A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Genetic-simulated annealing optimization for surface wave inversion of shear-wave velocity profiles of geotechnical sites
https://orcid.org/0000-0002-8410-5584
https://orcid.org/0000-0003-0677-9900
Abstract A new hybrid genetic-simulated-annealing (GSA) optimization algorithm is introduced to solve the multivariable minimization problem for surface wave inversion. The algorithm is effective for both global and local searches due to its combination of the reproduction and selective generation schemes from genetic algorithms (GA) with the nonlinear scaling fitness function and perturbation scheme from simulated annealing (SA). The hybrid GSA algorithm can reduce the risk of a solution becoming trapped in a local minimum and improve global searching efficiency. A mathematical test function as well as surface wave examples are used to examine the advantages and performance of the GSA algorithm. Comparisons of GA, SA, and GSA inversion results demonstrates that GSA can yield the smallest uncertainty and greatest efficiency, and improve the statistical confidence of using surface wave testing for shear-wave velocity profiling.
Genetic-simulated annealing optimization for surface wave inversion of shear-wave velocity profiles of geotechnical sites
https://orcid.org/0000-0002-8410-5584
https://orcid.org/0000-0003-0677-9900
Abstract A new hybrid genetic-simulated-annealing (GSA) optimization algorithm is introduced to solve the multivariable minimization problem for surface wave inversion. The algorithm is effective for both global and local searches due to its combination of the reproduction and selective generation schemes from genetic algorithms (GA) with the nonlinear scaling fitness function and perturbation scheme from simulated annealing (SA). The hybrid GSA algorithm can reduce the risk of a solution becoming trapped in a local minimum and improve global searching efficiency. A mathematical test function as well as surface wave examples are used to examine the advantages and performance of the GSA algorithm. Comparisons of GA, SA, and GSA inversion results demonstrates that GSA can yield the smallest uncertainty and greatest efficiency, and improve the statistical confidence of using surface wave testing for shear-wave velocity profiling.
Genetic-simulated annealing optimization for surface wave inversion of shear-wave velocity profiles of geotechnical sites
https://orcid.org/0000-0002-8410-5584
https://orcid.org/0000-0003-0677-9900
Abstract A new hybrid genetic-simulated-annealing (GSA) optimization algorithm is introduced to solve the multivariable minimization problem for surface wave inversion. The algorithm is effective for both global and local searches due to its combination of the reproduction and selective generation schemes from genetic algorithms (GA) with the nonlinear scaling fitness function and perturbation scheme from simulated annealing (SA). The hybrid GSA algorithm can reduce the risk of a solution becoming trapped in a local minimum and improve global searching efficiency. A mathematical test function as well as surface wave examples are used to examine the advantages and performance of the GSA algorithm. Comparisons of GA, SA, and GSA inversion results demonstrates that GSA can yield the smallest uncertainty and greatest efficiency, and improve the statistical confidence of using surface wave testing for shear-wave velocity profiling.
Genetic-simulated annealing optimization for surface wave inversion of shear-wave velocity profiles of geotechnical sites
Lin, Shibin (author) / Ashlock, Jeramy C. (author) / Zhao, Guochen (author) / Lai, Qinghui (author) / Xu, Longjun (author) / Zhai, Changhai (author)
2023-05-08
Article (Journal)
Electronic Resource
English
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