Global Optimization Search Method for Minimum Safety Factor of Slope Based on Chaotic Grey Wolf Optimization Algorithm

doi:10.12068/j.issn.1005-3026.2022.07.016

Abstract

Abstract: For the problems of uneven initial population and premature convergence in the basic grey wolf algorithm， grey wolf optimization(GWO) algorithm is improved from three aspects based on chaos theory， and a chaotic grey wolf optimization(CGWO) is proposed for determining the minimum safety factor of the slope. Firstly， an improved Tent chaotic mapping is used to improve the initial population diversity; Secondly， a chaotic perturbation strategy is used to avoid the algorithm from falling into a local optimal; Finally， a parametric chaotic non-linear adjustment mechanism is introduced to balance the global exploitation and local exploration arithmetic of the algorithm. Simulation results of 13 benchmark test functions show that the improved algorithm has a stronger integrated optimization search performance compared with the basic GWO， WOA， PSO and SCA. Selecting the ACADS side slope assessment questions for computation and analysis， the CGWO algorithm shows a high computational accuracy and convergence speed， and can effectively search for the minimum safety factor of complex stratified slopes. Compared with the finite element strength reduction method， the method has the advantages of easy operation and easy setting of the search area.

Key words: grey wolf optimization(GWO) algorithm; chaotic mapping; slope stability analysis; the most dangerous sliding surface; minimum safety factor

CLC Number:

TU43

WANG Shu-hong， WEI Wei， HAN Wen-shuai， CHEN Hao. Global Optimization Search Method for Minimum Safety Factor of Slope Based on Chaotic Grey Wolf Optimization Algorithm[J]. Journal of Northeastern University(Natural Science), 2022, 43(7): 1033-1042.

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