中文摘要：

采用粒子群优化算法搜索边坡的临界滑动面及其对应的最小安全系数。粒子群优化算法不断迭代更新试算滑动面，使其安全系数不断减小，经过有限次的迭代分析可确定边坡临界滑动面及其对应的全局最小安全系数。粒子群优化算法具有较好的全局搜索和局部搜索能力，可克服多数常规的优化方法易陷入安全系数局部极小的问题，并具有较高的搜索效率。同时，粒子群优化算法易于与极限平衡法或有限元-极限平衡法相结合进行边坡稳定分析。通过数值算例及与其他学者的结果比较，证明提出的确定边坡临界滑动面方法的有效性。

英文摘要：

A new method, particle swarm optimization（PSO） algorithm, is adopted to locate the non-circular critical slip surfaces of slopes. These slip surfaces are verified and refined frequently by PSO algorithm and tend gradually to the critical slip surface, and simultaneously their safety factors decrease towards the global minimum safety factors. The critical slip surface and its corresponding global minimum safety factor can be obtained within limited iterations. PSO algorithm can achieve a good balance between a global search and a local refinement： and it can solve the problem of falling into local minima, which happens in most of the regular optimization methods. At the same time, it shows great efficiency. Furthermore, PSO algorithm can be easily incorporated with the traditional limit equilibrium methods as well as the finite element method for slope stability analysis. A numerical example is analyzed to show the effectiveness and efficiency of the proposed method.