关键词: 支持向量机, 粒子群, 隧道变形, 预测

Abstract:  Aiming at solving the precocious convergence problem and the oscillation phenomena around global optimal solution of Particle Swarm Optimization（PSO） algorithm, a self-adaption inertia weight method based on PSO （SaωPSO） is proposed. The nonlinear change idea of the hyperbolic tangent function is introduced to the new algorithm. Additionally, the inertia weight with the increasing iterations produces adaptive adjustment. Moreover, this would enhance the searching ability and convergence speed of particle, and avoid falling into local extremum. The SaωPSO with Support Vector Machine（SVM） is applied to build the tunnel deformation prediction model. In the case analyses, the new method is used for parameters optimization. The effectiveness of the hybrid algorithm is tested by the two performance measured data in steady and non-steady states. The result shows that SaωPSO+SVM could improve the forecasting precision, enhance its robustness, and also contribute the deformation prediction model.

Key words: support vector machine, particle swarm optimization, tunnel deformation, prediction