基于CGWO算法的边坡最小安全系数全局寻优方法

针对基本灰狼算法存在初始种群不均匀、早熟收敛等问题，基于混沌理论从三个方面对灰狼优化(grey wolf optimization， GWO)算法进行改进，提出了混沌灰狼优化(chaotic grey wolf optimization，CGWO)算法用于确定边坡的最小安全系数.首先，采用改进Tent混沌映射提高初始种群多样性；其次，通过混沌扰动策略避免算法陷入局部最优；最后，引入参数混沌非线性调节机制均衡算法的全局开发和局部勘探算力.13个基准测试函数的仿真结果表明，改进后的算法与基本GWO，WOA，PSO以及SCA相比具有更强的综合寻优性能.选取ACADS边坡考核题进行计算分析，CGWO算法表现出较高的计算精度和收敛速度，能够有效地搜索到复杂分层边坡的最小安全系数.对比有限元强度折减法，该方法具有操作简易、搜索区域易于设置等优点.

Global Optimization Search Method for Minimum Safety Factor of Slope Based on Chaotic Grey Wolf Optimization Algorithm

For the problems of uneven initial population and premature convergence in the basic grey wolf algorithm， grey wolf optimization(GWO) algorithm is improved from three aspects based on chaos theory， and a chaotic grey wolf optimization(CGWO) is proposed for determining the minimum safety factor of the slope. Firstly， an improved Tent chaotic mapping is used to improve the initial population diversity; Secondly， a chaotic perturbation strategy is used to avoid the algorithm from falling into a local optimal; Finally， a parametric chaotic non-linear adjustment mechanism is introduced to balance the global exploitation and local exploration arithmetic of the algorithm. Simulation results of 13 benchmark test functions show that the improved algorithm has a stronger integrated optimization search performance compared with the basic GWO， WOA， PSO and SCA. Selecting the ACADS side slope assessment questions for computation and analysis， the CGWO algorithm shows a high computational accuracy and convergence speed， and can effectively search for the minimum safety factor of complex stratified slopes. Compared with the finite element strength reduction method， the method has the advantages of easy operation and easy setting of the search area.