Jianing Hao¹, Dan Yang², Guanxiong Ren¹, Ying Zhao³, Rangling Cao^4,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.1, pp. 141-159, 2025, DOI:10.32604/fdmp.2024.054469 - 24 January 2025

Abstract This study presents an innovative approach to calculating the failure probability of slopes by incorporating fuzzy limit-state functions, a method that significantly enhances the accuracy and efficiency of slope stability analysis. Unlike traditional probabilistic techniques, this approach utilizes a least squares support vector machine (LSSVM) optimized with a grey wolf optimizer (GWO) and K-fold cross-validation (CV) to approximate the limit-state function, thus reducing computational complexity. The novelty of this work lies in its application to one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) slope models, demonstrating its versatility and high precision. The proposed method consistently achieves… More > Graphic Abstract

Junqiang Jiang^1,2, Zhifang Sun¹, Xiong Jiang¹, Shengjie Jin¹, Yinli Jiang³, Bo Fan^1,*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1617-1644, 2023, DOI:10.32604/cmc.2023.041973 - 29 November 2023

Abstract The grey wolf optimizer (GWO) is a swarm-based intelligence optimization algorithm by simulating the steps of searching, encircling, and attacking prey in the process of wolf hunting. Along with its advantages of simple principle and few parameters setting, GWO bears drawbacks such as low solution accuracy and slow convergence speed. A few recent advanced GWOs are proposed to try to overcome these disadvantages. However, they are either difficult to apply to large-scale problems due to high time complexity or easily lead to early convergence. To solve the abovementioned issues, a high-accuracy variable grey wolf optimizer… More >