Mean-value analysis and convolution method for queue-dependent servers in closed queueing networks

In this paper, we formulate a recursive relation for the marginal probabilities of a closed network with K customers in terms of the same network with K ? 1 customers. Mean-value analysis (MVA) is an application of this relation, together with Little's formula. It is shown that the convolution method, too, can be based on the same recursive result. This leads to a new convolution algorithm called normalized convolution algorithm (NCA), which like MVA works entirely with probabilities and throughputs rather than with quantities such as normalization contacts. NCA avoids a difficult problem, the occurrence of floating-point over-flows in the original convolution algorithm.We shall also solve a numerical stability problem found in MVA. Finally, we show how MVA and the convolution algorithm can be combined in the same problem to yield a hybrid method retaining the best properties of both methods.     

Optimization of a finite dipole array with genetic algorithm including mutual coupling effects

In this article, the genetic algorithm together with a circuit model is applied to optimize the gain of a dipole array by adjusting the dipole element positions. This algorithm can optimize problems globally without any gradient calculations and is especially suitable for large antenna array design. © 2000 John Wiley & Sons, Inc. Int J RF and Microwave CAE 10: 379–382, 2000.     

基于遗传算法的信息系统可靠性优化设计

设备冗余是信息系统进行可靠性优化设计的常用策略之一,其主要问题在于冗余设备的选择和配置,以达到满足一定可靠性要求下实现成本最小化的目的。这是一类结构复杂的规划问题,很难采用传统的数值算法进行求解,遗传算法提供了有效的解决方法。首先运用信息系统Petri网模型的层次结构分析结果,给出区分结点重要度的系统可靠性度量公式。在此基础上提出优化模型,给出遗传算法求解优化问题的步骤,并通过实例证明了方法的有效性及实用性。     

A multiple objective grouping genetic algorithm for assembly line design

The purpose of this paper is to describe some of the main problems concerning assembly line design. The focus will be on the following steps: (1) the input data preparation, (2) the elaboration of the logical layout of the line, which consists in the distribution of operations among stations along the line and an assignment of resources to the different stations, (3) finally the mapping phase using a simulation package to check the obtained results. This work presents a new method to tackle the hybrid assembly line design, dealing with multiple objectives. The goal is to minimize the total cost of the line by integrating design (station space, cost, etc.) and operation issues (cycle time, precedence constraints, availability, etc.). This paper also presents in detail a very promising approach to solve multiple objective problems. It is a multiple objective grouping genetic algorithm hybridized with the multicriteria decision-aid method PROMETHEE II. An approach to deal with users preferences in design problems is also introduced. The essential concepts adopted by the method are described and its application to an industrial case study is presented.     

Approximate analysis of multiqueue systems with multiple cyclic servers

In this paper we consider an approximate model of a system of multiple queues served cyclically by a number of identical servers, which is an extension of the Kuehn model for the single server multiqueueing system. The arrival process of customers is Poissonian, walking and service times are general, servers work in the repeated mode, and they serve at most one customer per visit at a queue (node) in a cycle. Applications in the performance evaluation of the existent network of processors of the Brazilian switching system TRÓPICO are considered. The approximation is validated by means of computer simulations.     

基于协同进化遗传算法的神经网络优化

人工神经网络的结构设计没有系统的规律可循,而基于梯度的神经网络参数优化又易于陷入局部最优解.该文研究了用带退化的协同进化遗传算法来优化神经网络结构,同时优化网络参数.将网络参数作为实数编码基因进行遗传选择,参数个体的受损率超过退化阀值时发生结构退化.退化进程由协同进化的控制个体动态控制.实验证明,该方案能够有效简化神经网络的结构和得到最优网络参数,收敛速度比常规遗传算法快.     

Proxy-assisted periodic broadcast for video streaming with multiple servers

Large scale video streaming over the Internet requires a large amount of resources such as server I/O bandwidth and network bandwidth. A number of video delivery techniques can be used to lower these requirements. Periodic broadcast by a central server combined with proxy caching offers a significant reduction of the aggregate network and server I/O bandwidth usage. However, the resources available to a single server are still limited. In this paper we propose a system with multiple geographically distributed servers. The problem of multiple servers for periodic broadcast is quite different from the problem of object location for multiple web servers. Multiple servers offer increased amount of resources and service availability and may potentially allow a further reduction of network bandwidth usage. On the other hand, the benefit of periodic broadcast mostly comes from high demand videos. With multiple servers holding a video, the demand of the video at each server is reduced. Therefore, it is a challenge to use multiple servers efficiently. We first analyze the dependence of the resource requirements on the number and locations of the servers. Based on the character of the function describing such a dependence, we formulate and solve the problem of video location and delivery, in a way that minimizes resource usage. We explore a trade-off between network and I/O bandwidth requirements. We evaluate our proposed solutions through a number of tests.

基于自适应模拟退火遗传算法的多杂质用水网络设计

水资源的短缺和环境污染的日益严重,对过程工业提出了减少新鲜水用量和废水排放量的要求,且通常废水中都含有多种污染物,由此本文提出了考虑回用的多杂质用水网络设计。不但建立了多杂质用水网络超结构MINLP模型,而且针对MINLP问题求解困难的现状,开发了自适应模拟退火遗传算法。实例研究结果表明该算法可以找到全局最优解且计算时间可满足要求。另外,该算法可有效避免陷入局部最优,也不要求提供初始可行解。     

并行多目标遗传算法在控制系统优化中的应用

为了提高复杂控制系统设计问题中的效率,提出了一种基于主从模型的并行多目标遗传算法的优化器模型。主进程进行各类遗传操作和最优排序操作,所有进程都进行目标函数值和约束函数值的运算操作,并采用动态负载平衡策略。将该优化器应用在飞行器控制系统设计中,显示出了该算法的优良效果。     

一维下料的改进遗传算法优化

针对一维下料优化问题,在对一维下料方案数学模型分析的基础上,提出了基于改进遗传算法的优化求解方案。主要思想是把零件的一个顺序作为一种下料方案,定义了遗传算法中的关键问题:编码、解码方法、遗传算子和适应度函数的定义。该算法设计了一种新颖的遗传算子,包括顺序交叉算子、线性变异算子、扩展选择算子。根据这一算法开发出了一维下料方案的优化系统。实际应用表明,该算法逼近理论最优值,而且收敛速度快,较好地解决了一维下料问题。     

自适应多策略花朵授粉算法

针对花朵授粉算法极易陷入局部最优解且寻优精度不高的问题,提出自适应多策略花朵授粉算法(self-adaptive flower pollination algorithm with multiple strategies,SMFPA)。利用锚点策略提高种群的多样性,采用摄动策略改善全局勘探能力,采用局部搜索增强策略提升其开采最优解的能力。为验证SMFPA的性能,比较5种算法在解决12个测试问题上的寻优结果,实验结果表明,在寻优速度以及寻优精度方面,SMFPA算法表现更优。通过比较算法在管柱设计问题上的寻优结果,进一步评估SMFPA的寻优性能。     

改进的多目标遗传算法在营养决策中应用

将多目标遗传算法NSGA-Ⅱ应用于营养决策优化。采用多维实数向量的编码方式,使用状态转移表对遗传算子进行描述。对NSGA-Ⅱ算法进行改进,在进化操作中增加了随机变换运算和删除运算,加快了算法的收敛并避免了早熟。仿真结果证明该算法能逼近Pareto域,并在该域中均匀分布,经一次运行便可提供更多科学合理的营养决策优化候选方案。     

基于自适应递阶遗传算法的神经网络优化策略

基于递阶结构的遗传算法可以同时对多层前向神经网络进行结构优化和权重求解。与基本的遗传算法相比，这种算法不仅在权重训练方面更加快速稳定，而且能在学习过程中确定网络的拓扑结构，具有较高的学习效率，而在遗传过程中采用自适应的交叉和变异概率能有效加快遗传速度和避免早熟现象的出现。     

基于遗传算法的人工鱼群优化算法

人工鱼群算法(AFSA)是一种高效的群智能全局优化技术.通过对人工鱼群算法(AFSA)不足的研究,在遗传算法的基础上,提出了基于遗传算法的人工鱼群优化算法.该算法保留了人工鱼群算法(AFSA)简单、易实现的特点,同时克服了人工鱼漫无目的的随机游动或在非全局极值点的大量聚集,显著提高了算法的运行效率和求解质量.最后通过大量的函数和实例测试结果表明,与其它算法相比,该算法是可行和有效的,具有运行速度快和求解精度高等特点.     

基于遗传算法的一种生物序列比对方法

生物序列比对是对DNA（或RNA,蛋白质）序列,寻找和确定它们的相似部分或稳定区域.二重序列比对问题可采用动态规划方法求得其最优解;多重序列比对问题是一个NP完全的组合优化问题,有待进一步探索与研究.通过合理的编码表示,采用相应的遗传算子,设计了一种求生物序列比对的遗传算法.并对几组DNA序列进行了测试.     

A new grouping genetic algorithm approach to the multiple traveling salesperson problem

The multiple traveling salesperson problem (MTSP) is an extension of the well known traveling salesperson problem (TSP). Given m > 1 salespersons and n > m cities to visit, the MTSP seeks a partition of cities into m groups as well as an ordering among cities in each group so that each group of cities is visited by exactly one salesperson in their specified order in such a way that each city is visited exactly once and sum of total distance traveled by all the salespersons is minimized. Apart from the objective of minimizing the total distance traveled by all the salespersons, we have also considered an alternate objective of minimizing the maximum distance traveled by any one salesperson, which is related with balancing the workload among salespersons. In this paper, we have proposed a new grouping genetic algorithm based approach for the MTSP and compared our results with other approaches available in the literature. Our approach outperformed the other approaches on both the objectives.     

基于多智能体遗传算法的战术航段优化

针对飞机战术飞行要求和威胁规避目标的问题,采用优势函数和战术规避相结合的原则,将战术航段优化问题转化为路径搜索问题,提出了基于多智能体遗传算法来解决此问题.采用自适应交叉和变异算子,改进自学习算子获取子代的算法,实现了全局最优的结果.通过和传统遗传算法进行仿真比较,相比之下,基于多智能体的遗传算法可以有效利用地形,实现战术飞行.     

多周期多原料的供应链网络成本优化及其混合遗传算法

建立并求解一个基于成本最小的供应链网络模型.与以往研究不同,在该模型中生产一种产品需要至少两种原料,每种原料都可以由备选供应商提供.根据模型的特点,用0、1代表对原材料供应商、工厂和分销中心的选择情况,以MATLAB 7.6为平台,运用Sheffield大学的遗传算法工具箱,将遗传算法与线性规划算法相结合,实现了模型的求解.算例结果表明,给出的染色体编码方案正确,混合遗传算法有效,能解决多周期、多原料的供应链网络成本优化问题.还探讨了需求和距离变化,以及需求随机时对最优成本和最优个体的影响.研究表明,需求变化的影响大于距离变化的影响,需求随机对最优成本和最优个体的影响不大.     

Interval optimization of rotor-bearing systems with dynamic behavior constraints using an interval genetic algorithm

A new interval optimization algorithm is presented in this paper. In engineering, most optimization algorithms focus on exact parameters and optimum objectives. However, exact parameters are not easy to be manufactured to because of manufacturing errors and expensive manufacturing cost. To account for these problems, it is necessary to estimate interval design parameters and allowable objective error. This is the first paper to propose a new interval optimization algorithm within the context of Genetic Algorithms. This new algorithm, the Interval Genetic Algorithm (IGA), can neglect interval analysis and determines the optimum interval parameters. Furthermore, it can also effectively maximize the design scope. The optimizing ability of the IGA is tested through the interval optimization of a two-dimensional function. Then the IGA is applied to rotor-bearing systems. The results show that the IGA is effective in deriving optimal interval design parameters within the allowable error when minimizing shaft weight and/or transmitted force of rotor-bearing systems.     

基于遗传算法的大规模矩形件优化排样

大规模矩形件优化排样是一个典型的组合优化问题，属于NP-hard问题．实际工程中对一个排样方案一般有满足“一刀切”的工艺要求，“一刀切”要求增加了对排样的约束．提出的优化算法，将矩形匹配分割算法作为遗传算法染色体的解码器实现一个排样方案，用遗传算法进行排样方案的全局搜索．算例比较表明，该算法可以求得满足“一刀切”约束的最优解．