基于乘性规则的支持向量机

传统的二次规划由于涉及大量的矩阵运算，运算速度慢成为支持向量机的最大缺点．已有的乘性规则仅适于非负二次凸规划问题，推导出了求解支持向量机中混合约束二次凸规划的乘性规则，利用这一乘性规则极大地提高了优化速度．该方法提供了一种直接优化的方法，其所有变量可以并行迭代，乘性规则可以使得二次规划的目标函数单调下降到它的全局最小点．仿真试验结果表明了该算法有效性．     

基于分块矩阵的投影型神经网络收敛性分析*

投影神经网络算法被誉为最有希望解决优化问题的算法之一,可用于求解优化问题的前提是它应具有全局收敛性。根据凸二次规划约束条件的特点,利用常微分方程理论、M-矩阵理论,通过构造适当的Lyapunov函数,获得了该网络求解一类凸二次规划问题的全局指数收敛性条件,该条件只与神经元连接权矩阵的部分元素有关,其比现有文献所得的收敛条件更弱。最后给出一组实例,说明该网络计算上是可行和有效的。     

凸近似避障及采样区优化的智能车辆轨迹规划

针对结构化道路下作匀速运动的智能车辆避障轨迹规划问题，提出一种基于凸近似避障原理及采样区域优化的智能车辆轨迹规划方法。引入凸近似避障原理，得到轨迹可行域范围；将采样区域分为静态采样区、动态采样区两部分，并根据障碍物运动状态，另外划分动态、静态障碍物采样区；采用“动态规划（DP）+二次规划（QP）”思想求解轨迹：利用五次多项式对采样点依次连接，建立动态规划代价函数并筛选得到粗略轨迹；通过二次规划及约束条件的构造，对粗略轨迹进行平滑，最终得到最优轨迹。仿真结果表明：对于静态、低速、动态三种障碍物，该车能够有效地得到平滑轨迹并避开障碍物。     

求解一类特殊的双层规划问题的遗传算法

主要研究上层函数及其约束函数不要求具有凸性和可微性,下层是关于下层决策变量是凸二次规划的双层规划模型,通过Karush-Kuhn-Tucher 条件转化为一个单层规划,利用下层是正定二次规划,将下层的决策变量表示为关于 Lagrangian乘子的表达式,从而降低了搜索空间的维数,设计了遗传算法,并通过数值实验表明该遗传算非常有效。     

H2/ l1 混合优化问题的凸二次规划解法

采用上逼近算法求解H1/l1混合优化问题,首先将其转化为有限维的凸二次规划问题,并利用Lemke互补转轴算法求解;然后逐次进行逼近,计算请示例表明所得结果是正确的。     

H_2/l_1混合优化问题的凸二次规划解法

采用上逼近算法求解 H2 / l1 混合优化问题。首先将其转化为有限维的凸二次规划问题 ,并利用L emke互补转轴算法求解 ;然后逐次进行逼近。计算示例表明所得结果是正确的     

等式约束凸二次规划解析的新型神经网络方法*

针对不同于传统基于梯度法的递归神经网络定义一种基于标量范数取值的非负能量函数,通过定义一种基于向量取值的不定无界的误差函数,构建了一种能实时求解具有线性等式约束的凸二次规划问题。基于Simulink仿真平台的计算机实验结果表明,该新型神经网络模型能够准确有效地求解此类二次规划问题。     

小型无人直升机的模型预测控制算法研究

针对无人直升机在阵风干扰环境中的姿态控制精度低的问题.本文将非线性刚体动力学模型在悬停点应用小扰动理论得到了线性化数学模型.考虑系统输入输出和控制量约束,采用模型预测控制将控制器的设计问题转化为每个采样时刻求解一个带不等式和等式约束的凸二次规划问题.通过设计终端状态约束解决了有限时域模型预测控制(model predictive control, MPC)算法的稳定性问题,并通过引入松弛变量使得约束优化问题更容易求解.随机和常值阵风干扰下无人机悬停仿真验证了本文MPC预测控制器具有幅度不超过0.25 m/s的良好干扰抑制能力,性能明显优于线性二次型调节器(linear-quadratic regulator, LQR).     

基于信赖域二次规划的非线性模型预测控制优化算法

针对非线性预测控制如何在有限时域内有效的求解非凸非线性规划这一关键问题, 本文采用序列二次规划方法, 将非线性规划转化为一系列二次子规划求解. 首先根据非线性规划联立方法将系统状态和控制量同时作为优化变量, 得到以控制量步长为优化变量, 只包含不等式约束的子二次规划问题, 并用它取代原SQP子规划, 减小了子问题的规模; 随后采用基于信赖域二次规划的方法求解子规划问题, 保证每次迭代的可行性; 同时采用一种能够保持SQP问题Hessian矩阵稀疏结构的更新方法, 也在一定程度上降低了算法的复杂程度.最后的仿真结果表明了该方法的有效性.     

一种基于FPGA的二次规划求解方法

二次规划是一类重要的优化问题,许多工程上的优化问题都可以归结为二次规划的求解,如：预测控制,最小二乘等。嵌入式技术的发展使得嵌入式系统广泛地应用于人类生活的方方面面。由于二次规划的求解需要大量的计算,因此在传统基于ARM的嵌入式平台上实现比较困难。FPGA并行计算,硬件加速的特点使得在嵌入式平台上快速求解二次规划成为可能。本文主要介绍了一种基于FPGA的二次规划求解算法,首先介绍了二次规划的求解算法,然后将浮点算法改为定点算法,再采用Impulse C将定点算法变为HDL语言,最后通过实例验证了此方法的正确性。     

A neural network approach for solving linear bilevel programming problem

A novel neural network approach is proposed for solving linear bilevel programming problem. The proposed neural network is proved to be Lyapunov stable and capable of generating optimal solution to the linear bilevel programming problem. The numerical result shows that the neural network approach is feasible and efficient.     

基于Pareto支配的双目标优化求解非线性双层规划问题

双层规划问题是一类具有双层递阶结构的系统优化问题。采用Pareto支配的双目标优化策略求解非线性双层规划问题。利用K-T条件把双层规划问题等价转化单层规划问题,进而结合约束部分建立可行性度量目标形成双目标规划问题。在基本的差分进化算法框架中融入非负的最小二乘曲线拟合判断候选解的可行性,构造基于动态概率的Pareto支配选择策略挑选下一代个体,解决种群容易陷入局部最优的缺陷。15个标准函数的测试结果对比显示,该算法在求解非线性双层规划问题中具有较好的全局寻优能力、较低的计算复杂度、较强的稳定性和适用性,可以获得全局最优解。     

A neural network approach for solving nonlinear bilevel programming problem

A neural network model is presented for solving nonlinear bilevel programming problem, which is a NP-hard problem. The proposed neural network is proved to be Lyapunov stable and capable of generating approximal optimal solution to the nonlinear bilevel programming problem. The asymptotic properties of the neural network are analyzed and the condition for asymptotic stability, solution feasibility and solution optimality are derived. The transient behavior of the neural network is simulated and the validity of the network is verified with numerical examples.     

Solving a type of biobjective bilevel programming problem using NSGA-II

This paper considers a type of biobjective bilevel programming problem, which is derived from a single objective bilevel programming problem via lifting the objective function at the lower level up to the upper level. The efficient solutions to such a model can be considered as candidates for the after optimization bargaining between the decision-makers at both levels who retain the original bilevel decision-making structure. We use a popular multiobjective evolutionary algorithm, NSGA-II, to solve this type of problem. The algorithm is tested on some small-dimensional benchmark problems from the literature. Computational results show that the NSGA-II algorithm is capable of solving the problems efficiently and effectively. Hence, it provides a promising visualization tool to help the decision-makers find the best trade-off in bargaining.     

Approximated set-valued mapping approach for handling multiobjective bilevel problems

A significant amount of research has been done on bilevel optimization problems both in the realm of classical and evolutionary optimization. However, the multiobjective extensions of bilevel programming have received relatively little attention from researchers in both the domains. The existing algorithms are mostly brute-force nested strategies, and therefore computationally demanding. In this paper, we develop insights into multiobjective bilevel optimization through theoretical progress made in the direction of parametric multiobjective programming. We introduce an approximated set-valued mapping procedure that would be helpful in the development of efficient evolutionary approaches for solving these problems. The utility of the procedure has been emphasized by incorporating it in a hierarchical evolutionary framework and assessing the improvements. Test problems with varying levels of complexity have been used in the experiments.     

两层多目标规划的罚函数法

研究了一类非线性两层多目标规划问题.在下层多目标规划问题的目标函数是严格凸函数、决策变量约束集是凸集的假设下,通过将两层多目标规划问题转化成一系列单层多目标规划问题,建立了两层多目标规划的罚函数理论,并进行了收敛性分析.从而丰富了两层多目标规划的理论,为解决实际中的两层多目标决策问题提供了有力的工具.     

求解二层规划的混合微粒群算法

对于二层规划问题有许多经典的求解方法,如极点搜索法、分支定界法和罚函数法等。文中给出了基于微粒群算法的二层规划的一种新的求解方法。提出了分别先用单纯形法和内部映射牛顿法的子空间置信域法求解下层规划,然后用微粒群算法求解上层规划的求解方法,这两种混合微粒群算法分别用于求解线性二层规划和非线性二层规划。并结合实例的对比分析,说明了这两种混合微粒群算法求解二层规划的可行性和有效性。     

灰色二层多目标线性规划问题及其解法

针对二层多目标线性规划问题,结合灰色系统的特性,提出了一般灰色二层多目标线性规划问题,并给出了模型的相关定义和定理.针对漂移型灰色二层多目标线性规划问题,提出一种具有全局收敛性质的求解算法.首先通过线性加权模理想点法把多目标转化为单目标;然后当可行域为非空紧集时,利用库恩塔克条件把双层转化为单层,再利用粒子群算法搜索单目标单层线性规划即可得到原问题的解;最后通过算例表明了该算法的有效性.     

An approximate programming method based on the simplex method for bilevel programming problem

Many real problems can be modeled to the problems with a hierarchical structure, and bilevel programming is a useful tool to solve the hierarchical optimization problems. So the bilevel programming is widely applied, and numerous methods have been proposed to solve this programming. In this paper, we propose an approximate programming algorithm to solve bilevel nonlinear programming problem. Finally, the example illustrates the feasibility of the proposed algorithm.     

A study on the use of heuristics to solve a bilevel programming problem

The bilevel programming problem is characterized as an optimization problem that has another optimization problem in its constraints. The leader in the upper level and the follower in the lower level are hierarchically related where the leader's decisions affect both the follower's payoff function and allowable actions, and vice versa. One difficulty that arises in solving bilevel problems is that unless a solution is optimal for the lower level problem, it cannot be feasible for the overall problem. This suggests that approximate methods could not be used for solving the lower level problem, as they do not guarantee that the optimal solution is actually found. However, from the practical point of view near‐optimal solutions are often acceptable, especially when the lower level problem is too costly to be exactly solved thus rendering the use of exact methods impractical. In this paper, we study the impact of using an approximate method in the lower level problem, discussing how near‐optimal solutions on the lower level can affect the upper level objective function values. This study considers a bilevel production‐distribution planning problem that is solved by two intelligent heuristics hierarchically related: ant colony optimization for solving the upper level problem, and differential evolution method to solve the lower level problem.