Slope stability analysis based on quantum-behaved particle swarm optimization and least squares support vector machine

Abstract

a b s t r a c tGiven the complexity and uncertainty of the influencing factors of slope stability, its accurate evaluation is difficult to accomplish using conventional approaches. This paper presents the use of a least square support vector machine (LSSVM) algorithm based on quantum-behaved particle swarm optimization (QPSO) to establish the nonlinear relationship of slope stability. In the proposed QPSO-LSSVM algorithm, QPSO is employed to optimize the important parameters of LSSVM. To identify the local and global optimum, three popular benchmark functions are utilized to test the abilities of the proposed QPSO, the nonlinearly decreasing weight PSO, and the linearly decreasing weight PSO algorithms. The proposed QPSO exhibited superior performance over the other aforementioned algorithms. Simulation results obtained from PSO-LSSVM, QPSO-LSSVM, and LSSVM algorithms are compared in a case. Case analysis shows that QPSO-LSSVM has the quickest search velocity and the best convergence performance among the three algorithms, and is therefore considered most suitable for slope stability analysis.

A general framework for fatigue reliability analysis of a high temperature component

“…The support vector machine, known as a supervised learning technique, is applicable to both classification and regression analysis in the field of machine learning . Least squares support vector machines (LS‐SVM) is one of the developed standard SVM and is cable of approximating the LSF with more excellent accuracy and smaller computational cost . Due to that, it has been widely used in engineering reliability analysis …”

“…23,24 Least squares support vector machines (LS-SVM) is one of the developed standard SVM and is cable of approximating the LSF with more excellent accuracy and smaller computational cost. 25 Due to that, it has been widely used in engineering reliability analysis. 26,27 For a given training set of N data points {x k , y k } (k = 1, 2 .…”

A Fault Diagnosis Solution of Rolling Bearing Based on MEEMD and QPSO-LSSVM

“…It can improve the classification accuracy using the QPSO algorithm to perform parameters optimization of LSSVM. In the process of parameters optimization of the kernel function parameter σ and penalty factor C of LSSVM, the QPSO algorithm can guarantee the convergence of the algorithm during the search process and ensure that the algorithm can avoid premature convergence while the algorithm is convergent [31]. It has a very important impact on improving the learning ability and generalization ability of the LSSVM algorithm.…”

Profile Orientation and Slope Stability Analysis

“…optimization technique that has been applied to many different fields to solve many different types of problems, including the stability analysis of earth slopes. For earth slopes, PSO was primarily used to compute the minimum FS and to determine the most critical sliding surface [13][14][15][16][17][18][19][20]. In addition to theoretical framework development, a number of real-world engineering case studies were also available.…”