced air ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environ Earth Sci 74(4):2799–2817. doi:10.1007/s12665-015-4274-1 CrossRef

Hasanipanah M, Noorian-Bidgoli M, Armaghani DJ, Khamesi H (2016) Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng Comput 32(4):705–715CrossRef

Hasanipanah M, Shahnazar A, Amnieh HB, Armaghani DJ (2017) Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model. Eng Comput 33(1):23–31CrossRef

Hossain MS, El-Shafie A (2014) Evolutionary techniques versus swarm intelligences: application in reservoir release optimization. Neural Comput Appl 24(7):1583–1594. doi:10.1007/s00521-013-1389-8 CrossRef

Ismail A, Jeng DS (2012) Empirical method for settlement prediction of single piles using higher order neural network and particle swarm optimization. In: Geo Congress 2012, State of the Art Practice in Geotechnical Engineering, pp 285–294

Ismail A, Jeng DS, Zhang LL (2013) An optimised product-unit neural network with a novel PSO–BP hybrid training algorithm: applications to load-deformation analysis of axially loaded piles. Eng Appl Artif Intell 26(10):2305–2314. doi:10.1016/j.engappai.2013.04.007 CrossRef

Jiang JC, Yamagami T, Baker R (2003) Three-dimensional slope stability analysis based on nonlinear failure envelope. Chin J Rock Mech Eng 22:1017–1023

Jiang AN, Wang SY, Tang SL (2011) Feedback analysis of tunnel construction using a hybrid arithmetic based on support vector machine and particle swarm optimisation. Autom Constr 20(4):482–489. doi:10.1016/j.autcon.2010.11.016 CrossRef

Kalatehjari R (2013) An improvised three-dimensional slope stability analysis based on limit equilibrium method by using particle swarm optimization. Dissertation, Universiti Teknologi Malaysia

Kalatehjari R, Ali N (2013) A review of three-dimensional slope stability analyses based on limit equilibrium method. Electron J Geotech Eng 18:119–134

Kalatehjari R, Ali N, Hajihassani M, Kholghi Fard M (2012) The application of particle swarm optimization in slope stability analysis of homogeneous soil slopes. Int Rev Model Simul 5(1):458–465

Kalatehjari R, Ali N, Kholghifard M, Hajihassani M (2014) The effects of method of generating circular slip surfaces on determining the critical slip surface by particle swarm optimization. Arab J Geosci 7(4):1529–1539. doi:10.1007/s12517-013-0922-5 CrossRef

Kang F, Li JJ, Hu J (2006) Combined forecasting model for slope stability based on support vector machines with particle swarm optimization. Rock Soil Mech 27:648–652

Karaboga D, Basturk B (2007) A powerful efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471CrossRef

Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697. doi:10.1016/j.asoc.2007.05.007 CrossRef

Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceeding of the IEEE international conference on neural networks, Perth, Australia, pp 1942–1948

Khajehzadeh M, El-Shafie A, Taha MR (2010) Modified particle swarm optimization for probabilistic slope stability analysis. Int J Phys Sci 5(15):2248–2258

Khajehzadeh M, Taha MR, El-Shafie A (2011) Reliability analysis of earth slopes using hybrid chaotic particle swarm optimization. J Cent South Univ Technol 18(5):1626–1637. doi:10.1007/s11771-011-0882-4 CrossRef

Kitagawa S, Takenaka M, Fukuyama Y (2004) Recent optimization techniques and applications to customer solutions. Fuji Electric J 77(2):137–141

Kuok KK, Harun S, Shamsuddin SM (2010) Particle swarm optimization feedforward neural network for modeling runoff. Int J Environ Sci Technol 7(1):67–78. doi:10.1007/BF03326118 CrossRef

Li L, Chu XS (2011) An improved particle swarm optimization algorithm with harmony strategy for the location of critical slip surface of slopes. China Ocean Eng 25:357–364. doi:10.1007/s13344-011-0030-9 CrossRef

Li XL, Qian JX (2003) Studies on artificial fish swarm optimization algorithm based on decomposition and coordination techniques. J Circuits Syst 1:1–6

Li L, Chi SC, Lin G (2005) Improved complex method based on particle swarm optimization algorithm and its application to slope stability analysis. Rock Soil Mech 26(9):1393

Li L, Chi SC, Zheng YM (2008) Three-dimensional slope stability analysis based on ellipsoidal sliding body and simplified JANBU method. Rock Soil Mech 29(9):2439–2445

Li L, Yu G, Chu X, Lu S (2009) The harmony search algorithm in combination with particle swarm optimization and its application in the slope stability analysis. In: Proceeding of the international conference on computational intelligence and security, Beijing, China, pp 133–136

Li L, Yu GM, Chen ZY, Chu XS (2010) Discontinuous flying particle swarm optimization algorithm and its application to slope stability analysis. J Cent South Univ Technol 17:852–856. doi:10.1007/s11771-010-0566-5 CrossRef

Li H, Zhong H, Yan Z, Zhang X (2012) Particle swarm optimization algorithm coupled with finite element limit equilibrium method for geotechnical practices. Math Prob Eng. Article number 498690

Liou SW, Wang CM, Huang YF (2009) Integrative discovery of multifaceted sequence patterns by frame-relayed search and hybrid PSO-ANN. J Univers Comput Sci 15(4):742–764

Lovbjerg M, Rasmussen TK, Krink T (2001) Hybrid particle swarm optimizer with breeding subpopulations. In: Proceeding of the third genetic and evolutionary computation conference, San Francisco, pp 469–476

Lü ZS, Hou ZR, Du J (2006) Particle swarm optimization with adaptive mutation. Front Electr Electron Eng China 1(1):99–104. doi:10.1007/s11460-005-0021-9 CrossRef

Mashhadban H, Kutanaei SS, Sayarinejad MA (2016) Prediction modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network. Constr Build Mater 119:277–287. doi:10.1016/j.conbuildmat.2016.05.034 CrossRef

Mira V, Fonseca N (2002) New evolutionary particle swarm algorithm applied to voltage/var control. In: 14th Power systems computation conference, pp 1–6

Mohamad ET, Armaghani DJ, Momeni E, Abad SVANK (2015) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull Eng Geol Env 74(3):745–757CrossRef

Mohamad ET, Armaghani DJ, Momeni E, Yazdavar AH, Ebrahimi M (2016) Rock strength estimation: a PSO-based BP approach. Neural Comput Appl. doi:10.1007/s00521-016-2728-3

Momeni E, Armaghani DJ, Hajihassani M, Amin MFM (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement 60:50–63CrossRef

Mowen XJE (2004) A simple Monte Carlo method for locating the three-dimensional critical slip surface of a slope. Acta Geol Sin (English Edition) 78(6):1258–1266CrossRef

Mowen X, Zengfu W, Xiangyu L, Bo X (2011) Three-dimensional critical slip surface locating and slope stability assessment for lava lobe of Unzen volcano. J Rock Mech Geotech Eng 3(1):82–89. doi:10.3724/SP.J.1235.2011.00082 CrossRef

Najafzadeh M, Tafarojnoruz A (2016) Evaluation of neuro-fuzzy GMDH-based particle swarm optimization to predict longitudinal dispersion coefficient in rivers. Environ Earth Sci 75(2):1–12. doi:10.1007/s12665-015-4877-6 CrossRef

Neshat M (2013) FAIPSO: fuzzy adaptive informed particle swarm optimization. Neural Comput Appl 23(1):95–116. doi:10.1007/s00521-012-1256-z CrossRef

Neshat M, Sepidnam G, Sargolzaei M (2013) Swallow swarm optimization algorithm: a new method to optimization. Neural Comput Appl 23(2):429–454. doi:10.1007/s00521-012-0939-9 CrossRef

Ni Q, Deng J (2013) A new logistic dynamic particle swarm optimization algorithm based on rom topology. Sci World J. doi:10.1155/2013/409167

Poitras G, Lefrançois G, Cormier G (2011) Optimization of steel floor systems using particle swarm optimization. J Constr Steel Res 67(8):1225–1231. doi:10.1016/j.jcsr.2011.02.016 CrossRef

Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57. doi:10.1007/s11721-007-0002-0 CrossRef

Reynolds CW (1987) Flocks, herds and schools: a distributed behavioral model. ACM SIGGRAPH Comput Gr 21(4):25–34CrossRef

Robinson J, Sinton S, Rahmat-Samii Y (2002) Particle swarm, genetic algorithm, their hybrids: optimization of a profiled corrugated horn antenna. In: 2002 IEEE Antennas Propagation Society International Symposium, pp 314–317

Sadoghi Yazdi J, Kalantary F, Sadoghi Yazdi H (2011) Calibration of soil model parameters using particle swarm optimization. Int J Geomech 12(3):229–238. doi:10.1061/(ASCE)GM.1943-5622.0000142 CrossRef

Sharma LK, Singh TN (2017) Regression-based models for the prediction of unconfined compressive strength of artificially structured soil. In: Engineering with Computers, pp 1–12

Sharma LK, Singh R, Umrao RK, Sharma KM, Singh TN (2017a) Evaluating the modulus of elasticity of soil using soft computing system. Eng Comput 33(3):497–507CrossRef

Sharma LK, Umrao RK, Singh R, Ahmad M, Singh TN (2017b) Stability investigation of hill cut soil slopes along national highway 222 at Malshej Ghat, Maharashtra, India. J Geol Soc India 89(2):165–174CrossRef

Sharma LK, Umrao RK, Singh R, Ahmad M, Singh TN (2017c) Geotechnical characterization of road cut hill slope forming unconsolidated geo-materials: a case study. Geotech Geol Eng 35(1):503–515CrossRef

Shen ZP, Chen W (2017) On the improvement of PSO scripts for slope stability analysis. In: Advances in swarm intelligence: 8th international conference, ICSI 2017, Fukuoka, Japan, Part I, pp 174–179

Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: Proceeding of the IEEE international conference of evolutionary computation, Piscataway, pp 69–73

Singh TN, Singh R, Singh B, Sharma LK, Singh R, Ansari MK (2016) Investigations stability analyses of Malin village lslide of Pune district, Maharashtra, India. Nat Hazards 81(3):2019–2030CrossRef

Singh R, Umrao RK, Ahmad M, Ansari MK, Sharma LK, Singh TN (2017) Prediction of geomechanical parameters using soft computing and multiple regression approach. Measurement 99:108–119CrossRef

Taha MR, Khajehzadeh M, El-Shafie A (2012) Application of particle swarm optimization in evaluating the reliability of soil slope stability analysis. Sains Malaysia 41(7):847–854

Toufigh MM, Ahangarasr A, Ouria A (2006) Using non-linear programming techniques in determination of the most probable slip surface in 3D slopes. World Acad Sci Eng Technol 17(5):30–35

Umrao RK, Singh R, Sharma LK, Singh TN (2017) Soil slope instability along a strategic road corridor in Meghalaya, northeastern India. Arab J Geosci. doi:10.1007/s12517-017-3043-8

Wan S (2013) Entropy-based particle swarm optimization with clustering analysis on lslide susceptibility mapping. Environ Earth Sci 68(5):1349–1366. doi:10.1007/s12665-012-1832-7 CrossRef

Wang G, Li H, Chu X (2010) Slope stability analysis based on harmony search method particle swarm optimization method. J Liaoning Tech Univ (Natural Science Edition) 29:208–211

Wang L, Zeng J, Xu L (2011) A decision support system for substage-zoning filling design of rock-fill dams based on particle swarm optimization. Inf Technol Manag 12(2):111–119. doi:10.1007/s10799-011-0092-7 CrossRef

Wang SN, Shi C, Zhang YL, Chen KH (2013) Numerical limit equilibrium analysis method of slope stability based on particle swarm optimization. Appl Mech Mater 353:247–251. doi:10.4028/www.scientific.net/AMM.353-356.247

Wen-Tao MA (2009) Evaluation of rock slope stability based on PSO LSSVM. Rock Soil Mech 30(3):845–848

Windisch A, Wappler S, Wegener J (2007) Applying particle swarm optimization to software testing. In: Proceeding of 9th annual conference on genetic evolutionary computations, London, UK, pp 1121–1128

Xing J, Jiang A, Qiu J (2010) Studying the adaptive control of tunnel excavation based on numerical simulation and particle swarm optimization. In: ICCTP 2010: integrated transportation systems-green intelligent reliable, pp 3117–3125

Xu J, Zeng Z (2010) Applying optimal control model to dynamic equipment allocation problem: case study of concrete-faced rockfill dam construction project. J Constr Eng Manag 137(7):536–550. doi:10.1061/(ASCE)CO.1943-7862.0000325 CrossRef

Xu F, Xu WY, Liu ZB, Liu K (2011) Slope stability evaluation based on PSO-PP. Chin J Geotech Eng 33(11):1708–1715

Xue X (2017) Prediction of slope stability based on hybrid PSO LSSVM. J Comput Civ Eng 31(1):04016041CrossRef

Yagiz S, Karahan H (2011) Prediction of hard rock TBM penetration rate using particle swarm optimization. Int J Rock Mech Min Sci 48(3):427–433. doi:10.1016/j.ijrmms.2011.02.013 CrossRef

Yamagami T, Jiang JC (1997) A search for the critical slip surface in three-dimensional slope stability analysis. Soils Found 37:1–16. doi:10.3208/sf.37.3_1 CrossRef

Yang XS (2008) Nature-inspired metaheuristic algorithms. Luniver Press. ISBN 1-905986-10-6

Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: World Congress on Nature and Biologically Inspired Computing (NaBIC 2009). IEEE Publications, pp 210–214

Yunkai L, Yingjie T, Zhiyun O, Lingyan W, Tingwu X, Peiling Y, Huanxun Z (2010) Analysis of soil erosion characteristics in small watersheds with particle swarm optimization, support vector machine and artificial neuronal networks. Environ Earth Sci 60(7):1559–1568. doi:10.1007/s12665-009-0292-1 CrossRef

Zhan Y, Wu Y (2009) Recognition of altered rock based on improved particle swarm neural network. In: International symposium on neural networks, Springer: Berlin, pp 149–155

Zhang JR, Zhang J, Lok TM, Lyu MR (2007) A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training. Appl Math Comput 185(2):1026–1037. doi:10.1016/j.amc.2006.07.025

Zhang Y, Gallipoli D, Augarde C (2009) Parallel hybrid particle swarm optimization and applications in geotechnical engineering. In: Advances in computation and intelligence. Springer, Berlin, pp 466–475

Zhang Y, Gallipoli D, Augarde C (2013a) Parameter identification for elasto-plastic modelling of unsaturated soils from pressuremeter tests by parallel modified particle swarm optimization. Comput Geotech 48:293–303. doi:10.1016/j.compgeo.2012.08.004 CrossRef

Zhang Y, Xiong X, Zhang Q (2013b) An improved self-adaptive PSO algorithm with detection function for multimodal function optimization problems. Math Probl Eng. doi:10.1155/2013/716952

Zhao HB, Yin S (2009) Geomechanical parameters identification by particle swarm optimization support vector machine. Appl Math Model 33(10):3997–4012. doi:10.1016/j.apm.2009.01.011 CrossRef

Zhao HB, Yin S (2010) A CPSO-SVM model for ultimate bearing capacity determination. Mar Georesour Geotechnol 28(1):64–75. doi:10.1080/10641190903359076 CrossRef

Zhao H., Zou ZS, Ru ZL (2008) Chaotic particle swarm optimization for non-circular critical slip surface identification in slope stability analysis. In: Proceedings of the international young scholars’ symposium on rock mechanics—boundaries of rock mechanics recent advances challenges for the 21st century, Beijing, China, pp 585–588

Zhou YW, Cai XJ, Chen JB (2012) A new method of simulating arbitrary slip surface in soil slope. Appl Mech Mater 204:350–353

Titel: Applications of Particle Swarm Optimization in Geotechnical Engineering: A Comprehensive Review
verfasst von: M. Hajihassani
D. Jahed Armaghani
R. Kalatehjari
Publikationsdatum: 04.09.2017
Verlag: Springer International Publishing
Erschienen in: Geotechnical and Geological Engineering / Ausgabe 2/2018
Print ISSN: 0960-3182
Elektronische ISSN: 1573-1529
DOI: https://doi.org/10.1007/s10706-017-0356-z

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