单目标—多条件约束网络RRAP问题分段迭代PSO算法研究

研究2-状态可靠性-冗余分配问题（简写为RRAP）,即选择元件的可靠度与冗余度（都是决策变量）,在满足系统的费用、重量和体积等的约束下,使得系统的可靠度最大。构造系统的解结构：由系统的元件可靠度与系统可选元件的冗余度按照子系统元件顺序构成一个行向量,即行向量分量既有实数,又有整数部分。在此基础上,设计新解生成算法;构造具有固定压缩系数、动态权重系数的两阶段迭代粒子群优化算法。对算法用Matlab编程实现,用典型网络进行测试,算法都给出了问题的最优解。因此,适当设计的粒子群优化算法是求解复杂的可靠性-冗余分配问题的有效工具,算法具有原理容易理解,微型计算机上容易实现的特点。     

靶场某转发设备监控系统可靠性分配研究

阐述了航天靶场某转发设备的结构,针对其结构采用自上而下的可靠性分配原则,选用恰当的可靠性分配方法将设备的研制指标分配到各子系统;详细研究了系统的可靠性指标分配过程,得到了监控系统的可靠性指标。     

基于非稳态MAB的LEO卫星跳波束时隙分配算法

针对低地球轨道（LEO）卫星系统中的跳波束资源分配算法不能适应小区业务动态变化等问题，提出了一种基于非稳态多臂赌博机（MAB）的LEO卫星跳波束时隙分配算法。首先，以系统二阶差分容量最小化为优化目标，建立了时隙分配和波束等级匹配的联合优化问题。其次，由于该问题非凸且难以直接求解，基于有效小区和有效关键小区的概念提出波束等级组合方案生成算法，从而生成所有可能的波束等级组合方案。接下来，提出了基于非稳态MAB模型的动态时隙分配方案，在最优波束等级组合方案下完成时隙分配与波束等级匹配的联合优化。最后，计算机仿真结果表明，所提算法在多种小区业务分布的情况下，系统平均冗余度均不超过20%；相比于其他对比方案，所提算法在保持较高的系统吞吐量的同时，还可以将波束平均重访时间控制在300 ms左右。     

基于并行分配算法的认知无线电频谱分配算法

针对传统频谱分配方式不合理造成频谱匮乏以及CSGC算法在频谱分配上消耗时间过长影响实时通信的问题,文中提出了一种基于并行分配算法的认知无线电频谱分配算法,其通过同时给各顶点进行上色,从而节省了时间开销。实验证明,并行算法不仅具有与CSGC算法同样的高频谱利用率,且其频谱分配时间开销更低,是一种理想的认知无线电频谱分配算法。     

基于萤火虫算法的多中继功率分配

为了充分实现中继协作,降低多中继协作通信系统功率分配优化问题的计算复杂度,提出了基于萤火虫算法的多中继功率分配方案。在一定的总功率和节点功率约束下,以最大化平均信噪比为优化目标函数,建立了多中继协作系统的功率分配最优化模型。选取该目标函数作为萤火虫的适应度函数,用向量表示萤火虫的状态,该向量的维数为待分配源节点和中继节点的个数,通过萤火虫聚集得到种群中最好的萤火虫,即可获得渐进最优功率分配。仿真结果表明,与平均功率分配相比,基于萤火虫算法的功率分配方案能降低2.44%~6.17%的比特差错率,提高了系统性能。     

认知无线电网络中基于功率有效性的最优功率分配

在资源受限的认知无线电网络中,如何提高次用户网络的功率利用率是一个值得考虑的问题。针对这个问题,本文首先提出了认知无线电网络中基于功率有效性的次用户最优功率分配算法,该算法不仅考虑主用户网络中断概率对次用户发射功率的限制,而且兼顾次用户网络本身的中断概率要求。其次,为了进一步降低节点的计算复杂度,本文通过降维处理将目标最优化问题转化为两个子问题进行求解,从而提出一种次优的低复杂度功率分配算法。仿真结果表明,次优算法相比最优算法仅带来有限的性能损失,但是却有效地节省了计算时间和存储空间;此外,当中继节点靠近源节点时更有利于系统功率效率的提高,源节点到目的节点链路相比中继链路对系统的性能影响更大。      

MIMO空间复用系统的最小BER比特分配

该文基于最小误比特率(BER)准则,提出了多输入多输出(MIMO)空间复用系统的贪婪比特分配算法和基于二分法的比特分配算法。在总比特速率和每个发射天线分配相等功率的约束条件下,通过比特分配优化每个发射天线的调制方式,改善了系统的BER性能。仿真结果表明,与传统的MIMO系统相比,比特分配的MIMO系统可获得显著的信噪比(SNR)增益;与功率分配相比,比特分配在性能损失很小的情况下减少了每个发射天线的功率放大器的动态范围。     

基于权衡因子的联邦滤波信息分配系数研究

组合导航系统的融合算法普遍采用运行速度快、实时性强、计算量小的联邦滤波算法。针对该算法中当前的信息分配原则无法同时兼顾系统滤波精度及容错性的缺陷,采用了一种基于权衡因子的自适应信息分配方法。通过各子系统的误差协方差及量测噪声方差分别计算出能够提高系统滤波精度和容错性的信息分配系数,将各子系统的故障概率归一化得出该子系统的权衡因子,并在权衡因子的作用下自适应调节上述两种信息分配系数所占的比重,达到同时兼顾系统滤波精度和容错性的目的。仿真结果表明该方法能够减小系统的融合误差,保证系统的工作性能及融合精度。     

基于贪心算法的无线mesh时空域多信道分配研究

无线mesh网络多接口多信道分配算法中,信道分配与接口数目之间存在相互制约、相互依赖、“涟漪效应”,导致链路无效以及承载网络拓扑的主要业务节点存在时序关系,本文在基于多信道空间和时间联合信道分配算法的基础之上,考虑前一个子时序已分配信道对下一个子时序信道分配的影响,提出了基于贪心算法的无线mesh时空域多信道分配算法.根据贪心算法原理,尽量不改变已分配信道,减少信道切换时间,将剩余的未分配信道分配给要分配的接口,使信道能并行工作以提高整个网络的吞吐量.通过实验仿真,对比了能够抑制“涟漪效应”和链路无效的静态多接口多信道分配算法、空间与时间相结合的多接口多信道分配算法.结果表明,整个mesh网络的吞吐量有明显提高,且随着网络中业务节点变化的减小而增大,随着可利用信道数目的增加而增加.     

集中式跟踪融合系统中的自适应传感器分配

建立了传感器管理的一般最优化模型，研究了基于协方差控制策略的传感器分配算法，详细讨论了其分配算法在集中式跟踪融合系统中的具体实现，并给出3种不同的矩阵度量。结果表明，基于协方差控制的传感器分配算法，可依跟踪精度的需求动态地分配传感器以达到对协方差的有效控制，同时节约了传感器资源，因而是一种有效的传感器分配算法。     

Reliability allocation through cost minimization

This paper considers the allocation of reliability and redundancy to parallel-series systems, while minimizing the cost of the system. It is proven that under usual conditions satisfied by cost functions, a necessary condition for optimal reliability allocation of parallel-series systems is that the reliability of the redundant components of a given subsystem are identical. An optimal algorithm is proposed to solve this optimization problem. This paper proves that the components in each stage of a parallel-series system must have identical reliability, under some nonrestrictive condition on the component's reliability cost functions. This demonstration provides a firm grounding for what many authors have hitherto taken as a working hypothesis. Using this result, an algorithm, ECAY, is proposed for the design of systems with parallel-series architecture, which allows the allocation of both reliability and redundancy to each subsystem for a target reliability for minimizing the system cost. ECAY has the added advantage of allowing the optimal reliability allocation in a very short time. A benchmark is used to compare the ECAY performance to LM-based algorithms. For a given reliability target, ECAY produced the lowest reliability costs and the optimum redundancy levels in the successive reliability allocation for all cases studied, viz, systems of 4, 5, 6, 7, 8, 9 stages or subsystems. Thus ECAY, as compared with LM-based algorithms, yields a less costly reliability allocation within a reasonable computing time on large systems, and optimizes the weight and space-obstruction in system design throughout an optimal redundancy allocation.     

A new dynamic programming method for reliability & redundancy allocation in a parallel-series system

Reliability & redundancy allocation is one of the most frequently encountered problems in system design. This problem is subject to constraints related to the design, such as required structural, physical, and technical characteristics; and the components available in the market. This last constraint implies that system components, and their reliability, must belong to a finite set. For a parallel-series system, we show that the problem can be modeled as an integer linear program, and solved by a decomposition approach. The problem is decomposed into as many sub-problems as subsystems, one sub-problem for each subsystem. The sub-problem for a given subsystem consists of determining the number of components of each type in order to reach a given reliability target with a minimum cost. The global problem consists of determining the reliability target of subsystems. We show that the sub-problems are equivalent to one-dimensional knapsack problems which can be solved in pseudopolynomial time with a dynamic programming approach. We show that the global problem can also be solved by a dynamic programming technique. We also show that the obtained method YCC converges toward an optimal solution.     

An algorithmic approach to increased reliability through standbyredundancy

The authors address the issue of optimal redundancy allocation, viz, improving the reliability of a system by adding cold-standby spares, through an algorithmic approach that uses the unique characteristics of an assumed underlying failure distribution. Although the approach applies to systems with mixed parallel and series configurations, the discussion is limited to a series system of components. The lifetime distributions of individual parts are of the phase type. This class of probability distributions is chosen for its ease of numerical implementation. The formulation of the reliability enhancement problem is applied, and several heuristic design algorithms are examined     

Reliability-Redundancy Allocation for Multi-State Series-Parallel Systems

Current studies of the optimal design of multi-state series-parallel systems often focus on the problem of determining the optimal redundancy for each stage. However, this is only a partial optimization. There are two options to improve the system utility of a multi-state series-parallel system: 1) to provide redundancy at each stage, and 2) to improve the component state distribution, that is, make a component in states with respect to higher utilities with higher probabilities. This paper presents an optimization model for a multi-state series-parallel system to jointly determine the optimal component state distribution, and optimal redundancy for each stage. The relationship between component state distribution, and component cost is discussed based on an assumption on the treatment on the components. An example is used to illustrate the optimization model with its solution approach, and that the proposed reliability-redundancy allocation model is superior to the current redundancy allocation models.     

一种基于多源信息的广义比例组合可靠性分配法

提出一种基于多源信息的广义比例组合电子系统可靠性分配法.该方法在GJB 299C-2006元器件失效率预计公式的基础上,按新研系统的新标准、新环境统一折合元器件失效率,估计相似单机失效率,而后按照新系统设计方案来组合各类型单机,最后利用余度系统比例组合法将系统可靠性指标分配至各单机.该方法拓展了比例组合法的适用范围.基...     

基于粒子群算法的软件可靠性分配问题研究

针对软件可靠性分配中不易求解全局最优解这一问题,将可靠性指标分配到每个模块中,并利用改进的粒子群优化算法来搜索模型的最优解.实验结果表明,改进的粒子群优化算法在求解软件可靠性分配问题时的效果优于遗传算法等其他智能优化算法.     

Multilevel Redundancy Allocation Optimization Using Hierarchical Genetic Algorithm

This paper proposes a generalized formulation for multilevel redundancy allocation problems that can handle redundancies for each unit in a hierarchical reliability system, with structures containing multiple layers of subsystems and components. Multilevel redundancy allocation is an especially powerful approach for improving the system reliability of such hierarchical configurations, and system optimization problems that take advantage of this approach are termed multilevel redundancy allocation optimization problems (MRAOP). Despite the growing interest in MRAOP, a survey of the literature indicates that most redundancy allocation schemes are mainly confined to a single level, and few problem-specific MRAOP have been proposed or solved. The design variables in MRAOP are hierarchically structured. This paper proposes a new variable coding method in which these hierarchical design variables are represented by two types of hierarchical genotype, termed ordinal node, and terminal node. These genotypes preserve the logical linkage among the hierarchical variables, and allow every possible combination of redundancy during the optimization process. Furthermore, this paper developed a hierarchical genetic algorithm (HGA) that uses special genetic operators to handle the hierarchical genotype representation of hierarchical design variables. For comparison, the customized HGA, and a conventional genetic algorithm (GA) in which design variables are coded in vector forms, are applied to solve MRAOP for series systems having two different configurations. The solutions obtained when using HGA are shown to be superior to the conventional GA solutions, indicating that the HGA here is especially suitable for solving MRAOP for series systems.      

Optimal Reliability Design of a System: A New Look

The reliability literature offers an abundance of methods for the optimal design of systems under some constraints. In most of the papers, the problem considered is: given reliabilities of each constituent component and their constraint-type data, optimize the system reliability. This amounts to the assignment of optimal redundancies to each stage of the system, with each component reliability specified. This is a partial optimization of the system reliability. At the design stage, a designer has many options, e.g., component reliability improvement and use of redundancy. A true optimal system design explores these alternatives explicitly. Our paper demonstrates the feasibility of arriving at an optimal system design using the latter concept. For simplicity, only a cost constraint is used, however, the approach is more general and can be extended to any number of constraints. A particular cost-reliability curve is used to illustrate the approach.     

一种面向设计寿命全过程的电子系统可靠性分配法

提出一种基于等分配法的、面向各单元设计寿命全过程的电子系统可靠性分配法。该方法在保证满足系统设计寿命末可靠性指标的前提下,使各单元在设计寿命过程中各个时期的可靠度接近,从而避免出现可靠度显著地低于其它单元的＂短板＂单元。基于某＂软件星＂有效载荷的算例,演示了所提出的可靠性分配法的应用流程,比较了该方法与传统等分配法的分配结果。     

Techniques for Optimum Spares Allocation: A Tutorial Review

In many modern complex systems the problem of achieving high reliability leads to the use of interchangeable modular components accompanied by a stock of spare parts. This paper examines, compares, and assesses several of the techniques presented in the literature for allocating the numbers of spares of each part type to be stocked in order to maximize the system reliability subject to constraints on resources (i.e., weight, volume, cost, etc.). The problem of optimum spares allocation is complicated since resources are consumed in a discrete fashion and the expression for the system reliability is a nonlinear transcendental function. The classical dynamic programming algorithm produces all optimal spares allocations; however, the solution can become computationally intractable even with the aid of a modern high-speed digital computer. In the case of multiple constraints the time problem is vastly exacerbated. In such a case one must turn to a procedure that yields a near-optimal solution in a reasonable amount of computer time. Two approximate methods discussed in this paper are the incremental reliability per pound algorithm and the Lagrange multiplier algorithm. These algorithms are readily adaptable to handle multiple constraints. Computer programs are developed for each of the three optimization algorithms and are utilized to obtain the spares allocation for a few systems. The optimization theory presented is directly applicable to series or parallel systems. A concluding example illustrates how this can be extended to certain series-parallel systems.