滑翔飞行器多目标弹道优化的进化-配点混合求解策略

针对高超声速滑翔飞行器弹道多目标优化问题,综合考虑计算效率和精度,结合分解进化算法与配点法提出一种混合求解策略。根据滑翔飞行器动力学模型和弹道设计中需要考虑的约束条件,建立飞行器多目标弹道优化模型。利用控制量离散化方法将多目标弹道优化问题转化为带约束的多目标参数优化问题,并采用罚函数法处理约束条件,随后利用分解多目标进化算法进行求解。为了提高弹道优化的精度,将椭球聚合法与配点法相结合,以多目标进化算法得到的Pareto解作为初始解进行迭代求解。通过典型的复杂约束多目标弹道优化的算例表明,所提出的混合求解策略能够获得满足复杂约束要求的Pareto最优解集,实现有效的多目标弹道优化。     

采用改进遗传算法的舰载机保障调度方法

舰载机保障作业过程受到多种资源约束,同时可能存在突发事件的干扰。针对此特点,分析并制定干扰情况下的模型修正策略。在遗传算法中引入禁忌搜索算子改进传统遗传算法的变异操作,并通过具体实例进行仿真验证,其结果证明改进后的遗传算法在优化含干扰事件的多机保障问题时效率更高,并通过甘特图直观地反映重调度方案,为真实情况下有效处理舰载机保障过程中的干扰事件奠定理论基础。     

Sensor radiation interception risk control in target tracking

This paper is mainly on the problem of radiation interception risk control in sensor network for target tracking. Firstly, the sensor radiation interception risk is defined as the product of the interception probability and the cost caused by the interception. Secondly, the radiation interception probability model and cost model are established, based on which the calculation method of interception risk can be obtained. Thirdly, a sensor scheduling model of radiation risk control is established, taking the minimum interception risk as the objective function. Then the Hungarian algorithm is proposed to obtain sensor scheduling scheme. Finally, simulation experiments are mad to prove the effectiveness of the methods proposed in this paper, which shows that compared with the sensor radiation interception probability control method, the interception risk control method can keep the sensor scheduling scheme in low risk as well as protect sensors of importance in the sensor network.     

大容量储能发电机并联运行励磁控制算法

大容量储能发电机作为脉冲电源,整流后通过逆变器向大功率脉冲负载供电。采用储能发电机直流侧并联方式工作,可以有效提高系统带载能力与可靠性。当并联运行的储能发电机转速不同时,根据转速合理分配电机释放功率可以有效提高系统工作效能,因此需要设计一种具有功率调节功能的励磁控制算法。在电压、电流双闭环控制的基础上,引入功率前馈控制,推导并求解了前馈功率与励磁电流关系以及控制参数,并根据电机初始转速设计了功率分配算法。仿真试验结果表明,功率前馈控制在励磁控制过程中起主导作用,有效分配储能电机释放功率大小,提高了励磁电流响应速度,抑制了负载功率扰动对励磁控制系统的影响。     

Single‐machine scheduling with deadlines to minimize the total weighted late work

We consider scheduling a set of jobs with deadlines to minimize the total weighted late work on a single machine, where the late work of a job is the amount of processing of the job that is scheduled after its due date and before its deadline. This is the first study on scheduling with the late work criterion under the deadline restriction. In this paper, we show that (i) the problem is unary NP‐hard even if all the jobs have a unit weight, (ii) the problem is binary NP‐hard and admits a pseudo‐polynomial‐time algorithm and a fully polynomial‐time approximation scheme if all the jobs have a common due date, and (iii) some special cases of the problem are polynomially solvable.     

基于两阶段算法的半潜维修船功能舱室布局

以维修舱空间、舱室邻接、舱室全局位置三个指标为目标函数,以满足高初稳性要求、保证船舶浮态等为约束条件,建立技术可行前提下半潜维修船功能舱室布局优化模型。通过设计贪婪取走启发式算法与规划求解相结合的两阶段算法,实现对模型的求解。以载重为5×10~4 t的半潜船体为载体进行设计,得到半潜维修船功能舱室优化布局方案。随着维修设备质量系数的增加,可行的布局方案数呈上升趋势。该方法可提高半潜维修船在功能舱室布局过程中决策的科学性。     

基于改进协同遗传算法的有效载荷系统功能序列规划方法

针对传统回溯算法在求解基于知识模型的有效载荷系统功能序列规划问题中搜索效率低的问题,提出一种基于"择劣变异"(Worst Individual Mutation,WIM)策略的协同遗传算法(Co-evolutionary Genetic Algorithm,CGA)的改进算法WIM-CGA。该算法在遗传过程中采用双路线进化方案,即"择优实施标准遗传过程,择劣实施变异操作",达到提高求解精确度及搜索效率的目的。仿真结果表明,同等测试条件下,当功能规模为50,约束密度为1.0时,WIM-CGA算法在限定时间内最优解的平均精确度比优化的回溯算法提高了54.15%,比CGA算法提高了6.18%,且当所得解的精确度大于90%时,WIM-CGA算法比CGA算法的迭代次数减少了65.79%,耗时降低了48.97%,显著提高了功能序列规划的效率。     

加强约束的布尔可满足硬件求解器

利用现场可编程门阵列固有的并行性和灵活性,提出在硬件可编程平台上基于随机局部搜索算法的布尔可满足性求解器,用于求解大规模的布尔可满足性问题。相对其他求解器,该求解器的预处理技术能极大提高求解效率;其变元加强策略避免了同一变元被反复连续翻转,降低了搜索陷入局部最优的可能。评估结果表明,求解器最多能处理32 000个变元/128 000个子句的实例。相比当前同类型的求解器,其求解效率明显提高。     

A multi‐stage stochastic programming approach for network capacity expansion with multiple sources of capacity

In networks, there are often more than one sources of capacity. The capacities can be permanently or temporarily owned by the decision maker. Depending on the nature of sources, we identify the permanent capacity, spot market capacity, and contract capacity. We use a scenario tree to model the uncertainty, and build a multi‐stage stochastic integer program that can incorporate multiple sources and multiple types of capacities in a general network. We propose two solution methodologies for the problem. Firstly, we design an asymptotically convergent approximation algorithm. Secondly, we design a cutting plane algorithm based on Benders decomposition to find tight bounds for the problem. The numerical experiments show superb performance of the proposed algorithms compared with commercial software. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 600–614, 2017     

基于贝叶斯混合博弈的空袭火力资源分配决策模型

从博弈论的角度出发研究空袭火力资源的分配问题,针对空袭编队和防空火力单元攻防对抗过程中存在的不确定性、静态性以及动态性,建立基于贝叶斯混合博弈的空袭对抗火力分配模型。通过构造贝叶斯混合博弈树,采用逆向回溯法分别建立不同的博弈分析模型,利用混合粒子群算法求解那什均衡。仿真结果表明:以博弈论为背景研究空袭作战火力分配问题,符合真实的作战坏境,有效性好,有较高的理论应用价值。