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.     

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.     

未成年人德育环境优化

环境可以育人,良好的德育环境对未成年人的思想道德教育起着十分重要的作用。本文结合未成年人的特点,从学校德育环境、家庭德育环境和社会德育环境三方面,就如何优化德育环境,提高未成年人道德教育的实效性,促进未成年人健康成长做点探索。     

基于预配装思想的物资混装配载优化策略

提出了一种基于预配载思想的装备物资混装配载策略,并应用模糊聚类和混合智能算法进行了实例仿真。研究对于组合优化策略在装备保障信息化系统建设中的应用具有积极意义。     

关于军事物流系统优化的研究

根据现代战争对军事物流保障提出的客观需求,以及我国军事物流的发展现状,分析了进行军事物流系统优化的必要性,探讨了军事物流系统优化的基本思路,着重阐述了军事物流系统优化的几点措施.     

基于种群多样性测度的PSO算法及其应用研究

针对基本粒子群算法(particle swarm optimization,PSO)易局部收敛的缺陷,设计了一种根据种群多样性测度动态调整惯性权重的改进粒子群算法,通过仿真测试函数与基本粒子群算法、自适应粒子群算法(adaptive particle swarm optimization,APSO)、带收缩因子的粒子群算法(contractive particle swarm optimization,CPSO)进行比较,改进的PSO算法在提高算法的综合搜索能力方面具有优越性。将改进的PSO算法运用到作战飞行器航迹规划中,并进行了仿真实验,仿真结果验证了改进算法的有效性。     

基于IDEF3的部队装备中修业务流程优化重组

以IDEF3为工具描述了部队装备中修业务流程,分析了流程中存在的问题,通过采用基于状态的维修方式、无损检测先进技术手段、共享维修保障信息和调整器材发放审批权限等方法,对部队现行装备中修业务流程进行了优化,可有效解决装备维修过剩、维修不足、维修资源浪费和维修周期过长等问题．     

改进的Fisher准则及其在语音聚类中的应用

针对传统的Fisher准则在聚类分析方面的不足,提出了一种新的修正的Fisher准则。通过重新定义类内方差,得出最大Fisher值与最佳聚类数目的对应关系,从而预测出最佳聚类数目。通过对语音库中语音段落的说话者组成结构进行分析,证明了修正后的Fisher准则能有效地优化聚类分析,并显著提高了说话者组成结构分析结果的精度。应用基于修正后的Fisher准则的LBG聚类算法,能为语音操控系统中无监督的说话人自适应提供更可靠的说话人模板库。     

基于凸优化理论的三维微分对策制导规律求解

针对三维微分对策制导律(DGL)求解问题,引入凸优化理论,将DGL求解归结到Hamilton系统的求解,设计了DGL求解算法,通过对代价函数梯度特征的凸分析,推导出对策系统鞍点存在的充要条件和求解方法,解决了以往通过对微分对策模型简化求解导致的模型不能客观反映作战过程的问题.     

果蝇算法和5种群智能算法的寻优性能研究

截止到目前为止进化式算法主要有遗传算法、蚁群算法、鱼群算法、免疫算法、粒子群算法.这些算法已经被广泛地用于寻优,但都有各自的缺点,导致其不易被用于解决实际问题.某学者提出了一种新群智能算法——果蝇算法.对该算法的起源进行分析,并将该算法与其他算法对比,通过仿真分析各个算法寻优性能.重点分析果蝇算法的寻优性能,得出果蝇算法简单、参数少、易调节、计算量小、寻优精度较高,从而较容易被用于解决实际问题,对于复杂问题算法可能不稳定.指出该算法的缺点,提出应改进的地方,对其应用前景作了概括.