多目标的分布式协同进化MDO算法

通过引入非优超排序和排挤的多目标处理机制 ,将分布式协同进化MDO算法的能力扩展到多目标的多学科设计优化问题。多目标的分布式协同进化MDO算法在保持各学科充分自治和各学科并行设计优化协同的基础上 ,通过一次运行即可获得具有良好分布的多个Pareto最优解 ,逼近整个Pareto最优前沿。应用于导弹气动 /发动机 /控制三学科两目标设计优化问题 ,与约束法计算结果的对比表明算法能够有效逼近该问题的Pareto最优前沿 ,为设计决策提供了丰富的信息     

稳态随机过程激励下基于首次超越破坏的结构优化设计

把结构系统动力可靠性分析与最优化设计结合起来 ,以结构系统的最小质量为目标函数 ,给出了考虑在平稳随机过程激励下多自由度线性系统总的可靠性的结构优化设计方法。运用谱分析理论 ,推导了结构系统在平稳随机过程激励下响应的统计特征 ,同时结合首次超越破坏的Possion模型计算结构系统的可靠性 ,最终采用广义乘子法得到结构系统设计变量的最优值。计算结果表明该方法是可行的     

飞行器的轨道优化与制导规律研究综述

飞行器的轨道优化与制导规律研究是飞行器设计的重要课题之一。轨道优化与制导规律研究是根据给定的技术指标 ,建立飞行器的运动方程 ,并选择主要设计参数 ,构造性能泛函 ,运用现代控制理论及数学理论原理求解最优参数 ,形成制导规律与相应的飞行器飞行轨道     

关于Reich的公开问题

设E是具有一致G -可微范数的实Banach空间 ,D是E的非空闭凸子集 ,T :D→D是非扩张映象 ,F(T)非空。设 {αn} ,{ βn}是 [0 ,1]中满足一定条件的两个序列 ,定义压缩映象St:D→D为 :St(z) =(1-t)x tTz , x ,z∈D , n≥ 1,t∈ (0 ,1) .设zt 是St 的唯一不动点 ,若当t→ 1-时 ,{zt}强收敛于某点z∈F(T) .那么 ,Reich序列 {xn}强收敛于某点z∈F(T) .     

基于马尔柯夫决策过程动态WTA最优化模型分析

在作战过程中,通过把目标群的威胁程度作为目标函数制定决策,可将目标分配决策过程变成马尔柯夫决策过程.即首先对攻防作战态势进行抽象性描述,建立基于马尔柯夫决策过程动态WTA最优化模型,然后提出了有限次迭代求解最优策略的新方法,并进行了算例验证和分析.结果表明该方法可使武器系统获得最大的射击效率,并能有效地减少重点目标突防的概率.     

攻击机靶场效能优化:概念、方法与基本问题

系统阐述了攻击机靶场攻击、靶场效能以及靶场效能优化等基本概念,在此基础上,分析了攻击机靶场效能优化的基本任务和基本问题,并给出了形式化描述。     

Partitioning of biweighted trees

A districting problem is formulated as a network partitioning model where each link has one weight to denote travel time and another weight to denote workload. The objective of the problem is to minimize the maximum diameter of the districts while equalizing the workload among the districts. The case of tree networks is addressed and efficient algorithms are developed when the network is to be partitioned into two or three districts. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 143–158, 2002; DOI 10.1002/nav.10003     

天地测控资源调度的两阶段递进遗传算法

为对天地测控资源调度进行高效求解,设计了两阶段递进遗传算法对其进行优化调度。鉴于问题的目标具有一定的可分性,以时间为依据将被调度弧段划分成两个部分,在对第一部分形成种群并作进化求解的基础上,将其最优解与第二部分弧段组合,并作进一步的进化求解。为了缓解两阶段优化的局部搜索特性,在第一阶段个体适应度计算中,以一定概率设定虚拟弧段,保留具有潜在全局优势的个体。仿真表明该方法能在确保求解质量的前提下明显减少运算时间。     

Adaptive random search for continuous simulation optimization

We present, analyze, and compare three random search methods for solving stochastic optimization problems with uncountable feasible regions. Our adaptive search with resampling (ASR) approach is a framework for designing provably convergent algorithms that are adaptive and may consequently involve local search. The deterministic and stochastic shrinking ball (DSB and SSB) approaches are also convergent, but they are based on pure random search with the only difference being the estimator of the optimal solution [the DSB method was originally proposed and analyzed by Baumert and Smith]. The three methods use different techniques to reduce the effects of noise in the estimated objective function values. Our ASR method achieves this goal through resampling of already sampled points, whereas the DSB and SSB approaches address it by averaging observations in balls that shrink with time. We present conditions under which the three methods are convergent, both in probability and almost surely, and provide a limited computational study aimed at comparing the methods. Although further investigation is needed, our numerical results suggest that the ASR approach is promising, especially for difficult problems where the probability of identifying good solutions using pure random search is small. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

A stochastic gradual cover location problem

Covering models assume that a point is covered if it is within a certain distance from a facility and not covered beyond that distance. In gradual cover models it is assumed that a point is fully covered within a given distance from a facility, then cover gradually declines, and the point is not covered beyond a larger distance. Gradual cover models address the discontinuity in cover which may not be the correct approach in many situations. In the stochastic gradual cover model presented in this article it is assumed that the short and long distances employed in gradual cover models are random variables. This refinement of gradual cover models provides yet a more realistic depiction of actual behavior in many situations. The maximal cover model based on the new concept is analyzed and the single facility location cover problem in the plane is solved. Computational results illustrating the effectiveness of the solution procedures are presented. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010