随机小生境遗传算法求解多目标优化问题

针对广东省某县区域经济发展的现状,提出了改进的经济多目标优化模型。针对实际系统提出了随机小生境Pareto遗传算法(RNPGA),并将之应用于区域模型的计算与仿真。仿真结果表明了此方法的有效性与先进性。     

Optimal maintenance policies for systems subject to a Markovian operating environment

Many stochastic models of repairable equipment deterioration have been proposed based on the physics of failure and the characteristics of the operating environment, but they often lead to time to failure and residual life distributions that are quite complex mathematically. The first objective of our study is to investigate the potential for approximating these distributions with traditional time to failure distribution. We consider a single-component system subject to a Markovian operating environment such that the system’s instantaneous deterioration rate depends on the state of the environment. The system fails when its cumulative degradation crosses some random threshold. Using a simulation-based approach, we approximate the time to first failure distribution for this system with a Weibull distribution and assess the quality of this approximation. The second objective of our study is to investigate the cost benefit of applying a condition-based maintenance paradigm (as opposite to a scheduled maintenance paradigm) to the repairable system of interest. Using our simulation model, we assess the cost benefits resulting from condition-based maintenance policy, and also the impact of the random prognostic error in estimating system condition (health) on the cost benefits of the condition-based maintenance policy.     

A Randomized Stochastic Approximation Algorithm for Self-Learning

A new stochastic approximation algorithm with input perturbation for self-learning is designed with test perturbations and has certain useful properties, such as consistency of estimates under almost arbitrary perturbations and preservation of simplicity and performance with the growing size of the state space and increasing number of classes. An example on computer-aided modeling of learning is given to illustrate the performance of the algorithm.__________Translated from Avtomatika i Telemekhanika, No. 8, 2005, pp. 52–63.Original Russian Text Copyright © 2005 by Granichin, Izmakova.     

分布式系统中任务分配的一种结点自适应算法

基于一个非常简单的假设：各独立结点对系统反馈的认识具有一致性，提出了一种结点自适应算法，文中运用概率论的相关知识，分析了算法的各项重要性能指标，给出了一般情况下的表达式，并运用分段讨论和求极值等方法得出了这些表达式在特殊情况下的简化形式，算法所需条件较弱，具有广泛的适应性。     

遗传算法的应用举例

遗传算法作为一种通用、高效的优化算法,已应用到工程计算的各个领域。该文首先简要阐述了遗传算法的基本原理和其操作步骤。同时为了验证其全局的寻优能力,采用MATLAB语言编制程序实现遗传算法对数值优化和旅行商问题的求解,需要说明的是这两类问题的程序编制和求解分别依赖于不同的已有遗传算法工具箱。为了便于说明遗传算法的优越性,分别将对数值优化和旅行商问题的计算结果与用局域搜索法和模拟退火得出的优化结果进行比较。比较结果表明,对于数值优化问题,遗传算法比局域搜索法具有更佳的寻优能力;对于旅行商问题的求解也能得到满意的结果。     

遗传算法的改进与应用

遗传算法是一种借鉴生物界自然选择和自然遗传机制的随机搜索算法,它与传统的算法不同。大多数古典的优化算法是基于一个单一的度量函数(评估函数)的梯度或较高次统计,以产生一个确定性的试验解序列;遗传算法不依赖于梯度信息,而是通过模拟自然进化过程来搜索最优解。该文针对传统遗传算法的缺陷,提出了一些新的改进思路,即从搜索技术和遗传算子等的角度来改进遗传算法。     

一种非线性遗传算法的设计与应用

为了提高传统遗传优化算法的收敛速度和寻优精度,提出了一种改进型非线性遗传算法（NGA）,算法具有父代个体没有复制操作和交叉规律和变异规律更易产生优良子代个体,且交叉概率因子和变异概率因子根据激素调节规律而自适应改变等特点。实践中选择两个典型测试函数对NGA的寻优效果进行了检验;通过检测将NGA应用于一种基于肾上腺激素调控机制的智能控制（ALIC）的优化仿真。仿真结果表明：与标准GA相比,NGA具有比较快的收敛速度和搜索精度;ALIC具有较好的优化控制效果。     

遗传算法及其应用

遗传算法是智能优化方法中应用最为广泛也最为成功的算法,在各个领域得到广泛应用。该文在介绍了遗传算法的发展历史和具体操作步骤的基础上,总结出遗传算法的特点,并对它的各个应用领域进行了详细阐述。     

Resource-allocation algorithm for operating systems

Cybernetics and Systems Analysis -     

Neuro-dynamic programming for designing water reservoir network management policies

Stochastic dynamic programming (SDP) can improve the management of a multipurpose water reservoir by generating management policies which are efficient with respect to the management objectives (flood protection, water supply for irrigation, hydropower generation, etc.). The improvement in efficiency is even more remarkable for networks of reservoirs. Unfortunately, SDP is affected by the well-known ‘curse of dimensionality’, i.e. computational time and computer memory occupation increase exponentially with the dimension of the problem (number of reservoirs), and the problem rapidly becomes intractable. Neuro-dynamic programming (NDP) can sensibly mitigate this limitation by approximating Bellman functions with artificial neural networks (ANNs). In this paper the application of NDP to the problem of the management of reservoir networks is introduced. Results obtained in a real-world case study are finally presented.     

求解应用层组播路由问题的遗传算法

分析了应用层组播路由模型，提出了更合理的应用组播路由模型．进一步给出了求解应用层组播路由问题的遗传算法，并分析了该算法的复杂性．大量的数值仿真表明该算法有较好的数值效果．     

受限p-中心的遗传算法及其应用

图论中的受限p-中心问题是NP-难问题,文中以遗传算法的基本思想为基础,改进了选择、交叉、变异算子,并利用受限节点的概念减少备择点,采用二次选择的策略加快收敛进程。应用到高等级路政管理站选址的优化配置中,取得了令人满意的效果。     

遗传正交试验算法及其应用

遗传算法是一种能够在较大的参数空间中搜索到问题最优解的方法,在解决非线性问题时具有全局收敛性,但收敛性能差。论文提出一种结合遗传与正交试验两种算法优点的新混合遗传算法,应用表明该算法收敛能力强、寻优能力强及能产生大量次优解,是一种值得信赖的算法。     

Path-clearing policies for flexible manufacturing systems

In practical manufacturing settings it is often possible to obtain, in real-time, information about the operation of several machines in a flexible manufacturing system (FMS) that can be quite useful in scheduling part flows. The authors introduce some scheduling policies that can effectively utilize such information and they provide sufficient conditions for the stability of two such policies     

Active sensing policies for stochastic systems

In systems with sensing cost, an active sensing policy is needed to determine when to collect sensing observations. This note presents an active sensing policy for systems with additive and parametric white noise. The policy uses an open-loop estimator between sensings and a Kalman filter when observations are requested. We present two active sensing policies. The goal of the first policy is to maintain the uncertainty (variance) of the state estimate below a given threshold. Sufficient conditions are presented that guarantee that this goal is achievable and will be met. The second policy senses when needed to distinguish discrete state regions for control. Sufficient conditions are presented that show within any specified probability, the control under the active sensing will be identical to the control under conventional sensing. Experiments demonstrate that sensing and sensing communications can be significantly reduced with active sensing policies, while still meeting control objectives     

遗传算法算子优化的应用

为了解决简单的遗传算法中容易出现"早熟收敛"等问题,需要对遗传算法进行改进.改善的算法采用自适应的交叉和变异算子,让交叉和变异概率随适应函数值的变化而变化.测试证明这是一种有效的优化方法,可以起到跳出"早熟",加速收敛的效果.     

遗传算法在水下机器人路径规划中的应用

提出一种分层路径规划算法来解决大范围海洋环境下的智能水下机器人(AUV)的全局路径规划问题。该算法将机器人的工作空间分层分解,并在每一层搜索路径,最终得到一条与障碍物无碰的全局路径。同时为解决算法的全局最优问题,使用遗传算法在每一层搜索路径。实践证明,该方法具有灵活、实用的特点,并能显著的节省内存空间。     

遗传算法在货位分配中的应用研究

本文讨论了货位分配算法。首先建立了货位分配算法的数学模型,然后提出了采用遗传算法解决这个多目标组合优化的问题。最终通过应用验证了算法的适用性。     

Resource monitors: A design methodology for operating systems

Resource manager objects are the operating system entities that manage computer system resources. In this paper we extend Hoare's and Brinch Hansen's monitor concept, and introduce a resource monitor language construct that represents a resource manager object. Resource monitors provide requesting processes with implicit synchronization, and do not require separate processes to perform the resource access operations. The resource monitor construct is compared to current language constructs used to structure operating system software. For this purpose we use an evaluation methodology that combines software complexity measurements with program performance measurements. The evaluation itself is carried out in a Concurrent Pascal-like programming environment. The current language constructs have a software complexity that is larger by 37 to 219 per cent over the resource monitor's complexity. The run-time synchronization overhead of programs that use current language constructs is 1.43 to 2.75 times higher than the overhead of programs that use a resource monitor.     

遗传算法在图像增强中的应用研究

关于图像增强是图像处理中提高质量问题,由于在图像采集过程中存在各种干扰,图像质量低,传统分段线性变换算法采用人工方法进行参数优化,难以获得最佳增强效果。为提高图像增强效果,提出一种采用遗传算法的图像增强算法,用遗传算法自动选择两个折线拐点的位置和分段直线的斜率,得到最优的分段线性变换曲线,从而实现图像增强处理。仿真结果表明,相对其它增强方法,改进方法不仅提高图像整体亮度,而且增强了图像的对比度和细节,具有一定的抗噪能力,为图像增强处理提供了广泛的应用前景。