复杂环境下基于多目标粒子群的DWA路径规划算法

针对机器人在障碍物分布密集的复杂环境中运行时,动态窗口法(dynamic window approach,DWA)易出现避障失败或规划不合理的情况,提出一种基于多目标粒子群优化算法(multi-objective particle swarm optimization,MOPSO)的改进DWA规划算法。在建立多障碍物环境覆盖模型的基础上,提出一种障碍物密集度的判断方法;优化DWA算法中的子评价函数;利用改进的MOPSO算法实现DWA权重系数的动态调整,将权重系数的自适应变化问题转化为多目标优化问题;根据路径规划的要求将安全距离和速度作为优化目标,并使用改进的MOPSO算法对相应的多目标优化模型进行优化求解。仿真结果表明,该算法使机器人有效地通过障碍〖BHDWG8,WK10YQ,DK1*2,WK1*2D〗〖XCLXY.TIF;%129%129〗听语音 聊科研与作者互动 物密集区的同时兼顾了运行的安全性和速度,具有更好的路径规划效果。     

防空监视网络传感器资源分配的最优化

针对防空监视网络的传感器管理问题,讨论了传感器资源分配的最优化方法。提出了把传感器资源分配问题映射为多代理系统分布约束最优化问题的解决策略,设计了基于约束代价下界搜索的异步分枝定界最优化算法,实现了传感器资源分配问题最优解的异步并行搜索,给出的仿真实例说明了传感器资源分配最优化方法的有效性。     

防空导弹兵力规划模型

借助兵力交换的思想,从宏观层次上研究不同类型的防空导弹火力单元对空中目标的优化分配问题;分析在最优目标分配策略下区域防空中各类型防空导弹火力单元的火力分配问题,并建立了递阶优化的求解模型.该模型可为区域防空部署的决策与分析提供科学依据.     

基于火灾风险分析的消防力量优化布局

分析了我国目前消防力量布局存在的问题,从当前有限消防经费和现有的城市布局出发,提出了依据风险来优化消防力量布局的观点和方法,以最大限度的提高消防部队的灭火和抢险救援能力。     

防空兵群(团)火力配系优化

首先阐明了防空兵群(团)火力配系优化的基本概念,然后,运用作战模拟的有关理论,分别建立了火力单位距目标的最佳配置距离(D)、防空兵群(团)对来袭目标的抗击率(Pk)和被保护目标的安全率(Pa)的优化模型。在此基础上,提出了防空兵群(团)火力配系优化的一般方法。最后通过一个实例,说明了防空兵群(团)火力配系的具体步骤。     

主维对矩阵运算性能的影响

随着计算机体系结构的发展,高速缓存（ｃａｃｈｅ）的引入,分块方法成为矩阵计算中性能优化的主要方法,而矩阵主维对分块算法的性能影响很大。本文分析了矩阵主维影响性能的原因以及如何选取主维来改善性能,并与拷贝方法进行了比较。最后用矩阵乘法和ＬＵ分解进行了试算,取得了满意的结果     

递进优化技术在汽油机工作过程优化计算中的应用

采用数学规化的方法优化汽油机的工作过程,可以在限制发动机热负荷、机械负荷、氧化氮排放和不爆震的条件下,使发动机在运行过程中经济性最好.但是要求得比较精确的优化解,一般要进行大量计算,花费较多机时.本文提出了一种分段、分层、分级的递进优化方法,应用于汽油机工作过程的优化计算中,取得了较好的效果.     

Minimizing sums and products of linear fractional functions over a polytope

In this paper, we develop efficient deterministic algorithms for globally minimizing the sum and the product of several linear fractional functions over a polytope. We will show that an elaborate implementation of an outer approximation algorithm applied to the master problem generated by a parametric transformation of the objective function serves as an efficient method for calculating global minima of these nonconvex minimization problems if the number of linear fractional terms in the objective function is less than four or five. It will be shown that the Charnes–Cooper transformation plays an essential role in solving these problems. Also a simple bounding technique using linear multiplicative programming techniques has remarkable effects on structured problems. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 583–596, 1999     

Probabilistic analysis of an asymptotically optimal solution for the completion time variance problem

Scheduling a set of n jobs on a single machine so as to minimize the completion time variance is a well‐known NP‐hard problem. In this paper, we propose a sequence, which can be constructed in O(n log n) time, as a solution for the problem. Our primary concern is to establish the asymptotical optimality of the sequence within the framework of probabilistic analysis. Our main result is that, when the processing times are randomly and independently drawn from the same uniform distribution, the sequence is asymptotically optimal in the sense that its relative error converges to zero in probability as n increases. Other theoretical results are also derived, including: (i) When the processing times follow a symmetric structure, the problem has 2^{⌊(n−1)/2⌋} optimal sequences, which include our proposed sequence and other heuristic sequences suggested in the literature; and (ii) when these 2^{⌊(n−1)/2⌋} sequences are used as approximate solutions for a general problem, our proposed sequence yields the best approximation (in an average sense) while another sequence, which is commonly believed to be a good approximation in the literature, is interestingly the worst. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 373–398, 1999     

Coordinated replenishments from multiple suppliers with price discounts

In this study we present an integer programming model for determining an optimal inbound consolidation strategy for a purchasing manager who receives items from several suppliers. The model considers multiple suppliers with limited capacity, transportation economies, and quantity discounts. We propose an integrated branch and bound procedure for solving the model. This procedure, applied to a Lagrangean dual at every node of the search tree, combines the subgradient method with a primal heuristic that interact to change the Lagrangean multipliers and tighten the upper and lower bounds. An enhancement to the branch and bound procedure is developed using surrogate constraints, which is found to be beneficial for solving large problems. We report computational results for a variety of problems, with as many as 70,200 variables and 3665 constraints. Computational testing indicates that our procedure is significantly faster than the general purpose integer programming code OSL. A regression analysis is performed to determine the most significant parameters of our model. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 579–598, 1998