Shortest path interdiction problem with arc improvement recourse: A multiobjective approach

We consider the shortest path interdiction problem involving two agents, a leader and a follower, playing a Stackelberg game. The leader seeks to maximize the follower's minimum costs by interdicting certain arcs, thus increasing the travel time of those arcs. The follower may improve the network after the interdiction by lowering the costs of some arcs, subject to a cardinality budget restriction on arc improvements. The leader and the follower are both aware of all problem data, with the exception that the leader is unaware of the follower's improvement budget. The effectiveness of an interdiction action is given by the length of a shortest path after arc costs are adjusted by both the interdiction and improvement. We propose a multiobjective optimization model for this problem, with each objective corresponding to a different possible improvement budget value. We provide mathematical optimization techniques to generate a complete set of strategies that are Pareto‐optimal. Additionally, for the special case of series‐parallel graphs, we provide a dynamic‐programming algorithm for generating all Pareto‐optimal solutions.     

炮控伺服系统自抗扰控制器优化设计

针对自抗扰控制器参数难于整定的问题,给出了一种基于自适应变异粒子群优化算法的炮控伺服系统自抗扰控制器优化设计方法.该算法结构简单,具有良好的全局收敛性,充分发挥了自抗扰控制器的控制性能.通过对某炮控伺服系统的仿真研究,表明了该设计方法的有效性.     

炮兵火力任务分配的优化模型

现代作战,参战兵种多,炮兵的火力单位和类型更为复杂,不同的火力单位具有不同的毁伤效果,如何用最小的代价取得最大的军事效益,是一种火力任务分配的优化问题.根据不同炮种和目标的特点,建立在一定约束条件下的炮兵火力任务分配模型,并将其转化为指派问题,运用匈牙利算法求解,达到优化分配火力任务的目的.通过仿真计算证明这是辅助指挥员进行射击决策的一种有效手段,为作战指挥理论提供了一种新的数学模型.     

A worst‐case formulation for constrained ranking and selection with input uncertainty

In this research, we consider robust simulation optimization with stochastic constraints. In particular, we focus on the ranking and selection problem in which the computing time is sufficient to evaluate all the designs (solutions) under consideration. Given a fixed simulation budget, we aim at maximizing the probability of correct selection (PCS) for the best feasible design, where the objective and constraint measures are assessed by their worst‐case performances. To simplify the complexity of PCS, we develop an approximated probability measure and derive the asymptotic optimality condition (optimality condition as the simulation budget goes to infinity) of the resulting problem. A sequential selection procedure is then designed within the optimal computing budget allocation framework. The high efficiency of the proposed procedure is tested via a number of numerical examples. In addition, we provide some useful insights into the efficiency of a budget allocation procedure.     

一种基于蚁群算法的准动态防空武器分配算法

武器目标分配问题是一个典型的限制组合优化问题,旨在得到在整个防御阶段中针对目标函数的最优武器分配方案。分配算法主要分为静态和动态两大类。针对传统静态分配模型中存在的几点问题,提出了基于时间窗的准动态武器目标分配算法,该算法综合考虑拦截概率、拦截时间和武器耗费多个优化指标,并将该算法推广至多类防空武器的优化分配中。通过大量实验验证,该算法在性能、时间复杂度等方面均有较大优势,并且能较好地适应战场态势的变化,及时调整分配方案,具有很好的实用性。     

船艇发动机虚拟维修中的场景优化技术研究

针对船艇发动机虚拟维修场景构建过程中模型的高逼真度和实时交互之间的矛盾,提出了降低场景复杂度的初期建模优化技术,以及模型合成后的全景优化技术。通过在发动机虚拟维修训练系统中的综合应用,有效地简化了模型的多边形网格,降低了场景的复杂度,提高了场景的生成速度。     

What you should know about approximate dynamic programming

Approximate dynamic programming (ADP) is a broad umbrella for a modeling and algorithmic strategy for solving problems that are sometimes large and complex, and are usually (but not always) stochastic. It is most often presented as a method for overcoming the classic curse of dimensionality that is well‐known to plague the use of Bellman's equation. For many problems, there are actually up to three curses of dimensionality. But the richer message of approximate dynamic programming is learning what to learn, and how to learn it, to make better decisions over time. This article provides a brief review of approximate dynamic programming, without intending to be a complete tutorial. Instead, our goal is to provide a broader perspective of ADP and how it should be approached from the perspective of different problem classes. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009     

Optimization of reluctance accelerator efficiency by an improved discharging circuit

In this paper, an improved discharging circuit was proposed to quicken the decay of the current in the drive coil in a reluctance accelerator when the armature reaches the center of the coil. The aim of this is to prevent the suck-back effect caused by the residual current in drive coil. The method is adding a reverse charging branch with a small capacitor in the traditional pulsed discharging circuit. The results under the traditional circuit and the improved circuit were compared in a simulation. The experiment then verified the simulations and they had good agreement. Simulation and experiment both demonstrated the improved circuit can effectively prevent the suck-back effect and increase the efficiency. At the voltage of 800 V, an efficiency increase of 36.34% was obtained.     

舰炮身管的模态分析与多目标优化

舰炮的炮口扰动是影响舰炮射击精度的重要因素。为了减少舰炮的炮口扰动,优化身管结构,建立了柔性身管的ABAQUS有限元模型,将有限元模型的模态计算值与模态试验值进行比较,发现建立的柔性身管有限元模型是合理可行的。提出了混沌量子粒子群算法与动力学联合优化的方法,进行身管和炮口的多目标优化。优化结果表明,优化后炮口中心的线速度、角速度和角位移与优化前相比显著减小,身管质量有所降低,结构更加合理,该优化方法有效可行,为下一步全炮总体优化设计提供了一定的借鉴。     

求解不确定型车辆路径问题的弱鲁棒优化方法

为降低鲁棒优化模型最优解的保守性,以最小化违约车辆数和总惩罚成本为目标,建立针对旅行时间不确定的开放式车辆路径问题的弱鲁棒优化模型。对于不确定数据集的每个取值,该模型的最优解可以使其目标函数值始终不超过某数值,进而改善最优解的保守性。为提高启发式算法发现最优解的概率,提出一种自设计遗传算法对模型进行求解,其主要思想是利用粒子群算法搜索出可使遗传算法预期产生最好解的算法要素,并将其进行组合,从而产生新的遗传算法。采用新产生的遗传算法对模型继续求解,输出最好解。计算结果表明:与以往的鲁棒优化方法相比,弱鲁棒优化方法的最优解的保守性显著降低。