A search game taking account of attributes of searching resources

This article deals with a two‐person zero‐sum game called a search allocation game (SAG), in which a searcher and a target participate as players. The searcher distributes his searching resources in a search space to detect the target. The effect of resources lasts a certain period of time and extends to some areas at a distance from the resources' dropped points. On the other hand, the target moves around in the search space to evade the searcher. In the history of search games, there has been little research covering the durability and reachability of searching resources. This article proposes two linear programming formulations to solve the SAG with durable and reachable resources, and at the same time provide an optimal strategy of distributing searching resources for the searcher and an optimal moving strategy for the target. Using examples, we will analyze the influences of two attributes of resources on optimal strategies. © 2007 Wiley Periodicals, Inc. Naval Research Logistics 2008     

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     

一种信号检测模糊技术及其应用

本文基于模糊理论,建立一种信号检测的模糊模型,并实际运用于连续波多普勒测速雷达目标速度的提取,取得了满意的结果     

智能搜救机器人在障碍地形的自主构型规划

为了解决带有辅助摆臂的智能搜救机器人自动规划构型以实现自主越障的难题,提出一种能够适应复杂地面形状的搜救机器人越障构型规划新方法,其核心是一种高适应性、高效率的机器人姿态预测算法。通过将地形表示为离散的点集,建立了搜救机器人的单侧姿态预测数学模型;进一步提出了快速求解该问题的算法,每秒可预测1 000～1 500个姿态。基于此,设计了机器人越障过程中状态、动作的评价指标,运用动态规划算法与滚动优化思想构建了具有优化能力的、能够实时运行的构型规划器。仿真与实物实验的结果表明,该方法能够使机器人自主调整构型穿越复杂地形,且相较强化学习算法和人工操作具有更平稳的越障效果。     

集群搜索对策理论在搜索-规避对抗中的应用

使用对策论的观点和方法 ,结合搜索论的知识 ,建立了一类搜索 -规避对抗对策模型 .对模型的结论做了系统分析 ,考虑了对策双方的最优策略及使用 .     

机载红外搜索跟踪系统

红外搜索和跟踪（ＩＲＳＴ） 系统是迅速探测红外威胁源的存在, 识别威胁源的种类,测定威胁源的方位并报警的一种光电系统。介绍了对机载ＩＲＳＴ的性能要求, 美国和欧洲等国对机载ＩＲＳＴ的研制及装备概况, 分析了今后机载ＩＲＳＴ系统的发展方向。     

高超声速伸缩翼变形飞行器轨迹多目标优化

针对高超声速条件下变形技术的应用模式，对具有伸缩翼的组合式飞行器滑翔弹道进行了多目标优化研究。介绍了伸缩翼的变形模式，给出了不同变形状态下的气动特性；建立了三自由度滑翔轨迹动力学模型和伸缩翼前缘热流计算模型；采用MOEA/D多目标优化算法，以变形条件和飞行攻角为设计变量、以最大射程和最小翼前缘总吸热量为目标函数，进行了多目标优化计算。优化结果表明，MOEA/D计算得到了相对均匀分布的Pareto最优解集，将伸缩翼外形与无变形外形相比，飞行器滑翔段射程得到了显著提高，同时伸缩翼前缘总吸热量有明显的降低。     

气控软体驱动器结构分析与优化设计

气控软体驱动器的结构特性对其运动和力学特性影响显著,目前气控软体驱动器支反力和倾角性能亟须提升.基于ABAQUS提供的脚本接口,采用Python语言开发脚本建立气控软体驱动器参数化仿真模型,将尺寸因子作为优化变量,联立Isight和ABAQUS,采用单因子试验方法确定倾角和支反力两个目标量的高敏感性影响因子.运用归一化...     

Benders' cuts guided large neighborhood search for the traveling umpire problem

This article introduces the use of Benders' cuts to guide a large neighborhood search to solve the traveling umpire problem, a sports scheduling problem inspired by the real‐life needs of the officials of a sports league. At each time slot, a greedy matching heuristic is used to construct a schedule. When an infeasibility is recognized first a single step backtracking is tried to resolve the infeasibility. If unsuccessful, Benders' cuts are generated to guide a large neighborhood search to ensure feasibility and to improve the solution. Realizing the inherent symmetry present in the problem, a large family of cuts are generated and their effectiveness is tested. The resulting approach is able to find better solutions to many instances of this problem. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

A genetic algorithm with neighborhood search for the resource‐constrained project scheduling problem

The resource‐constrained project scheduling problem (RCPSP) consists of a set of non‐preemptive activities that follow precedence relationship and consume resources. Under the limited amount of the resources, the objective of RCPSP is to find a schedule of the activities to minimize the project makespan. This article presents a new genetic algorithm (GA) by incorporating a local search strategy in GA operators. The local search strategy improves the efficiency of searching the solution space while keeping the randomness of the GA approach. Extensive numerical experiments show that the proposed GA with neighborhood search works well regarding solution quality and computational time compared with existing algorithms in the RCPSP literature, especially for the instances with a large number of activities. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011