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Comparison study on nature-inspired optimization algorithms for optimization back analysis of underground engineering

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Abstract

Optimization back analysis is the most common approach to displacement back analysis for underground engineering. However, this is a non-convex problem that requires the use of nature-inspired global optimization algorithms. Therefore, the present study will investigate on the suitability of six state-of-the-art nature-inspired algorithms for elastic back analysis and elastic–plastic back analysis. These algorithms include improved genetic algorithm, immunized evolutionary programming, particle swarm optimization, continuous ant colony optimization, artificial bee colony and black hole algorithm. Numerical results indicate that immunized evolutionary programming is overall the best algorithm followed by the black hole algorithm; while, the improved genetic algorithm is the worst optimizer. Meanwhile, using elastic back analysis, the sensitivity analysis of the main input parameters for these nature-inspired optimization algorithms has been conducted. At last, the comparative results have been verified by using in one real underground roadway in Huainan coal mine of China.

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Authors and Affiliations

  1. Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, College of Civil and Transportation Engineering, Hohai University, Nanjing, 210098, People’s Republic of China

    Wei Gao

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Gao, W. Comparison study on nature-inspired optimization algorithms for optimization back analysis of underground engineering. Engineering with Computers 37, 1895–1919 (2021). https://doi.org/10.1007/s00366-019-00918-7

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  • DOI: https://doi.org/10.1007/s00366-019-00918-7

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