基于混合变异策略差分进化算法的边坡滑裂面搜索研究
|
摘要
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。
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.
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.
引文
[1]邵龙潭,李红军.土工结构稳定有限元极限平衡分析法及其应用[M].北京:科学出版社,2011.
[2]冯欣,熊子正,滕志强,等.基于积分法的均质边坡稳定性区间分析[J].水资源与水工程学报,2017,28(1):192-196.
[3]窦灿,商开卫,张伯涛.杨房沟水电站高边坡块体极限平衡稳定性分析[J].水资源与水工程学报,2011,22(1):50-54.
[4]祁小辉,李典庆,周创兵,等.考虑土体空间变异性的边坡最危险滑动面随机分析方法[J].岩土工程学报,2013,35(4):745-753.
[5]孙聪,李春光,郑宏.基于整体稳定性分析法的边坡临界滑动面搜索[J].岩土力学,2013,34(9):2583-2588.
[6]李宁,郭双枫,姚显春.边坡潜在滑动面模拟方法研究[J].岩石力学与工程学报,2016,35(12):2377-2387.
[7]郭明伟,李春光,葛修润,等.基于矢量和分析方法的边坡滑面搜索[J].岩土力学,2009,30(6):1775-1781.
[8]谢桂华,刘荣桂,王军.基于遗传算法的边坡关键概率滑面搜索[J].江苏科技大学学报(自然科学报),2011,25(2):115-118
[9]李南生,唐博,谈风婕,等.基于统一强度理论的土石坝边坡稳定分析遗传算法[J].岩土力学,2013,34(1):243-249.
[10]高玮,张鲁渝,张飞君.边坡临界滑动面搜索的奖惩蚁群算法研究[J].水利学报,2012,43(2):209-215.
[11]邓志平,李典庆,曹子君,等.考虑地层变异性和土体参数变异性的边坡可靠度分析[J].岩土工程学报,2017,39(6):986-995.
[12]黄晓锋,石崇,朱珍德,等.基于粒子群优化算法的边坡临界滑动面搜索方法[J].防灾减灾工程学报,2014,34(6):751-757.
[13]胡卫东,曹文贵.基于粒子群BP网络混合算法的边坡稳定性评价[J].铁道科学与工程学报,2015,12(1):66-71.
[14]KAHATADENIYA K S,NANAKORN P,NEAUPANE K M.Determination of the critical failure surface for slope stability analysis using ant colony optimization[J].Engineering Geology,2009,108(1-2):133-141.
[15]石露,李小春,孙冠华,等.地下矿体采动下的露天采场边坡稳定性研究[J].岩土力学,2012,33(3):812-820.
[16]STORN R,PRICE K.Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J].Journal of Global Optimization,1997,11(4):341-359.
[17]张春美.差分进化算法理论与应用[M].北京:北京理工大学出版社,2014.
[18]QIN A K,HUANG V L,SUGANTHAN P N.Differential evolution algorithm with strategy adaptation for global numerical optimization[J].IEEE Transactions on Evolutionary Computation,2009,13(2):398-417.
[19]TANABE R,FUKUNAGA A.Success-history based parameter adaptation for Differential Evolution[C]//IEEE Evolutionary Computation,2013:71-78.
[20]钱武文,柴军瑞,张子映,等.基于反射变异策略的自适应差分进化算法[J/OL].计算机工程与应用,2012,49(6):1288-1297[2017-12-01].http://kns.cnki.net/kcms/detail/11.2127.TP.20171130.1606.010.html.
[21]MALKAWI A I H,HASSAN W F,SARMA S K.Global search method for locating general slip surface using monte carlo techniques[J].Journal of Geotechnical&Geoenvironmental Engineering,2001,127(8):688-698.
[2]冯欣,熊子正,滕志强,等.基于积分法的均质边坡稳定性区间分析[J].水资源与水工程学报,2017,28(1):192-196.
[3]窦灿,商开卫,张伯涛.杨房沟水电站高边坡块体极限平衡稳定性分析[J].水资源与水工程学报,2011,22(1):50-54.
[4]祁小辉,李典庆,周创兵,等.考虑土体空间变异性的边坡最危险滑动面随机分析方法[J].岩土工程学报,2013,35(4):745-753.
[5]孙聪,李春光,郑宏.基于整体稳定性分析法的边坡临界滑动面搜索[J].岩土力学,2013,34(9):2583-2588.
[6]李宁,郭双枫,姚显春.边坡潜在滑动面模拟方法研究[J].岩石力学与工程学报,2016,35(12):2377-2387.
[7]郭明伟,李春光,葛修润,等.基于矢量和分析方法的边坡滑面搜索[J].岩土力学,2009,30(6):1775-1781.
[8]谢桂华,刘荣桂,王军.基于遗传算法的边坡关键概率滑面搜索[J].江苏科技大学学报(自然科学报),2011,25(2):115-118
[9]李南生,唐博,谈风婕,等.基于统一强度理论的土石坝边坡稳定分析遗传算法[J].岩土力学,2013,34(1):243-249.
[10]高玮,张鲁渝,张飞君.边坡临界滑动面搜索的奖惩蚁群算法研究[J].水利学报,2012,43(2):209-215.
[11]邓志平,李典庆,曹子君,等.考虑地层变异性和土体参数变异性的边坡可靠度分析[J].岩土工程学报,2017,39(6):986-995.
[12]黄晓锋,石崇,朱珍德,等.基于粒子群优化算法的边坡临界滑动面搜索方法[J].防灾减灾工程学报,2014,34(6):751-757.
[13]胡卫东,曹文贵.基于粒子群BP网络混合算法的边坡稳定性评价[J].铁道科学与工程学报,2015,12(1):66-71.
[14]KAHATADENIYA K S,NANAKORN P,NEAUPANE K M.Determination of the critical failure surface for slope stability analysis using ant colony optimization[J].Engineering Geology,2009,108(1-2):133-141.
[15]石露,李小春,孙冠华,等.地下矿体采动下的露天采场边坡稳定性研究[J].岩土力学,2012,33(3):812-820.
[16]STORN R,PRICE K.Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J].Journal of Global Optimization,1997,11(4):341-359.
[17]张春美.差分进化算法理论与应用[M].北京:北京理工大学出版社,2014.
[18]QIN A K,HUANG V L,SUGANTHAN P N.Differential evolution algorithm with strategy adaptation for global numerical optimization[J].IEEE Transactions on Evolutionary Computation,2009,13(2):398-417.
[19]TANABE R,FUKUNAGA A.Success-history based parameter adaptation for Differential Evolution[C]//IEEE Evolutionary Computation,2013:71-78.
[20]钱武文,柴军瑞,张子映,等.基于反射变异策略的自适应差分进化算法[J/OL].计算机工程与应用,2012,49(6):1288-1297[2017-12-01].http://kns.cnki.net/kcms/detail/11.2127.TP.20171130.1606.010.html.
[21]MALKAWI A I H,HASSAN W F,SARMA S K.Global search method for locating general slip surface using monte carlo techniques[J].Journal of Geotechnical&Geoenvironmental Engineering,2001,127(8):688-698.