遗传算法改进及其在岩土参数反分析中的应用

Translated title of the contribution: Enhancement of genetic algorithm and its application to the identification of soil parameters by inverse analysis

Hui Ji, Yin Fu Jin, Zhen Yu Yin, Ze Xiang Wu, Shui Long Shen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

The aim of this paper is to develop a new hybrid real-coded genetic algorithm to identify soil parameters.The new development is under the framework of a classical GA by combining two recently developed and efficient crossover operators with a hybrid strategy.A dynamic random mutation has been incorporated into the new RCGA to maintain the diversity of the population.Additionally,in order to improve the convergence speed,a chaotic local search(CLS) has been adopted.The new GA is applied to identify parameters from an in-situ pressuremeter test and an excavation respectively.In order to highlight the performance of the new GA,5 classic optimization methods(classic genetic algorithm,particle swarm optimization,simulated annealing,differential evolution algorithm and artificial bee colony algorithm) are selected to solve the same problems.The search ability and efficiency of the new hybrid RCGA is estimated by comparisons of all the above methods.

Translated title of the contributionEnhancement of genetic algorithm and its application to the identification of soil parameters by inverse analysis
Original languageChinese
Pages (from-to)224-229
Number of pages6
JournalJisuan Lixue Xuebao/Chinese Journal of Computational Mechanics
Volume35
Issue number2
DOIs
Publication statusPublished - 1 Apr 2018
Externally publishedYes

Keywords

  • Constitutive model
  • Finite element method
  • Genetic algorithm
  • Geomechanics
  • Inverse analysis

ASJC Scopus subject areas

  • Computational Mechanics
  • Modelling and Simulation
  • Applied Mathematics

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