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Slope stability analysis using evolutionary optimization techniques
Author(s) -
Gandomi A.H.,
Kashani A.R.,
Mousavi M.,
Jalalvandi M.
Publication year - 2016
Publication title -
international journal for numerical and analytical methods in geomechanics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.419
H-Index - 91
eISSN - 1096-9853
pISSN - 0363-9061
DOI - 10.1002/nag.2554
Subject(s) - evolutionary algorithm , stability (learning theory) , maxima and minima , metaheuristic , benchmark (surveying) , differential evolution , mathematical optimization , optimization problem , computer science , genetic algorithm , global optimization , evolutionary computation , mathematics , algorithm , machine learning , geology , mathematical analysis , geodesy
Summary Slope stability optimization, in the presence of a band of a weak layer between two strong layers, is accounted for in complicated geotechnical problems. Classical optimization algorithms are not suitable for solving such problems as they need a proper preliminary solution to converge to a valid result. Therefore, it is necessary to find a proper algorithm which is capable of finding the best global solution. Recently a lot of metaheuristic algorithms have been proposed which are able to evade local minima effectively. In this study four evolutionary algorithms, including well‐known and recent ones, such as genetic algorithm, differential evolution, evolutionary strategy and biogeography‐based optimization (BBO), are applied in slope stability analysis and their efficiencies are explored by three benchmark case studies. Result show BBO is the most efficient among these evolutionary algorithms and other proposed algorithms applied to this problem. Copyright © 2016 John Wiley & Sons, Ltd.