针对时变速度下的低碳配送需求,本文以配送总成本最小化为目标,构建考虑三维装载和时间窗约束的绿色车辆路径优化模型。模型考虑时变速度和实时载重对车辆燃油消耗量的影响。为准确计算行驶时间和油耗,采用二分K-means聚类算法对时段进...针对时变速度下的低碳配送需求,本文以配送总成本最小化为目标,构建考虑三维装载和时间窗约束的绿色车辆路径优化模型。模型考虑时变速度和实时载重对车辆燃油消耗量的影响。为准确计算行驶时间和油耗,采用二分K-means聚类算法对时段进行合理划分。设计两阶段算法求解模型:第一阶段采用自适应大规模邻域搜索(adaptive large neighborhood search,ALNS)算法以确定车辆配送路径;第二阶段采用遗传算法(genetic algorithm,GA)对货物进行三维装载顺序的可行性校验。算例结果表明,基于二分K-means聚类算法的时段划分方法能更精确地计算总成本,从而验证了本文所构建的模型和所设计的算法具有可行性和有效性。展开更多
沙戈荒区域丰富的风光热资源有利于支撑高能耗数据中心集群快速发展,但会使其面临算力负载强时变性、风光出力间歇性及恶劣天气离网运行可靠性的多重挑战。为此,该文提出一种考虑任务负载需求响应及源荷不确定性的数据中心集群微网电-...沙戈荒区域丰富的风光热资源有利于支撑高能耗数据中心集群快速发展,但会使其面临算力负载强时变性、风光出力间歇性及恶劣天气离网运行可靠性的多重挑战。为此,该文提出一种考虑任务负载需求响应及源荷不确定性的数据中心集群微网电-热设备容量协同优化配置方法。首先,根据计算任务对时延的敏感性,精细化建模可推迟可中断、可推迟不可中断及不可推迟3类任务负载的时间约束,在此基础上综合源荷不确定性建立数据中心集群微网“并网-离网”2阶段分布鲁棒优化模型,采用列与约束生成(column and constraint generation,C&CG)算法求解。以青海某实际数据中心为案例的分析结果表明:所提出的方法可使微网容量配置成本下降约25.8%,弃风率下降约56%,并大幅提高数据中心集群微网离网运行可靠性。该文研究为沙戈荒区域绿色低碳数据中心建设提供了理论支撑。展开更多
针对昂贵多目标优化问题(EMOP),尽管已有许多相关算法被提出,但大多数现有算法未能取得令人满意的结果。主要原因是这些算法中的填充采样准则不能很好地平衡选择个体的收敛性、多样性和不确定性。因此,提出一种基于两阶段填充采样的昂...针对昂贵多目标优化问题(EMOP),尽管已有许多相关算法被提出,但大多数现有算法未能取得令人满意的结果。主要原因是这些算法中的填充采样准则不能很好地平衡选择个体的收敛性、多样性和不确定性。因此,提出一种基于两阶段填充采样的昂贵多目标进化算法(TISEMOEA)。在第一阶段,设计一种基于收敛性的填充采样准则,以选择收敛性和多样性都良好的个体,进而平衡收敛性和多样性;在第二阶段,设计一种基于多样性的填充采样准则,在不损害收敛性的前提下选择不确定性较大的个体,进而提高模型的精度和增强种群的多样性。此外,提出一种自适应多样性增强策略,以调整使用基于多样性的填充采样准则选择个体的频率,从而在增强种群多样性的同时平衡算法的探索和开发能力。把TISEMOEA与MOEA/D-EGO(MOEA/D with the Gaussian process model)、HeEMOEA(Heterogeneous Ensemble-based infill criterion for MOEA)、TISS-EMOA(Two-stage Infill Sampling-based Semisupervised EMOA)、PCSAEA(Pairwise Comparison based Surrogate-Assisted Evolutionary Algorithm)以及SFA/DE(Evolutionary multiobjective optimization assisted by scalarization function approximation for high-dimensional expensive problems)这5种先进算法在DTLZ的28个测试问题和WFG的27个测试问题上进行对比实验,并分析反转世代距离(IGD)指标。实验结果显示:TISEMOEA分别在19个和16个测试问题上获得了最佳结果。展开更多
Considering the nonlinea r, time-varying and ripple coupling properties in the hydraulic servo system, a two-stage Radial Basis Function （RBF） neural network model is proposed to realize the failure detection and fa...Considering the nonlinea r, time-varying and ripple coupling properties in the hydraulic servo system, a two-stage Radial Basis Function （RBF） neural network model is proposed to realize the failure detection and fault localization. The first-stage RBF neural network is adopted as a failure observer to realize the failure detection. The trained RBF observer, working concurrently with the actual system, accepts the input voltage signal to the servo valve and the measurements of the ram displacements, rebuilds the system states, and estimates accurately the output of the system. By comparing the estimated outputs with the actual measurements, the residual signal is generated and then analyzed to report the occurrence of faults. The second-stage RBF neural network can locate the fault occurring through the residual and net parameters of the first-stage RBF observer. Considering the slow convergence speed of the K-means clustering algorithm, an improved K-means clustering algorithm and a self-adaptive adjustment algorithm of learning rate arc presented, which obtain the optimum learning rate by adjusting self-adaptive factor to guarantee the stability of the process and to quicken the convergence. The experimental results demonstrate that the two-stage RBF neural network model is effective in detecting and localizing the failure of the hydraulic position servo system.展开更多
Two-stage problem of stochastic convex programming with fuzzy probability distribution is studied in this paper. Multicut L-shaped algorithm is proposed to solve the problem based on the fuzzy cutting and the minimax ...Two-stage problem of stochastic convex programming with fuzzy probability distribution is studied in this paper. Multicut L-shaped algorithm is proposed to solve the problem based on the fuzzy cutting and the minimax rule. Theorem of the convergence for the algorithm is proved. Finally, a numerical example about two-stage convex recourse problem shows the essential character and the efficiency.展开更多
文摘针对时变速度下的低碳配送需求,本文以配送总成本最小化为目标,构建考虑三维装载和时间窗约束的绿色车辆路径优化模型。模型考虑时变速度和实时载重对车辆燃油消耗量的影响。为准确计算行驶时间和油耗,采用二分K-means聚类算法对时段进行合理划分。设计两阶段算法求解模型:第一阶段采用自适应大规模邻域搜索(adaptive large neighborhood search,ALNS)算法以确定车辆配送路径;第二阶段采用遗传算法(genetic algorithm,GA)对货物进行三维装载顺序的可行性校验。算例结果表明,基于二分K-means聚类算法的时段划分方法能更精确地计算总成本,从而验证了本文所构建的模型和所设计的算法具有可行性和有效性。
文摘沙戈荒区域丰富的风光热资源有利于支撑高能耗数据中心集群快速发展,但会使其面临算力负载强时变性、风光出力间歇性及恶劣天气离网运行可靠性的多重挑战。为此,该文提出一种考虑任务负载需求响应及源荷不确定性的数据中心集群微网电-热设备容量协同优化配置方法。首先,根据计算任务对时延的敏感性,精细化建模可推迟可中断、可推迟不可中断及不可推迟3类任务负载的时间约束,在此基础上综合源荷不确定性建立数据中心集群微网“并网-离网”2阶段分布鲁棒优化模型,采用列与约束生成(column and constraint generation,C&CG)算法求解。以青海某实际数据中心为案例的分析结果表明:所提出的方法可使微网容量配置成本下降约25.8%,弃风率下降约56%,并大幅提高数据中心集群微网离网运行可靠性。该文研究为沙戈荒区域绿色低碳数据中心建设提供了理论支撑。
文摘针对昂贵多目标优化问题(EMOP),尽管已有许多相关算法被提出,但大多数现有算法未能取得令人满意的结果。主要原因是这些算法中的填充采样准则不能很好地平衡选择个体的收敛性、多样性和不确定性。因此,提出一种基于两阶段填充采样的昂贵多目标进化算法(TISEMOEA)。在第一阶段,设计一种基于收敛性的填充采样准则,以选择收敛性和多样性都良好的个体,进而平衡收敛性和多样性;在第二阶段,设计一种基于多样性的填充采样准则,在不损害收敛性的前提下选择不确定性较大的个体,进而提高模型的精度和增强种群的多样性。此外,提出一种自适应多样性增强策略,以调整使用基于多样性的填充采样准则选择个体的频率,从而在增强种群多样性的同时平衡算法的探索和开发能力。把TISEMOEA与MOEA/D-EGO(MOEA/D with the Gaussian process model)、HeEMOEA(Heterogeneous Ensemble-based infill criterion for MOEA)、TISS-EMOA(Two-stage Infill Sampling-based Semisupervised EMOA)、PCSAEA(Pairwise Comparison based Surrogate-Assisted Evolutionary Algorithm)以及SFA/DE(Evolutionary multiobjective optimization assisted by scalarization function approximation for high-dimensional expensive problems)这5种先进算法在DTLZ的28个测试问题和WFG的27个测试问题上进行对比实验,并分析反转世代距离(IGD)指标。实验结果显示:TISEMOEA分别在19个和16个测试问题上获得了最佳结果。
文摘Considering the nonlinea r, time-varying and ripple coupling properties in the hydraulic servo system, a two-stage Radial Basis Function （RBF） neural network model is proposed to realize the failure detection and fault localization. The first-stage RBF neural network is adopted as a failure observer to realize the failure detection. The trained RBF observer, working concurrently with the actual system, accepts the input voltage signal to the servo valve and the measurements of the ram displacements, rebuilds the system states, and estimates accurately the output of the system. By comparing the estimated outputs with the actual measurements, the residual signal is generated and then analyzed to report the occurrence of faults. The second-stage RBF neural network can locate the fault occurring through the residual and net parameters of the first-stage RBF observer. Considering the slow convergence speed of the K-means clustering algorithm, an improved K-means clustering algorithm and a self-adaptive adjustment algorithm of learning rate arc presented, which obtain the optimum learning rate by adjusting self-adaptive factor to guarantee the stability of the process and to quicken the convergence. The experimental results demonstrate that the two-stage RBF neural network model is effective in detecting and localizing the failure of the hydraulic position servo system.
文摘Two-stage problem of stochastic convex programming with fuzzy probability distribution is studied in this paper. Multicut L-shaped algorithm is proposed to solve the problem based on the fuzzy cutting and the minimax rule. Theorem of the convergence for the algorithm is proved. Finally, a numerical example about two-stage convex recourse problem shows the essential character and the efficiency.
