电子侦察卫星联合侦察的混合调度算法

针对电子侦察卫星的使用约束,及不同任务的调度需求,建立了电子侦察卫星联合侦察的多目标混合整数规划模型.利用进化算法的全局搜索能力和变邻域搜索的局部优化能力,提出了一种多目标进化算法和变邻域搜索相结合两阶段混合调度算法MOEA VNS.针对问题多时间窗组合优化特点,设计了进化算子与邻域移动算子,在确保解多样性的同时使算法...     

光学成像侦察卫星作战效能分析

为支持航天装备体系作战效能评估,建立了光学成像侦察卫星性能指标与卫星发现目标概率和定位精度的关系以及信息处理与传递模型。以支持导弹打击大型移动目标为例,以命中目标概率为作战效能指标,建立了光学成像侦察卫星作战效能模型。对模型进行了仿真,分析了影响卫星作战效能的主要因素。仿真结果表明所建模型能够合理反映光学成像侦察卫星对作战结果的影响,可用于从顶层分析航天装备。     

基于MOEO的电子侦察卫星多目标规划方法

分析了电子侦察卫星任务规划问题的内涵,基于合理假设建立了问题的多目标规划模型;设计了一种基于MOEO(Multi-objective Extremal Optimization)的多目标极值优化算法对模型进行求解,提出了一种针对电子侦察卫星规划问题的随机交换变异算子对解空间进行搜索;同时,为防止Pareto近似最优解的丢失,引入了外部精英种群策略对其进行保留。最后通过仿真实例验证了该算法能够有效解决电子侦察卫星任务规划问题。     

光学成像侦察卫星威胁评估方法

未来作战中地面部队将面临越来越多来自空间的威胁,其中最突出的是侦察卫星威胁。以光学成像侦察卫星为例,进行了卫星威胁评估方法研究。首先探讨了卫星图像品质评价方法和影响因素,建立了侦察卫星对地面目标的检测概率模型,并引入光照、气象、对比度影响因子对检测概率进行修正。在此基础上考虑军事任务需求建立了针对目标尺寸已知情况下的威胁度计算模型,进一步按照目标大小划分威胁等级,给出了卫星威胁等级评价方法,所得到的威胁等级指标可为军事行动提供参考。最后给出了光学侦察卫星威胁评估应用实例。     

光学成像侦察卫星威胁特性分析与仿真研究北大核心

为有效评估光学成像侦察卫星对地面目标的威胁,在对光学成像侦察卫星的成像过程以及影响其成像效果的成像条件进行分析的基础上,建立了基于目标检测概率的卫星威胁特性分析和预报模型,划分了威胁等级。通过STK/Matlab联合仿真,实时显示卫星在轨运行情况以及卫星对目标区域的覆盖信息,计算并输出在过境时间段内光学成像侦察卫星的威胁结果。仿真结果表明,该方法可提供更可靠和直观的光学成像侦察卫星威胁结果。     

基于约束满足的多目标对地观测卫星成像调度

EOS(对地观测卫星)成像调度需要根据用户提出的成像任务需求确定卫星成像序列,是一个复杂的组合优化问题。考虑到成像侧视约束条件,建立EOS成像调度的多目标有向无环约束图模型。在此模型的基础上提出了基于SPEA2(strength pareto evolutionary algorithm 2)的多目标成像调度算法,采用约束控制技术设计遗传算子处理成像约束。经过三个实际的多目标成像调度问题测试,表明该算法可以有效地解决EOS成像调度问题。     

带数据传输的光学卫星成像调度系统关键技术研究

针对光学小卫星成像调度系统设计需求,考虑侧视、存储容量、能量和数据传输等复杂约束,面向小规模问题应用,设计了问题求解流程.建立了顶点和边都带权的成像约束图模型,并提出了基于标记更新最短路算法的复杂约束成像卫星调度算法解决成像方案生成过程;对数传方案生成过程,给出背包模型并采用带回看策略的贪婪启发式方法进行问题求解.实验结果表明,该方法是可行和适用的.     

基于可执行模型架构的成像侦察卫星系统建模与仿真

成像侦察卫星系统内部关系复杂、任务需求种类较多,利用基于町执行模型架构的系统工程对其进行建模研究,可以有效地实现从模型到仿真的综合集成．通过研究可执行模型架构理论,集成UPDM与成像侦察卫星系统功能模型,对成像侦察卫星系统进行了建模与仿真,验证了可执行模型架构的有效性与实用性．     

光学侦察卫星的目标探测概率分析

结合光学侦察卫星的特点建立了光学成像卫星目标探测概率与卫星轨道倾角、探测幅宽、访问间隔、目标特性等因素的关系模型。该模型明确了卫星技术性能指标和作战性能指标的相互关系,可作为对光学侦察卫星进行需求分析、顶层设计以及效能评估的依据。以大型舰船为探测目标,用仿真结果说明了模型的适用性。     

通信对抗侦察装备分配优化问题仿真

定量研究通信对抗侦察装备分配优化问题能够为增大侦察效率提供帮助。首先,建立了通信对抗侦察装备需求估算模型;然后,结合搜索论、非线性规划及不确定规划等方法,分别从时间、频段和数量3个角度建立了相关的区域分配优化模型。最后,使用混合智能算法进行了实例的仿真计算,分析了计算结果。研究成果对侦察方案的制定具有辅助决策作用。     

A metaheuristic scheduling procedure for resource-constrained projects with cash flows

Resource-constrained project scheduling problems with cash flows (RCPSPCF) are complex, combinatorial optimization problems. Many heuristics have been reported in the literature that produce reasonable schedules in limited project environments. However, the lack of a heuristic that dominates under differing project conditions can lead to a suboptimal choice of an appropriate heuristic for scheduling any given project. This may result in poor schedules and monetary losses. This paper reports on the application of the tabu search metaheuristic procedure for the RCPSPCF. Strategies for neighborhood generation and candidate selection that exploit the special features of the problem are combined with a simple multiheuristic start procedure. Extensive experimentation, with multiple data sets and comparison with an upper bound, indicates a significant improvement, both in project Net Present Value (NPV) as well as the number of projects, where the metaheuristic outperforms the best known heuristics in the literature. More specifically, this procedure produces the best schedules in over 85% of the projects tested, in contrast to the best single-pass heuristics which have been shown to dominate in at most 20% of the same cases. This iterative, general purpose heuristic is able to adapt significantly better to the complex interactions of the many critical parameters of the RCPSPCF than single-pass heuristics that use more specific information about each project environment. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 912–927, 1999     

卫星任务调度问题的约束规划模型

卫星任务规划与调度是空间资源管理的重要内容之一,其目的在于为卫星系统的任务计划编制提供科学合理的决策手段与依据。卫星任务调度问题的重要特点在于,调度任务存在可见时间窗口约束。只有在可见时间窗口内,调度任务才可能执行并完成。在进行合理假设的基础上,建立卫星任务调度问题的约束规划模型。对基本禁忌搜索算法进行改进,提出了模型求解的变邻域禁忌搜索算法。应用结果表明,约束规划模型的建立与求解是合理的。     

Neighborhood search heuristics for selecting hierarchically well‐formulated subsets in polynomial regression

The importance of subset selection in multiple regression has been recognized for more than 40 years and, not surprisingly, a variety of exact and heuristic procedures have been proposed for choosing subsets of variables. In the case of polynomial regression, the subset selection problem is complicated by two issues: (1) the substantial growth in the number of candidate predictors, and (2) the desire to obtain hierarchically well‐formulated subsets that facilitate proper interpretation of the regression parameter estimates. The first of these issues creates the need for heuristic methods that can provide solutions in reasonable computation time; whereas the second requires innovative neighborhood search approaches that accommodate the hierarchical constraints. We developed tabu search and variable neighborhood search heuristics for subset selection in polynomial regression. These heuristics are applied to a classic data set from the literature and, subsequently, evaluated in a simulation study using synthetic data sets. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

Local search methods for the discrete time/resource trade-off problem in project networks

In this paper we consider the discrete time/resource trade-off problem in project networks. Given a project network consisting of nodes (activities) and arcs (technological precedence relations), in which the duration of the activities is a discrete, nonincreasing function of the amount of a single renewable resource committed to it, the discrete time/resource trade-off problem minimizes the project makespan subject to precedence constraints and a single renewable resource constraint. For each activity, a work content is specified such that all execution modes (duration/resource requirement pairs) for performing the activity are allowed as long as the product of the duration and the resource requirement is at least as large as the specified work content. We present a tabu search procedure which is based on a decomposition of the problem into a mode assignment phase and a resource-constrained project scheduling phase with fixed mode assignments. Extensive computational experience, including a comparison with other local search methods, is reported. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 553–578, 1998     

Heuristics for the location of inspection stations on a network

This article considers the preventive flow interception problem (FIP) on a network. Given a directed network with known origin‐destination path flows, each generating a certain amount of risk, the preventive FIP consists of optimally locating m facilities on the network in order to maximize the total risk reduction. A greedy search heuristic as well as several variants of an ascent search heuristic and of a tabu search heuristic are presented for the FIP. Computational results indicate that the best versions of the latter heuristics consistently produce optimal or near optimal solutions on test problems. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 287–303, 2000     

基于随机搜索策略的中继卫星调度方法

高质量的调度方案不仅能满足用户的需求,还能为中继卫星系统的计划编制提供科学的决策手段和依据。针对日益多样化的用户需求,采用全新的中继卫星调度应用模式,允许用户提交多个可以滑动的时间窗口。面向这种调度模式,考虑中继业务中任务调度的灵活性和任务间的冲突,构建考虑多滑动窗口的中继卫星调度模型,并设计基于随机搜索策略的中继卫星调度算法。算法包括任务资源匹配与邻域生成、可用时间段生成、任务冲突分析、邻域搜索与冲突消解以及资源与任务集更新5个算子。通过仿真实验将该算法与基于时间自由度的启发式算法进行对比,验证了算法的有效性。     

面向多星观测调度的分层迭代算法

提出一种基于分治策略的多星观测分层调度框架,在该框架下,用蚁群优化算法把任务分配至各轨道圈次上,并利用自适应模拟退火算法求解各轨道圈次的调度问题。根据各轨道圈次调度结果的反馈情况,再调整任务分配方案,重复上述过程直到达到算法终止条件。为了提高算法的性能,在设计蚁群算法的启发式信息模型时,应充分考虑卫星调度问题的领域知识;在模拟退火算法中设计两个邻域结构,采用动态选择策略在优化过程中确定最佳邻域搜索结构。仿真实验表明,该方法有效地降低了问题求解的复杂度,尤其在求解大规模多星观测调度问题时表现出优异的性能。