面向节能的单/多列车优化控制研究

主要研究了以节能为目标的单/多列车优化控制问题.对于单列车,建立了以最小化牵引耗能为目标的优化控制模型.采用了逐步修正法对模型进行求解,计算结果表明每公里牵引耗能仅为7.8 kWh.对于多列车,建立了以停车时间为决策变量,最大化再生能量利用为目标的多列车优化控制模型,并采用遗传算法进行了求解.计算结果表明,模型能够节省大约25%-30%的牵引耗能.最后对实际运行中可能出现的延误,给出相应的调整策略,并对其进行了蒙特卡罗模拟.     

基于粒子群算法的面向节能的单/多列车优化决策问题

针对列车运行优化控制问题,首先对列车运行过程的受力情况进行分析,建立了列车运行模型;接着根据最优惰行点选择原则和再生制动节能的方法,确立了单列车区间运行优化模型与多列车节能运行模型;然后采用基于高斯白噪声扰动变异的粒子群算法(GMPSO)求解上述模型得出各情况下列车的最优控制策略;最后探讨了列车延迟情况下的节能调整方案.     

基于模拟退火算法的城市物流多目标配送车辆路径优化研究

基于传统模拟退火算法,通过引入记忆函数、结合GIS手段,运用SPSS聚类分析来确定初始化状态种群、多种群并行机制和新状态的产生.依据种群规模采用不同产生算法来改进算法,并将改进算法应用于城市物流中确定的多目标车辆路径优化问题,验证了算法的可行性与实用价值.此外,改进算法分别与传统模拟退火算法和GIS图解法相比较,优化效率和准确率都得到了很大的提高.     

下层存在多追随者的分层次线性诱导决策问题及算法

本文讨论上层决策给定的条件下,下层存在多追随者的多目标分层次诱 导决策问题．在线性情况下,此类问题的最优解可在有界多面体区域的某个端点实 现;应用罚函数理论,原决策问题转换为一个在有界多面体区域求连续凸函数最大值 的最优化问题．建议采用的计算方法较为简单,容易实现,而且能够保证求出问题的 全局最优解．     

一种改进的GDF多目标优化算法

本文提出一种交互式非线性多目标优化算法，该算法是ＧＤＦ多目标优化算法的改进，具有这样的特点：算法采用了既约设计空间策略，具有良好的收敛性；算法生成的迭代点是有效解；算法具有多种一维搜索准则；对于线性多目标问题，算法只需一次交互迭代即可示出多目标问题的最优解。     

公交车优化调度中的几个问题的研究

本文将 2 0 0 1 CMCM B题公交车调度的最优化问题分成两个相对独立的问题来讨论 ,由所要运送的乘客数来决定公交公司的最小车辆数 ,由乘客到达来决定发车时刻表 .我们求出了最小车辆数和一个可行的最优时刻表 .     

弹性需求下城市公交网络服务的优化

对具有弹性需求的城市公交网络系统进行了票价结构与发车频率组合的优化。考虑到公交定价和发车频率会影响乘客需求以及乘客对路径的选择行为,将这一问题描述为一个双层规划问题,上层是寻求社会福利最大的优化问题;下层考虑了乘客的出行选择行为,为弹性需求下乘客在城市公交网络上流量分布的随机用户平衡分配模型。鉴于双层规划问题的非凸性,运用模拟退火算法对模型进行求解,并给出一个仿真算例说明提出的模型和算法的合理性。     

模拟退火猫群优化算法

猫群优化算法(Cat Swarm Optimization,CSO)是建立在猫的行为模式和群体智能基础上的一种新型群体智能优化算法。为提高猫群优化算法的性能,把模拟退火算法应用于猫群优化算法,提出模拟退火猫群优化算法(Simulated Annealing Cat Swarm Optimization,SACSO),通过变异算子调整所要优化的种群。其基本过程为先行产生随机初始种群,接着进行搜索,并设置初始温度,继而应用模拟退火算法获取全局最优替代值,再依据位置和速度公式更新新解,然后在个体较优位置再运用变异运算,进行进一步地搜索。然后分别将猫群优化算法、模拟退火粒子群算法(Simulated Annealing Particle Swarm Optimization,SAPSO)、模拟退火猫群优化算法在11个典型的基准测试函数下进行仿真对比,结果表明模拟退火猫群优化算法不仅增加了全局收敛性,而且在收敛速度和精度方面均优于其它两种算法。     

基于模拟退火算法的矩形优化排样问题的研究

考虑了一种矩形优化排样系统中遗传算法和模拟退火算法的结合算法.首先建立了该系统的通用数学模型.然后给出了求解该问题的遗传模拟退火算法.最后用VC++6.0模拟算例的结果表明该算法是一种行之有效的方法.     

求群体多目标决策问题的一种可行方向交互算法

对于群体多目标决策问题,决策者可以各自的关于目标之间的权衡比表达其偏爱信息并进行决策.当个体权衡比具有加性性质时可得群体权衡比.本文以此构造一种求解群体非线性规划问题的交互算法.迭代中基于求解决非线性规划的Topkis-Veinott方法构造可行方向.在一定的条件下,算法收敛于所讨论问题的群体满意解.     

三维水平井轨道设计模糊最优控制模型

建立了三维水平井井眼轨道设计模糊非线性多目标最优控制模型 ,利用模糊集理论把该模型转化为非线性规划问题 ,并把该模型应用到水平井的实际生产中 ,得到满意的结果 .     

一种具有非线性约束线性规划全局优化算法

本文提出了一种新的适用于处理非线性约束下线性规划问题的全局优化算法。该算法通过构造子问题来寻找优于当前局部最优解的可行解。该子问题可通过模拟退火算法来解决。通过求解一系列的子问题,当前最优解被不断地更新,最终求得全局最优解。最后,本算法应用于几个典型例题,并与罚函数法相比较,数值结果表明该算法是可行的,有效的。     

A Globally Convergent and Efficient Method for Unconstrained Discrete-Time Optimal Control

Shift schemes are commonly used in non-convex situations when solving unconstrained discrete-time optimal control problems by the differential dynamic programming (DDP) method. However, the existing shift schemes are inefficient when the shift becomes too large. In this paper, a new method of combining the DDP method with a shift scheme and the steepest descent method is proposed to cope with non-convex situations. Under certain assumptions, the proposed method is globally convergent and has q-quadratic local conve rgence. Extensive numerical experiments on many test problems in the literature are reported. These numerical results illustrate the robustness and efficiency of the proposed method.     

Efficient Sequential Quadratic Programming Implementations for Equality-Constrained Discrete-Time Optimal Control

Efficient sequential quadratic programming (SQP) implementations are presented for equality-constrained, discrete-time, optimal control problems. The algorithm developed calculates the search direction for the equality-based variant of SQP and is applicable to problems with either fixed or free final time. Problem solutions are obtained by solving iteratively a series of constrained quadratic programs. The number of mathematical operations required for each iteration is proportional to the number of discrete times N. This is contrasted by conventional methods in which this number is proportional to N ³. The algorithm results in quadratic convergence of the iterates under the same conditions as those for SQP and simplifies to an existing dynamic programming approach when there are no constraints and the final time is fixed. A simple test problem and two application problems are presented. The application examples include a satellite dynamics problem and a set of brachistochrone problems involving viscous friction.     

The Optimal Control of a Train

We consider the problem of determining an optimal driving strategy in a train control problem with a generalised equation of motion. We assume that the journey must be completed within a given time and seek a strategy that minimises fuel consumption. On the one hand we consider the case where continuous control can be used and on the other hand we consider the case where only discrete control is available. We pay particular attention to a unified development of the two cases. For the continuous control problem we use the Pontryagin principle to find necessary conditions on an optimal strategy and show that these conditions yield key equations that determine the optimal switching points. In the discrete control problem, which is the typical situation with diesel-electric locomotives, we show that for each fixed control sequence the cost of fuel can be minimised by finding the optimal switching times. The corresponding strategies are called strategies of optimal type and in this case we use the Kuhn–Tucker equations to find key equations that determine the optimal switching times. We note that the strategies of optimal type can be used to approximate as closely as we please the optimal strategy obtained using continuous control and we present two new derivations of the key equations. We illustrate our general remarks by reference to a typical train control problem.     

The Existence of Optimal Solutions for a Kind of Large-Scale Goal Proramming

In this paper, we shall decompose the large-scale goal programming model with couple-block diagnal structure into several subproblems. We will also propose the definition of deviation sets. Relations of optimal solution between sub-problems and the large-scale problem will be discussed. The existence of optimal solution for large-scale goal programming will be considered.Research is supported by a grant of the National Natural Science Foundation of China.     

Optimal control synthesis in therapy of solid tumor growth

A mathematical model of tumor growth therapy is considered. The total amount of a drug is bounded and fixed. The problem is to choose an optimal therapeutic strategy, i.e., to choose an amount of the drug permanently affecting the tumor that minimizes the number of tumor cells by a given time. The problem is solved by the dynamic programming method. Exact and approximate solutions to the corresponding Hamilton-Jacobi-Bellman equation are found. An error estimate is proved. Numerical results are presented.     

Efficient dynamic programming implementations of Newton's method for unconstrained optimal control problems

Naive implementations of Newton's method for unconstrainedN-stage discrete-time optimal control problems with Bolza objective functions tend to increase in cost likeN ³ asN increases. However, if the inherent recursive structure of the Bolza problem is properly exploited, the cost of computing a Newton step will increase only linearly withN. The efficient Newton implementation scheme proposed here is similar to Mayne's DDP (differential dynamic programming) method but produces the Newton step exactly, even when the dynamical equations are nonlinear. The proposed scheme is also related to a Riccati treatment of the linear, two-point boundary-value problems that characterize optimal solutions. For discrete-time problems, the dynamic programming approach and the Riccati substitution differ in an interesting way; however, these differences essentially vanish in the continuous-time limit.This work was supported by the National Science Foundation, Grant No. DMS-85-03746.     

Optimal Hysteretic Control for the BMAP/G/ 1 System with Single and Group Service Modes

In this paper, we consider a single server queuing model with an infinite buffer in which customers arrive according to a batch Markovian arrival process (BMAP). The services are offered in two modes. In mode 1, the customers are served one at a time and in mode 2 customers are served in groups of varying sizes. Various costs for holding, service and switching are imposed. For a given hysteretic strategy, we derive an expression for the cost function from which an optimal hysteretic control can be obtained. Illustrative numerical examples are presented.