边坡临界滑面搜索的改进粒子群优化算法

边坡临界滑面的确定对边坡稳定分析和加固设计极为重要,采用基于变异和二次序列规划的改进粒子群优化算法(VSPSO)进行临界滑面搜索。VSPSO算法中通过变异操作增强粒子群跳出局部最优解的能力,并用二次序列规划(SQP)加速局部搜索,大大提高了粒子群获得全局最优的能力。通过对有解析解的边坡算例进行分析,验证了该算法的准确性及优越性;对澳大利亚计算机应用协会(ACADS)提供的均质边坡、多层土边坡以及含软弱层边坡进行分析,结果表明改进的VSPSO算法搜索所得滑面比传统PSO算法更逼近推荐答案,具有更好的鲁棒性,而且随着边坡复杂程度的增加,更能体现改进VSPSO算法的优越性,具有广阔的应用前景。

Search of critical slip surface of slopes using improved particle swarm optimization method

The location of the critical slip surface is a very important issue in slope stability analysis and reinforcement design. In this study, an improved particle swarm optimization (VSPSO) algorithm is proposed to search for the critical slip surface based on particle variation (PV) and sequential quadratic programming (SQP). PV enhances the ability of PSO in jumping out of the local optimum, and SQP accelerates local search. The combination of PV and SQP greatly promotes the capacity of the original PSO in looking for the global optimum. An example with analytical solution is analyzed by the VSPSO, and the results demonstrate the accuracy and efficiency of the proposed model. Three typical examples from ACADS are then given, which are respectively homogeneous slope, multilayer soil slope and slope with weak layer. It is shown that results from the VSPSO are more approximate to the recommended values than those from the PSO. Furthermore, the VSPSO has a quite well robustness for the slopes with very complicated geometries and material properties.