微粒群算法在传感器优化配置中的应用

为了将动态测试中的传感器配置在合理的自由度,以便充分反映结构的动力特性,需对传感器进行优化配置.分别以MAC矩阵、Fisher信息阵,及其组合为优化准则,采用微粒群算法,对传感器优化配置问题进行了研究,探讨了优化准则和优化算法对传感器优化配置结果的影响.通过与模态动能法、有效独立法及基于QR分解的逐步累积法比较,传感器优化配置的结果表明微粒群算法优于上述方法.     

基于HSM的传感器最优配置研究

给出了一种基于深知识模型、最大化可诊断性的最优化传感器(或测试点)配置的数学描述模型;对传感器(或测试点)的典型配置策略和存在的不足进行了分析,从实现最大化可诊断性的角度对传感器最优配置问题进行了深入研究;针对复杂系统传感器配置运算开销呈指数增加的问题,给出了基于BPSO算法的传感器配置策略;最后通过抽象实例进行验证,结果表明基于BPSO的传感器配置策略与其他方法相比,收敛速度快、诊断精度高,可用于大型系统传感器配置问题。     

航线网络规划中的时刻配置优化方法

时刻和航权资源是航空公司的重要资源,其中繁忙机场的时刻和航权资源最为宝贵,属于需要重点优化的部分。针对繁忙机场航线网络优化的问题,提出了一种基于0—1整数规划的时刻配置优化算法。该算法可以量化繁忙机场时刻价值,运用运筹优化理论,搭建0—1整数规划模型,优化繁忙机场航线网络布局。以境内最繁忙的11个机场为优化机场或固定机场,以整体贡献最大化为目标,优化其航线网络布局。实验结果表明,多优化机场方案实现平均28.6%的提升,为航空公司编制航季航班计划提供优化建议。     

改进的混合蛙跳算法在传感器配置优化中的应用

传感器配置优化是可测性设计的重要研究内容,将混合蛙跳算法应用于传感器配置优化是一种新的尝试。针对传感器配置优化属于离散问题求解,提出离散的混合蛙跳算法,设计了一种离散化的更新方式。为克服蛙跳算法的早熟收敛问题,在改进的离散蛙跳算法中采用混沌优化算法以概率的形式对全局极值进行了优化。最后通过具体系统实例验证了该方法的正确性和有效性。     

遗传算法在传感器优化配置中的应用

针对于传感器网络中传感器配置问题,在保证局部网络损坏的情况下仍能进行检测,提高检测网络的可靠性,提出了用遗传算法对其进行优化,并设计了该遗传算法的计算步骤和其相应的适应度函数程序。计算了一个实例,得出了较好的配置方式,满足了传感网络可靠性的要求,说明了用遗传算法进行传感器优化配置是可行的。     

CW-PSO及其在古建筑传感器优化配置中的应用研究

针对粒子群优化算法容易陷入早熟收敛以及全局搜索和局部搜索平衡能力差等缺点,提出了基于余弦自适应调整惯性权重的粒子群优化算法(CW-PSO),并将其应用在木构古建筑传感器优化配置中。仿真结果表明,该算法在一定程度上避免了早熟收敛,提高了全局和局部搜索性能,又能得到较为精确的寻优结果。     

人工鱼群算法在桥梁传感器优化配置中的应用

为了实现桥梁结构健康监测传感器的优化配置,用尽可能少的传感器获取尽可能多的反映桥梁结构健康状况的信息,将人工鱼群算法应用于一座拱桥的传感器配置中,利用人工鱼的三种典型行为,解决桥梁传感器优化配置问题。结果表明,人工鱼群算法自适应能力强,收敛精度高,可以实现桥梁结构健康监测传感器优化配置。     

基于传感器网络节点配置优化仿真研究

研究无线传感网络节点配置覆盖优化问题。由于无线传感网络存在着热区问题,对网络的覆盖性能造成严重的影响,同时影响网络配置优化。为了有效的提高无线传感网络的覆盖率,提出了一种改进的粒子群算法优化无线网络节点覆盖。针对粒子群算法存在易陷入局部极值和早熟的缺陷,引入遗传算法中的交叉算子和变异算子,优化传感网络节点的混合粒子群算法,在严格确保无线传感器网络连通性的条件下,传感器节点配置数目达到要求的覆盖度,并进行仿真。仿真结果表明混合粒子群算法能快速收敛到更精确的解,使网络节点配置达到覆盖的优化要求。     

装备健康管理中的传感器优化配置问题研究

传感器优化配置是装备健康管理(HM)的基础。考虑传感器失效对健康管理能力的影响,以传感器代价和漏检故障的危害程度最小为优化目标,以检测性能指标为约束条件,建立了传感器优化配置的模型;并针对传感器优化配置是一个多目标非线性整数规划问题,提出了基于扩展微粒子群优化算法的模型求解方法。应用实例表明了模型和算法的有效性。     

基于键合图模型的新型解析冗余关系故障诊断方法研究

研究了通过诊断键合图模型建立系统解忻冗余关系(ARRs)的故障诊断方法.通过在系统键合图中加入虚拟传感器的方法建立系统的诊断键合图,根据诊断键合图模型的因果关系路径构建系统的解析冗余关系和系统故障特征矩阵,并利用系统实际观测特征与故障特征的比较进行系统的故障检测和隔离,通过在双容水箱系统的仿真验证,证明了该方法便捷性和...     

New formulation and optimization methods for water sensor placement

Optimal sensor placement for detecting contamination events in water distribution systems is a well explored problem in water distribution systems security. We study herein the problem of sensor placement in water networks to minimize the consumption of contaminated water prior to contamination detection. For any sensor placement, the average consumption of contaminated water prior to event detection amongst all simulated events is employed as the sensing performance metric. A branch and bound sensor placement algorithm is proposed based on greedy heuristics and convex relaxation. Compared to the state of the art results of the battle of the water sensor networks (BWSN) study, the proposed methodology demonstrated a significant performance enhancement, in particular by applying greedy heuristics to repeated sampling of random subsets of events.     

A dominance-based stepwise approach for sensor placement optimization

A Wireless Sensor Network (WSN) usually consists of numerous wireless devices deployed in a region of interest, each of which is capable of collecting and processing environmental information and communicating with neighboring devices. The problem of sensor placement becomes non trivial when we consider environmental factors such as terrain elevations. In this paper, we differentiate a stepwise optimization approach from a generic optimization approach, and show that the former is better suited for sensor placement optimization. Following a stepwise optimization approach, we propose a Crowd-Out Dominance Search (CODS), which makes use of terrain information and intersensor relationship information to facilitate the optimization. Finally, we investigate the effect of terrain irregularity on optimization algorithm performances, and show that the proposed method demonstrates better resistance to terrain complexity than other optimization methods.     

基于信息覆盖理论的传感器节点部署方法

传感器网络中,覆盖控制是一个基本的问题.传感器节点既可以随机部署也可以在特定的环境中指定部署.传感器部署在传感区域网格的顶点处,如果信息能够覆盖网格中所有的顶点,此传感器网络为完全信息覆盖.通过研究基于完全信息覆盖理论的传感器节点指定部署,把传感器部署问题转化为受限的优化问题,并提出了一种贪婪算法,目的是在保证覆盖要求的条件下总代价的最小化.计算结果显示该算法不仅能有效的求得高质量的解决方案,而且还能在满足信息覆盖的要求的同时,极大地减少所需传感器的数量.     

Robust sensor placement for pipeline monitoring: Mixed integer and greedy optimization

Intelligent water systems – aided by sensing technologies – have been identified as an important mechanism towards ensuring the resilience of urban systems. In this work, we study the problem of sensor placement that is robust to intermittent failures of sensors, i.e. sensor interruptions. We propose robust mixed integer optimization (RMIO) and robust greedy approximation (RGA) solution approaches. The underlying idea of both approaches is to promote solutions that achieve multiple detectability of events, such that these events remain detectable even when some sensors are interrupted. Additionally, we apply a previously proposed greedy approximation approach for solving the robust submodular function optimization (RSFO) problem. We compare scalability of these approaches and the quality of the solutions using a set of real water-networks. Our results demonstrate that considering sensor interruptions in the design stage improves sensor network performance. Importantly, we find that although the detection performances of RMIO and RGA approaches are comparable, RMIO generally has better performance than RGA, and is scalable to large-scale networks. Furthermore, the results demonstrate that RMIO and RGA approaches tend to outperform the RSFO approach.     

A sensor array optimization method for electronic noses with sub-arrays

Sensor array configuration needs to be optimized in developing an application-specific instrument of electronic noses. Currently, sensor array optimization is commonly done by feature selection techniques. These methods could solve how to optimize a sensor array. However, they could not figure out what are the unique functions that each sensor plays in the optimized sensor array. The method proposed in this paper could solve this problem by sensor clustering and dividing the whole sample classification mission into several small recognition tasks. A measurement with a six Taguchi Gas Sensors (TGS sensor hereinafter) sensors array to classify 11 gas sorts was used in the data validation. The sensor array was optimized to three sensors with the proposed method. Each sensor in the optimized array had unique functions to solve different recognition tasks. TGS2600 had the unique functions to discriminate butanone and acetaldehyde. TGS2602 had the unique functions to discriminate benzene and cyclohexane, methanol and ethanol. TGS813 had the unique functions to discriminate cyclohexane and pentane. The combination of TGS2600 and TGS2602 had the unique functions to discriminate acetone and butanone, acetone and acetaldehyde. The proposed method might be a new generation of sensor array optimization methods.     

Optimal pressure sensor placement and assessment for leak location using a relaxed isolation index: Application to the Barcelona water network

Water distribution networks are large complex systems affected by leaks, which often entail high costs and may severely jeopardise the overall water distribution performance. Successful leak location is paramount in order to minimize the impact of these leaks when occurring. Sensor placement is a key issue in the leak location process, since the overall performance and success of this process highly depends on the choice of the sensors gathering data from the network. Common problems when isolating leaks in large scale highly gridded real water distribution networks include leak mislabelling and the obtention of large number of possible leak locations. This is due to similarity of leak effect in the measurements, which may be caused by topological issues and led to incomplete coverage of the whole network. The sensor placement strategy may minimize these undesired effects by setting the sensor placement optimisation problem with the appropriate assumptions (e.g. geographically cluster alike leak behaviors) and by taking into account real aspects of the practical application, such as the acceptable leak location distance. In this paper, a sensor placement methodology considering these aspects and a general sensor distribution assessment method for leak diagnosis in water distribution systems is presented and exemplified with a small illustrative case study. Finally, the proposed method is applied to two real District Metered Areas (DMAs) located within the Barcelona water distribution network.     

A Tabu Search Algorithm for application placement in computer clustering

This paper presents and analyzes a model for the problem of placing applications on computer clusters (APP). In this problem, organizations requesting a set of software applications have to be assigned to computer clusters such that the costs of opening clusters and installing the necessary applications are minimized. This problem is related to known OR problems such as the multiproduct facility location problem and the generalized bin packing problem. We show that APP is NP-hard, and then propose a simple Tabu Search heuristic to solve it. The performance of the Tabu Search heuristic is assessed via extensive computational experiments, which indicate the promise of the proposed Tabu Search.     

考虑可靠性的给水管网水质传感器多目标优化选址模型

针对给水管网水质传感器有可能失效的情况,提出了考虑传感器可靠性的多目标优化选址模型(SPM-R).该模型在最大限度监测突发或者蓄意的污染事件之外,期望传感器选址具有一定的可靠性,即若某一传感器发生故障,存在其它传感器能够继续监测污染事件,并且由此造成的监测时间增加值在允许范围之内.结合一管网算例,首先利用EPANET软件随机模拟管网节点被注入污染物后各个节点污染物浓度变化情况,得到监测时间矩阵,然后,基于非支配排序遗传算法-Ⅱ(NSGA-Ⅱ)详细阐述了SPM-R模型的求解过程,并对计算结果进行了参数敏感性分析.     

A simulation optimization method for internal trucks sharing assignment among multiple container terminals

Owing that the internal trucks (ITs) are frequently used as transportation equipments between yards and quaysides, the transportation efficiency of ITs secures a crucial position in container terminal productivity. Hence, a container terminal cannot contain a big number of ITs. As such, it is an imperative to explore an appropriate IT assignment strategy. Specifically for those container terminals with adjacent locations, an approach to sharing internal trucks among multiple container terminals (SIMT) is investigated. In this study, a novel strategy to resolve the SIMT problem was proposed for a specific large port with multiple adjacent container terminals. Firstly, an illustration of the SIMT strategy was presented. Then, an integer programming model for this problem is developed, where the objective functions are subject to the minimization of the total overflowed workloads and total transferring costs in every time-period among these container terminals. In particular, the rolling-horizon approach is employed for considering the immediate scheduling. Furthermore, a simulation optimization method, which integrates the genetic algorithm (GA) searching and simulation, is proposed for the near optimal solutions. Finally, the computational experiments are used to verify the effectiveness of the proposed SIMT strategy and simulation optimization method.