多指标动态规划的人机交互式满意权衡法

1984年,Nakayama和Sawaragi提出了一种求解静态多目标决策问题的人机交互式满 意权衡方法.本文结合动态规划的结构特点,进一步发展了Nakayama方法的基本思想,表明 该方法可以推广到用来求解多指标动态规划问题,而且通过对原方法的改进,消除了其存在的 一些不足之处.本文所提方法,在较弱的限制条件下,针对一类普遍使用的多指标动态规划模 型,可以获得决策者充分满意的解.     

基于自回归模型的移动机器人路径规划

文章研究了全局环境未知且存在动态障碍物情况下的移动机器人路径规划问题;采用全局规划和局部规划相结合的方法,提出了动态未知环境下移动机器人的一种在线实时路径规划方法;该法利用自回归模型来预测动态障碍物的运动轨迹,并把预测位置上的动态障碍物视为是瞬时静态的,然后对该"静态"障碍物进行避碰路径规划;仿真实验结果表明该法有效可行,具有优化性、实时规划性、高度的稳定性和良好的避障能力.     

改进动态规划算法的移动机器人路径规划

针对静态栅格环境下的移动机器人全局路径规划问题,通过分析移动机器人到达目标的搜索方向和路径变化的动态特征,分别建立下降路径搜索动态规划模型和上升路径搜索动态规划模型,并依据整列元素路径值变化特点设计了两种模型交互使用的改进动态规划算法。仿真实验结果表明算法具有较好的路径规划效率,可以同时完成多个目标路径规划,且覆盖率越大的环境求解越快速。实验也表明改进动态规划算法同蚁群算法对比能够更快速有效地给出移动机器人较优通行路径。     

基于稀疏A*搜索和改进人工势场的无人机动态航迹规划

针对不同属性的障碍物所构成的威胁分布模型, 本文提出了一种基于稀疏A*搜索算法预规划和改进人工势场相结合的无人机动态避障算法. 该算法首先对威胁分布建立栅格化模型; 然后根据静态威胁, 基于稀疏A*搜索算法进行全局航迹规划; 最后结合预规划路径和动态威胁分布, 利用改进人工势场法完成无人机的动态避障. 仿真结果表明, 该方法能够规划出给定威胁指标下的全局最优路径并达到良好的动态规避性能.     

突发事件下车辆路径问题的动态规划算法

突发事件下的车辆运输具有紧迫性、动态性和随机不确定性等特点.本文研究了突发事件下动态车辆路径问题的数学模型,构建了一种基于混沌优化的动态规划算法,为此通过路径计算和动态规划两个模块来实现车辆路径的动态规划.为实现从混沌运动空间向问题可行解空间的有效映射,提出了相应的编码方法和操作算子.最后进行仿真,通过对静态环境、道路受损和道路拥塞三种情况的分析,验证了实时修订路经的有效性和实用性,为突发事件提供参考.     

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。     

基于多元模糊规划的微粒群优化算法

在微粒群的静态多元规划模式的基础上,考虑到多元最优值对群体寻优的引导因子间的比例在寻优过程中不能进行动态自适应调整,因而将模糊逻辑引入对微粒群的多元规划引导,提出了一种用于自适应动态规划的模糊微粒群算法模式,并以最优和次最优分布信息的模糊规划为例,进行了微粒群多元模糊规划模式的设计和数值仿真.仿真结果表明,该算法模式较静态多元规划模式具有更好的总体收敛性能.     

蚁群算法在机器人路径规划中的应用研究

针对传统机器人路径规划方法无法保证寻找全局最优路径的问题,本文提出了一种基于蚁群算法求解机器人路径规划的方法.在此基础上构建了移动机器人路径规划模型,并通过Visual C 6.0进行仿真.结果表明该算法能够在动态和静态环境中迅速找到机器人的最优路径,与基于遗传算法的路径规划方法相比具有较大的优势.     

电路参数调整的最优化方法研究

针对电路调试工作复杂耗时的问题,提出了一种电路调试的参数最优化方法.首先采用拉氏变换建立电路的数学模型,然后利用数学规划的方法来调整电路参数,使电路达到理想的静态和动态指标.Matlab仿真结果表明,该方法是一种有效的计算机辅助设计方法.     

动态不确定环境下移动机器人的在线实时路径规划

提出一种基于极坐标空间的、以机器人期望运动方向角为路径优化指标的动态不确定环境下移动机器人的在线实时路径规划方法。该法通过机器人的传感器系统,实时探测局部环境信息,在每一采样时刻,机器人首先对视野内的动态障碍物的位置进行采样,然后根据所采样的位置信息,利用自回归模型预测出下一采样时刻动态障碍物的位置,再将预测位置上的动态障碍物当作静态障碍物来处理,然后对其规划避碰路径,从而将动态路径规划转化为静态路径规划。仿真和实验结果验证了该方法有效可行,具有实时规划性和良好的避障能力。     

On solving a multiobjective fixed charge problem with imprecise fractional objectives

The fixed charge problem is a special type of nonlinear programming problem which forms the basis of many industry problems wherein a charge is associated with performing an activity. In real world situations, the information provided by the decision maker regarding the coefficients of the objective functions may not be of a precise nature. This paper aims to describe a solution algorithm for solving such a fixed charge problem having multiple fractional objective functions which are all of a fuzzy nature. The enumerative technique developed not only finds the set of efficient solutions but also a corresponding fuzzy solution, enabling the decision maker to operate in the range obtained. A real life numerical example in the context of the ship routing problem is presented to illustrate the proposed method.     

基于GP的多目标决策函数稳定性分析

在多目标决策问题中，必须设定许多值．由于受到主观因素的影响，不同目标的重要性和可行性方案的效用值有时非常逼近．因此，分析设定值的微小变动对决策结果的敏感度影响尤为重要，根据期望的要求选择一个决策函数，尽可能地求出一个稳定的解，这是求解决策问题的根本任务．提出了一种基于遗传程序设计（GP）的新方法，该方法能产生比普通方法更好的决策函数，理论上的期望值得到实例的验证。     

离散时间最优控制—–评论动态规划

阐述离散时间最优控制的特点.对比3种求解离散时间最优控制的解法,即:1)用非线性规划求解离散时间最优控制;2)用无约束优化求解离散时间最优控制;3)动态规划及其数值解.1)和2)都适用于多维静态优化,计算效率较高,是高级方法.在名义上,3)为动态优化.实际上,3)为一维分段无约束静态优化,计算效率较低,是初级方法.本文并用数字实例进一步阐明动态规划及其数值解在求解方面较差,故动态规划及其数值解已失去实用价值.在求解离散时间最优控制问题方面,无法与非线性规划求解相匹敌.     

Decomposition methods in multiobjective discrete-time dynamic problems

Decomposition methods for multicriteria dynamic (discrete-time) problems are derived. In these methods, the original problem is reduced to a series of multicriteria subproblems related to individual stages. Hence, the dimensionality of decision variables in each subproblem is smaller than in the original problem. The following decomposition procedures for such problems are developed: (1) a dynamic programming method, (2) a two-point boundary value problem method, (3) multilevel methods, and (4) the formulation of a temporal hierarchy. For completeness, methods for multicriteria dynamic problems are reviewed that, at the outset, transform a problem into a series of single-objective problems. Formulation of the multiobjective problem in the context of a multilayer temporal hierarchy is also presented. The temporal structure motivates problem simplification by decomposing the overall decision-making problem according to relative time scales.     

部分权重信息下对方案有偏好的多属性决策法

研究只有部分权重信息且对方案有偏好的多属性决策问题．首先对方案的偏好信息以互反判断矩阵和互补判断矩阵这两种形式给出的情形，分别建立一个目标规划模型，通过求解这两个模型可确定属性的权重；然后提出一种基于目标规划模型的多属性决策方法；最后通过实例说明了该方法的可行性和有效性。     

An interactive approach based on a discrete differential evolution algorithm for a class of integer bilevel programming problems

This paper presents an interactive approach based on a discrete differential evolution algorithm to solve a class of integer bilevel programming problems, in which integer decision variables are controlled by an upper-level decision maker and real-value or continuous decision variables are controlled by a lower-level decision maker. Using the Karush--Kuhn–Tucker optimality conditions in the lower-level programming, the original discrete bilevel formulation can be converted into a discrete single-level nonlinear programming problem with the complementarity constraints, and then the smoothing technique is applied to deal with the complementarity constraints. Finally, a discrete single-level nonlinear programming problem is obtained, and solved by an interactive approach. In each iteration, for each given upper-level discrete variable, a system of nonlinear equations including the lower-level variables and Lagrange multipliers is solved first, and then a discrete nonlinear programming problem only with inequality constraints is handled by using a discrete differential evolution algorithm. Simulation results show the effectiveness of the proposed approach.     

An intuitionistic fuzzy goal programming approach for finding pareto-optimal solutions to multi-objective programming problems

Multi-objective optimization in the intuitionistic fuzzy environment is the process of finding a Pareto-optimal solution that simultaneously maximizes the degree of satisfaction and minimizes the degree of dissatisfaction of an intuitionistic fuzzy decision. In this paper, a new method for solving multi-objective programming problems is developed that unlike other methods in the literature, provides compromise solutions satisfying both the conditions of intuitionistic fuzzy efficiency and Pareto-optimality. This method combines the advantages of the intuitionistic fuzzy sets concept, goal programming, and interactive procedures, and supports the decision maker in the process of solving programming problems with crisp, fuzzy, or intuitionistic fuzzy objectives and constraints. A characteristic of the proposed method is that it provides a well-structured approach for determining satisfaction and the dissatisfaction degrees that efficiently uses the concepts of violation for both objective functions and constraints. Another feature of the proposed method comes from its continuous interaction with the decision maker. In this situation, through adjusting the problem's parameters, the decision maker would have the ability of revisiting the membership and non-membership functions. Therefore, despite the lack of information at the beginning of the solving process, a compromise solution that satisfies the decision maker's preferences can be obtained. A further feature of the proposed method is the introduction of a new two-step goal programming approach for determining the compromise solutions to multi-objective problems. This approach ensures that the compromise solution obtained during each iterative step satisfies both the conditions of intuitionistic fuzzy efficiency and Pareto-optimality. The application of the proposed model is also discussed in this paper.     

Fuzzy heterogeneous multiattribute decision making method for outsourcing provider selection

One of the critical activities for outsourcing success is outsourcing provider selection, which may be regarded as a type of fuzzy heterogeneous multiattribute decision making (MADM) problems with fuzzy truth degrees and incomplete weight information. The aim of this paper is to develop a new fuzzy linear programming method for solving such MADM problems. In this method, the decision maker’s preferences are given through pair-wise alternatives’ comparisons with fuzzy truth degrees, which are expressed with trapezoidal fuzzy numbers (TrFNs). Real numbers, intervals, and TrFNs are used to express heterogeneous decision information. Giving the fuzzy positive and negative ideal solutions, we define TrFN-type fuzzy consistency and inconsistency indices based on the concept of the relative closeness degrees. The attribute weights are estimated through constructing a new fuzzy linear programming model, which is solved by using the developed fuzzy linear programming method with TrFNs. The relative closeness degrees of alternatives can be calculated to generate their ranking order. An example of the IT outsourcing provider selection problem is analyzed to demonstrate the implementation process and applicability of the method proposed in this paper.     

Ant algorithms and simulated annealing for multicriteria dynamic programming

The paper presents a comparison of ant algorithms and simulated annealing as well as their applications in multicriteria discrete dynamic programming. The considered dynamic process consists of finite states and decision variables. In order to describe the effectiveness of multicriteria algorithms, four measures of the quality of the nondominated set approximations are used.