This study proposes an alternative calculation mode for stresses on the slip surface(SS).The calculation of the normal stress(NS)on the SS involves examining its composition and expanding its unknown using the Taylor ...This study proposes an alternative calculation mode for stresses on the slip surface(SS).The calculation of the normal stress(NS)on the SS involves examining its composition and expanding its unknown using the Taylor series.This expansion enables the reasonable construction of a function describing the NS on the SS.Additionally,by directly incorporating the nonlinear Generalized Hoke-Brown(GHB)strength criterion and utilizing the slope factor of safety(FOS)definition,a function of the shear stress on the SS is derived.This function considers the mutual feedback mechanism between the NS and strength parameters of the SS.The stress constraints conditions are then introduced at both ends of the SS based on the spatial stress relation of one point.Determining the slope FOS and stress solution for the SS involves considering the mechanical equilibrium conditions and the stress constraint conditions satisfied by the sliding body.The proposed approach successfully simulates the tension-shear stress zone near the slope top and provides an intuitive description of the concentration effect of compression-shear stress of the SS near the slope toe.Furthermore,compared to other methods,the present method demonstrates superior processing capabilities for the embedded nonlinear GHB strength criterion.展开更多
为设计高效稳定的演化算法,将方程求根的不动点迭代思想引入到优化领域,通过将演化算法的寻优过程看作为在迭代框架下方程不动点的逐步显示化过程,设计出一种基于数学模型的演化新算法,即不动点演化算法(fixed point evolution algorith...为设计高效稳定的演化算法,将方程求根的不动点迭代思想引入到优化领域,通过将演化算法的寻优过程看作为在迭代框架下方程不动点的逐步显示化过程,设计出一种基于数学模型的演化新算法,即不动点演化算法(fixed point evolution algorithm,FPEA).该算法的繁殖算子是由Aitken加速的不动点迭代模型导出的二次多项式,其整体框架继承传统演化算法(如差分演化算法)基于种群的迭代模式.试验结果表明:在基准函数集CEC2014、CEC2019上,本文算法的最优值平均排名在所有比较算法中排名第1;在4个工程约束设计问题上,FPEA与CSA、GPE等多个算法相比,能以较少的计算开销获得最高的求解精度.展开更多
梯级水电短期调度时需保障各电站下游河段的生态要求,而生态调度的深入研究要求生态约束越来越多样化,使得流域梯级短期优化调度问题愈加复杂。该文首先将生态约束按流量约束和控制方式分为Ⅰ类、Ⅱ类和Ⅲ类,满足梯级库群复杂的生态调...梯级水电短期调度时需保障各电站下游河段的生态要求,而生态调度的深入研究要求生态约束越来越多样化,使得流域梯级短期优化调度问题愈加复杂。该文首先将生态约束按流量约束和控制方式分为Ⅰ类、Ⅱ类和Ⅲ类,满足梯级库群复杂的生态调度要求;接着以剩余负荷峰谷差最小为目标,构建了耦合复杂生态约束的梯级水电短期日前调峰模型;最后利用分段线性化技术处理一元水力非线性约束,将四边形网格栅格化技术与第二类特殊顺序集(special ordered sets of type 2,SOS2)约束建模方法结合处理二元电力非线性约束,采用大M法对较为复杂的Ⅲ类生态约束进行线性化,将原模型转化为标准混合整数线性规划(mixed-integer linear programming,MILP)模型后进行求解。以乌江干流(贵州段)6座梯级水库为工程背景,在汛枯期不同来水场景下验证了模型的有效性且计算结果较逐次逼近动态规划(dynamic programming successive approximation,DPSA)算法优越性显著,通过与3个参照模型的结果对比分析得出生态约束的设置与类型会明显影响调峰效果,强调水利工程生态流量合理设计的必要性。展开更多
针对包含复杂约束条件的约束多目标优化问题(CMOP),在确保算法满足严格约束的同时,有效平衡算法的收敛性与多样性是重大挑战。因此,提出一种双种群双阶段的进化算法(DPDSEA)。该算法引入2个独立进化种群:主种群和副种群,并分别利用可行...针对包含复杂约束条件的约束多目标优化问题(CMOP),在确保算法满足严格约束的同时,有效平衡算法的收敛性与多样性是重大挑战。因此,提出一种双种群双阶段的进化算法(DPDSEA)。该算法引入2个独立进化种群:主种群和副种群,并分别利用可行性规则和改进的epsilon约束处理方法进行更新。在第一阶段,主种群和副种群分别探索约束Pareto前沿(CPF)与无约束Pareto前沿(UPF),从而获取UPF和CPF的位置信息;在第二阶段,设计一种分类方法,根据UPF与CPF的位置对CMOP进行分类,从而对不同类型的CMOP执行特定的进化策略;此外,提出一种随机扰动策略,在副种群进化到CPF附近时,对它进行随机扰动以产生一些位于CPF上的个体,从而促进主种群在CPF上的收敛与分布。把所提算法与6个具有代表性的算法:CMOES(Constrained Multi-objective Optimization based on Even Search)、dp-ACS(dual-population evolutionary algorithm based on Adaptive Constraint Strength)、c-DPEA(DualPopulation based Evolutionary Algorithm for constrained multi-objective optimization)、CAEAD(Constrained Evolutionary Algorithm based on Alternative Evolution and Degeneration)、BiCo(evolutionary algorithm with Bidirectional Coevolution)和DDCMOEA(Dual-stage Dual-population Evolutionary Algorithm for Constrained Multiobjective Optimization)在LIRCMOP和DASCMOP两个测试集上进行实验比较。实验结果表明,DPDSEA在23个问题中取得了15个最优反转世代距离(IGD)值和12个最优超体积(HV)值,展现了DPDSEA在处理复杂CMOP时显著的性能优势。展开更多
基金Project(52278380)supported by the National Natural Science Foundation of ChinaProject(2023JJ30670)supported by the National Science Foundation of and Technology Major Project of Hunan Province,China。
文摘This study proposes an alternative calculation mode for stresses on the slip surface(SS).The calculation of the normal stress(NS)on the SS involves examining its composition and expanding its unknown using the Taylor series.This expansion enables the reasonable construction of a function describing the NS on the SS.Additionally,by directly incorporating the nonlinear Generalized Hoke-Brown(GHB)strength criterion and utilizing the slope factor of safety(FOS)definition,a function of the shear stress on the SS is derived.This function considers the mutual feedback mechanism between the NS and strength parameters of the SS.The stress constraints conditions are then introduced at both ends of the SS based on the spatial stress relation of one point.Determining the slope FOS and stress solution for the SS involves considering the mechanical equilibrium conditions and the stress constraint conditions satisfied by the sliding body.The proposed approach successfully simulates the tension-shear stress zone near the slope top and provides an intuitive description of the concentration effect of compression-shear stress of the SS near the slope toe.Furthermore,compared to other methods,the present method demonstrates superior processing capabilities for the embedded nonlinear GHB strength criterion.
文摘为设计高效稳定的演化算法,将方程求根的不动点迭代思想引入到优化领域,通过将演化算法的寻优过程看作为在迭代框架下方程不动点的逐步显示化过程,设计出一种基于数学模型的演化新算法,即不动点演化算法(fixed point evolution algorithm,FPEA).该算法的繁殖算子是由Aitken加速的不动点迭代模型导出的二次多项式,其整体框架继承传统演化算法(如差分演化算法)基于种群的迭代模式.试验结果表明:在基准函数集CEC2014、CEC2019上,本文算法的最优值平均排名在所有比较算法中排名第1;在4个工程约束设计问题上,FPEA与CSA、GPE等多个算法相比,能以较少的计算开销获得最高的求解精度.
文摘梯级水电短期调度时需保障各电站下游河段的生态要求,而生态调度的深入研究要求生态约束越来越多样化,使得流域梯级短期优化调度问题愈加复杂。该文首先将生态约束按流量约束和控制方式分为Ⅰ类、Ⅱ类和Ⅲ类,满足梯级库群复杂的生态调度要求;接着以剩余负荷峰谷差最小为目标,构建了耦合复杂生态约束的梯级水电短期日前调峰模型;最后利用分段线性化技术处理一元水力非线性约束,将四边形网格栅格化技术与第二类特殊顺序集(special ordered sets of type 2,SOS2)约束建模方法结合处理二元电力非线性约束,采用大M法对较为复杂的Ⅲ类生态约束进行线性化,将原模型转化为标准混合整数线性规划(mixed-integer linear programming,MILP)模型后进行求解。以乌江干流(贵州段)6座梯级水库为工程背景,在汛枯期不同来水场景下验证了模型的有效性且计算结果较逐次逼近动态规划(dynamic programming successive approximation,DPSA)算法优越性显著,通过与3个参照模型的结果对比分析得出生态约束的设置与类型会明显影响调峰效果,强调水利工程生态流量合理设计的必要性。
文摘针对包含复杂约束条件的约束多目标优化问题(CMOP),在确保算法满足严格约束的同时,有效平衡算法的收敛性与多样性是重大挑战。因此,提出一种双种群双阶段的进化算法(DPDSEA)。该算法引入2个独立进化种群:主种群和副种群,并分别利用可行性规则和改进的epsilon约束处理方法进行更新。在第一阶段,主种群和副种群分别探索约束Pareto前沿(CPF)与无约束Pareto前沿(UPF),从而获取UPF和CPF的位置信息;在第二阶段,设计一种分类方法,根据UPF与CPF的位置对CMOP进行分类,从而对不同类型的CMOP执行特定的进化策略;此外,提出一种随机扰动策略,在副种群进化到CPF附近时,对它进行随机扰动以产生一些位于CPF上的个体,从而促进主种群在CPF上的收敛与分布。把所提算法与6个具有代表性的算法:CMOES(Constrained Multi-objective Optimization based on Even Search)、dp-ACS(dual-population evolutionary algorithm based on Adaptive Constraint Strength)、c-DPEA(DualPopulation based Evolutionary Algorithm for constrained multi-objective optimization)、CAEAD(Constrained Evolutionary Algorithm based on Alternative Evolution and Degeneration)、BiCo(evolutionary algorithm with Bidirectional Coevolution)和DDCMOEA(Dual-stage Dual-population Evolutionary Algorithm for Constrained Multiobjective Optimization)在LIRCMOP和DASCMOP两个测试集上进行实验比较。实验结果表明,DPDSEA在23个问题中取得了15个最优反转世代距离(IGD)值和12个最优超体积(HV)值,展现了DPDSEA在处理复杂CMOP时显著的性能优势。
