基于混合变异策略差分进化算法的边坡滑裂面搜索研究

针对"基于反射变异策略的自适应差分进化算法"仍易陷入局部最优的问题,通过引入一个基本的变异策略提出一种基于混合变异策略(DE/current-to-rand/1)的差分进化算法。根据各变异策略生成成功子代的比率使用轮盘赌选择为各个个体选择合适的变异策略,以改善算法的全局收敛能力。将提出的算法结合有限元应力场应用于两个经典算例的边坡临界滑动面搜索及安全系数求解,与其他极限平衡法进行了对比,并使用其中一个算例作为计算模型与其他优化算法进行了收敛性能比较。统计结果验证了改进算法的性能更稳定且收敛速度较快,也验证了该算法结合有限元应力场求解边坡问题的有效性。

Research on slope sliding surface search based on differential evolution algorithm with mixed mutation strategy

In order to solve the problem that adaptive differential evolution algorithm based on reflection mutation strategy is prone to local optimization, a differential evolution algorithm based on mixed mutation strategy is proposed by introducing a basic mutation strategy, DE/current-to-rand/1. In order to improve the global convergence of the algorithm, the percentage of successful subgenerations based on the mutation strategy uses the roulette selection to select the appropriate mutation strategy for each individual. In this paper, the proposed algorithm combined with the finite element stress field is applied to the critical sliding surface search and the safety coefficient solution of two classical calculation examples. The results are compared with other limit equilibrium methods, and one example is used as a computational model to compare the convergence performance with other optimization algorithms. The results show that the improved algorithm proposed here has more stable performance and faster convergence speed. It also proves the effectiveness of the algorithm in combination with the finite element stress field to solve the slope problem.