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Enhancement of backtracking search algorithm for identifying soil parameters
Author(s) -
Jin YinFu,
Yin ZhenYu
Publication year - 2020
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.3059
Subject(s) - backtracking , benchmark (surveying) , identification (biology) , algorithm , robustness (evolution) , computer science , convergence (economics) , differential evolution , mathematical optimization , mathematics , biochemistry , chemistry , botany , geodesy , economic growth , gene , economics , biology , geography
Summary In this paper, an enhanced backtracking search algorithm (so‐called MBSA‐LS) for parameter identification is proposed with two modifications: (a) modifying the mutation of original backtracking search algorithm (BSA) considering the contribution of current best individual for accelerating convergence speed and (b) novelly incorporating an efficient differential evolution (DE) as local search for improving the quality of population. The proposed MBSA‐LS is first validated with better performance than the original BSA and some other typical state‐of‐the‐art optimization algorithms on a benchmark of soil parameter identification in terms of effectiveness, efficiency, and robustness. Then, the efficiency of the MBSA‐LS is further illustrated by two representative cases: identifying soil parameters from both laboratory tests and field measurements. All comparisons demonstrate that the proposed MBSA‐LS algorithm can give accurate results in a short time. Finally, to conveniently solve the problems of parameter identification, a practical tool ErosOpt for parameter identification is developed by integrating the proposed MBSA‐LS and some other efficient algorithms for readers to conduct the parameter identification using optimisation algorithms.