Constrained minimax optimization of continuous search efforts for the detection of a stationary target

Analytical resolution of search theory problems, as formalized by B.O. Koopman, may be applied with some model extension to various resource management issues. However, a fundamental prerequisite is the knowledge of the prior target density. Though this assumption has the definite advantage of simplicity, its drawback is clearly that target reactivity is not taken into account. As a preliminary step towards reactive target study stands the problem of resource planning under a min–max game context. This paper is related to Nakai's work about the game planning of resources for the detection of a stationary target. However, this initial problem is extended by adding new and more general constraints, allowing a more realistic modeling of the target and searcher behaviors. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

基层消防官兵职业心理应激及应对

消防职业心理应激源容易使基层消防官兵处于应激状态,产生一系列心理应激反应和危害。追溯消防职业心理应激源,探讨基层消防官兵职业心理应激反应,正确应对基层消防官兵职业心理应激,可以提高基层消防官兵心理健康水平,提高部队战斗力。     

新形势下加强高校师德建设的对策与思考

高校教师,承担着培养社会主义事业建设者和接班人的神圣使命,加强高校师德师风建设是教育使命和教师职责的必然要求,也是现阶段高校贯彻与实践“三个代表”重要思想和落实科学发展观的需要。本文从高校师德的现状及成因分析,提出了加强高校师德建设的对策与建议。     

听力策略与大学生听力能力的关系研究

听力能力的形成和发展依赖于语言知识和背景知识的掌握,但知识要想最终真正转化成为能力则需要策略为中介。元认知策略、认知策略和社会情感策略等听力策略却是将这些知识转化为能力的中介,他们对学生英语听力能力起着决定性的作用。为了了解听力策略对学生听力能力是否有影响,本文采取了问卷调查的研究方法。通过调查,找出问题,提出对策,以增强学生运用听力策略的意识,提高其在听力学习过程中对策略运用的能力。     

任务分配问题的对称群方法

探讨了无人飞行器(UAV)编队的任务分配问题。任务分配是UAV协同控制的基础,其解是任务区域内各任务的一个排列。求解UAV任务分配问题的有效方法是能在合理的计算时间内找到近似最优解的启发式算法。用对称群描述UAV任务分配的搜索空间,基于右乘运算构造搜索邻域。仿真结果验证了群论禁忌搜索算法的有效性。     

一种基于部分搜索的GNSS模糊度解算方法

为解决LAMBDA算法搜索策略效率不高的问题,提出了一种基于部分搜索的模糊度解算新方法。该方法的解算成功率接近LAMBDA算法,且只需对最后3维模糊度元素采取搜索策略。同时,通过合理设定搜索椭球的大小和搜索策略;大大提高了搜索效率。通过实验对该新算法与Bootraping算法、LAMBDA算法进行了比较分析,进一步验证了该新算法的有效性。     

求解CVRP问题的一种改进启发式蚁群算法

针对蚁群算法求解CVRP问题时收敛速度慢、求解质量不高的缺点,提出了一种改进启发式蚁群算法。该算法借鉴蚁群系统和基于排列的蚂蚁系统的优点设计信息素更新策略,既加强了对每次迭代最好解的利用,又避免了陷入局部最优;按一定比例使用基本方法和基于PFIH方法构造路径,扩大了算法的搜索空间;采用一种混合局部搜索算子,增强了算法局部寻优能力。实验结果表明,改进启发式蚁群算法可以大幅度减少车辆运行成本,具有较快的收敛速度。     

远程制导火箭子母弹对机场跑道毁伤研究

对机场跑道毁伤封锁是远程制导武器的重要任务,其毁伤判断的依据是最小升降窗口存在与否。采用Monte-Carlo方法对远程制导火箭子母弹打击机场跑道的炸点分布进行仿真模拟,提出了一种改进的搜索飞机起降最小升降窗口算法,并用该算法计算分析了远程制导火箭子母弹相关战技术因素对机场跑道毁伤的影响程度。计算结果表明改进算法计算封锁概率的值相对于区域搜索算法更加精确,研究结果对远程制导火箭子母弹的作战运用及进一步优化设计具有参考与实用价值。     

A learning tabu search for a truck allocation problem with linear and nonlinear cost components

The two‐level problem studied in this article consists of optimizing the refueling costs of a fleet of locomotives over a railway network. The goal consists of determining: (1) the number of refueling trucks contracted for each yard (truck assignment problem denoted TAP) and (2) the refueling plan of each locomotive (fuel distribution problem denoted FDP). As the FDP can be solved efficiently with existing methods, the focus is put on the TAP only. In a first version of the problem (denoted (P1)), various linear costs (e.g., fuel, fixed cost associated with each refueling, weekly operating costs of trucks) have to be minimized while satisfying a set of constraints (e.g., limited capacities of the locomotives and the trucks). In contrast with the existing literature on this problem, two types of nonlinear cost components will also be considered, based on the following ideas: (1) if several trucks from the same fuel supplier are contracted for the same yard, the supplier is likely to propose discounted prices for that yard (Problem (P2)); (2) if a train stops too often on its route, a penalty is incurred, which represents the dissatisfaction of the clients (Problem (P3)). Even if exact methods based on a mixed integer linear program formulation are available for (P1), they are not appropriate anymore to tackle (P2) and (P3). Various methods are proposed for the TAP: a descent local search, a tabu search, and a learning tabu search (LTS). The latter is a new type of local search algorithm. It involves a learning process relying on a trail system, and it can be applied to any combinatorial optimization problem. Results are reported and discussed for a large set of instances (for (P1), (P2), and (P3)), and show the good performance of LTS. © 2014 Wiley Periodicals, Inc. 62:32–45, 2015