基于遗传算法的热管多目优化设计

热管作为一种高效热传输设备,被广泛应用于许多领域.对高效热管的优化设计,通常会涉及到多个目标参数,传统的设计方法往往无法同时有效地优化多个目标.首先通过对热管建立热网络分析模型和热传输极限模型,给出了热管多目标优化问题的数学描述.其次设计了基于遗传算法的多目标优化算法,并通过多个算例证实了该算法的有效性及优越性.由于数学问题的提出是基于热管的基本原理,因此该算法具有可推广性,可用于多种形式的热管设计.     

基于遗传算法的板翅式换热器优化设计

以板翅式换热器的质量为目标函数,以换热器芯体外形尺寸和冷热两侧翅片参数为优化变量,分别采用改进遗传算法和基本遗传算法对其结构尺寸进行优化设计.结果表明,与原始数据相比,换热器的质量明显减小,同时证明改进遗传算法的有效性和先进性.     

给水管网管径优化设计的遗传算法

给水管网管径设计是离散变量的非线性优化问题，常规的数学规划方法采用连续变量求解，其最优解受初始值影响大。本文提出一种新方法－－遗传算法，它的特点在于：从多个初始点开始寻优，并采用交迭和变异算子避免过早地收敛到局部最优解，可获得全局最优解，且不受初始值影响。该算法不必求导计算，编程简单。     

基于遗传算法的谐波齿轮可靠性优化设计

谐波齿轮具有优异的特性,在传动系统中的应用越来越广泛.针对谐波齿轮,考虑其设计过程中的各种不确定因素,根据其失效形式建立谐波齿轮的可靠性分析模型.在可靠性理论和优化设计方法的基础上,将可靠性设计与遗传算法结合起来;根据应力-强度干涉理论使用一次二阶矩法对谐波齿轮进行了优化设计;使用矩阵微分技术,分析了设计变量对结构可靠性的影响程度,即灵敏度.并使用蒙特卡洛模拟的方法对可靠性进行了验证.在基本随机参数的前二阶矩已知的情况下,通过计算机程序可以迅速准确地得到谐波齿轮可靠性优化设计信息.数值算例表明,所提出的方法是一种实用、有效的方法.     

遗传算法用于衍射光学元件的优化设计

提出了一种基于遗传算法的衍射光学元件优化设计方法;在衍射光学元件设计中遗传算法运行参数对遗传算法性能有一定的影响:采用较大的群体规模,遗传算法越容易获得最优解;交叉算子越大,遗传算法全局搜索能力越强;选择算子对遗传算法的影响不是太大;如果要进一步提高解的精度,可选取较大的终止代数。数值计算结果表明,用遗传算法优化设计的衍射光学元件,其误差小于 5.2%,衍射效率达到 91.2%。遗传算法很适合衍射光学元件的优化设计。     

《基于多目标遗传算法的消声器优化设计》

四端网络法结合遗传算法设计消声器。以四端子网络为理论基础建立消声器的插入损失、传递损失、噪声降低的模型,模拟计算出消声器的消声特性响应曲线。应用多目标遗传算法对得到的消声器的噪声特性进行优化,结果使消声器的消声性能得到明显的改善,可以缩短设计周期,提高效率。     

基于改进神经网络和遗传算法的真空灭弧室优化设计

提出一种基于人工神经网络和遗传算法相结合的电器电场均匀度优化设计方法,以12 kV真空灭弧室为研究对象,对其内部电场进行场影响因素分析与电极型面优化,以实现均匀场设计。建立了以悬浮屏蔽电极长度和端部型面曲率、动静触头电极端部曲率为输入变量,灭弧室内部电场均匀度为输出变量的改进神经网络模型结构;并以灭弧室内部电场均匀度为优化目标,采用遗传算法对灭弧室结构参数进行寻优,以提高灭弧室静态绝缘性能。     

变频空调散热器仿真优化设计

为节约开发成本,缩短开发周期,更好满足市场需求,论文以某款变频空调室外机的电子元器件散热为研究对象。在不改变电器盒及散热器安装位置的前提下,利用CFD模拟手段进行散热器仿真分析及优化设计。经实验方案验证,最终在短时期内设计出较优的散热器。     

遗传算法加权中值滤波器的优化设计

当测试数据中随机干扰满足高斯分布时,可以采用线性滤波器进行信号处理得到所要求的有用信号.而当随机干扰为非高斯分布时,则必须采用非线性的信号处理方法才能获得所要求的有用信号.这里讨论一类非线性滤波器——加权中值滤波器的最优设计问题.取损失函数为绝对误差的数学期望值,采用实数值编码多子种群的标准遗传算法来极小化损失函数.由于遗传算法:是用点群进行寻优,而不是用一个单点进行寻优,具有隐含并行算法的特点;群体在每一代的进化过程中执行同样的复制、交叉、变异操作,仅使用问题本身所对应的适应度函数,而不需要任何其它先决条件或辅助信息;遗传算法使用随机转换规则,而不是确定性规则进行运算.遗传算法作为一类全局最优算法,它所得到的加权中值滤波器也是全局最优的.数值计算结果表明,采用遗传算法可以得到更小的绝对误差平均值,且优于LMA算法.     

遗传算法用于电子枪发射系统的全局最优化设计

常用的优化设计方法 ,如单纯形法、Powell法等 ,易陷入局部最优解。而遗传算法是一种新兴的直接搜索最优化算法 ,它模拟达尔文遗传选择与自然进化的理论 ,根据“适者生存”和“优胜劣汰”的原则 ,借助“复制”、“交换”、“突变”等操作可以得到全局最优解。本文将遗传算法运用于电子枪发射系统的最优化设计 ,得到了使交叠点半径尽可能小的发射系统的最佳结构和相应电参量     

Optimal design of unit hydrographs using probability distribution and genetic algorithms

A nonlinear optimization model is developed to transmute a unit hydrograph into a probability distribution function (PDF). The objective function is to minimize the sum of the square of the deviation between predicted and actual direct runoff hydrograph of a watershed. The predicted runoff hydrograph is estimated by using a PDF. In a unit hydrograph, the depth of rainfall excess must be unity and the ordinates must be positive. Incorporation of a PDF ensures that the depth of rainfall excess for the unit hydrograph is unity, and the ordinates are also positive. Unit hydrograph ordinates are in terms of intensity of rainfall excess on a discharge per unit catchment area basis, the unit area thus representing the unit rainfall excess. The proposed method does not have any constraint. The nonlinear optimization formulation is solved using binary-coded genetic algorithms. The number of variables to be estimated by optimization is the same as the number of probability distribution parameters; gamma and log-normal probability distributions are used. The existing nonlinear programming model for obtaining optimal unit hydrograph has also been solved using genetic algorithms, where the constrained nonlinear optimization problem is converted to an unconstrained problem using penalty parameter approach. The results obtained are compared with those obtained by the earlier LP model and are fairly similar.     

Foil strip type heat sinks for low temperature use

Two types of heat sinks for cryogenic applications have been made with commercial technology. Both are described and analysed.     

遗传算法在两分频扬声器系统设计中的应用

文章论述了运用遗传算法优化设计两分频扬声器分频网络的方法。使用实测的扬声器阻抗、频响数据,模拟扬声器接入分频网络后扬声器系统的响应,并运用遗传算法对系统响应进行优化设计。文中给出的实例表明,设计结果和实测的曲线是一致的。     

Rolling element bearing design through genetic algorithms

The design of rolling element bearings has been a challenging task in the field of mechanical engineering. While most of the real aspects of the design are never disclosed by bearing manufacturers, the common engineer is left with no other alternative than to refer to standard tables and charts containing the bearing performance characteristics. This paper presents a more viable method to solve this problem using genetic algorithms (GAs). Since the algorithm is basically a guided random search, it weakens the chances of getting trapped in local maxima or minima. The method used has yielded improved performance parameters than those catalogued in standard tables.     

Optimal packetization scheme for embedded multimedia bitstreams using genetic algorithms

The problem of packetizing embedded multimedia bitstreams into fixed‐size packets is investigated and an optimal packetization scheme using a genetic algorithm is presented. In the proposed method, each individual packetization instance is represented by a decision sequence and mapped to a chromosome. A steady‐state genetic algorithm is applied to search for the optimal chromosome that minimizes the distortion between the original and the reconstructed media. In addition, we propose a fast method to calculate the fitness value based on the distortion associated with each chromosome to speed up the evolution process. The computer simulation results show that our proposed packetization scheme has high compression efficiency and provides error resiliency to packet losses with a relatively fast speed. © 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 77–84, 2006     

Engineering design optimization using species-conserving genetic algorithms

The species conservation technique described here, in which the population of a genetic algorithm is divided into several groups according to their similarity, is inspired by ecology. Each group with similar characteristics is called a species and is centred on a dominating individual, called the species seed. A genetic algorithm based on this species conservation technique, called the species-conserving genetic algorithm (SCGA), was established and has been proved to be effective in finding multiple solutions of multimodal optimization problems. In this article, the SCGA is used to solve engineering design optimization problems. Different distance measures (measures of similarity) are investigated to analyse the performance of the SCGA. It is shown that the Euclidean distance is not the only possible basis for defining a species and sometimes may not make sense in engineering applications. Two structural design problems are used to demonstrate how the choice of a meaningful measure of similarity will help the exploration for significant designs.     

基于整数编码遗传算法的传感器优化配置研究

动态测试中,为了将传感器配置在合理的自由度上,以便充分反映结构的动力特性,需对传感器进行优化配置。本文分别以模态置信度矩阵、Fisher信息阵和它们的组合为优化准则,采用整数编码遗传算法,探讨了单目标和多目标优化的传感器优化配置问题。通过与现有的模态动能法、有效独立法及基于QR分解的逐步累积法进行比较,传感器优化配置的结果表明整数编码遗传算法优于上述三种现有方法。