THE INVERSE ANALYSIS OF INTERNAL DEFECTS (INCLUSIONS) IN STRUCTURES USING A COMBINED XFEM AND GA METHOD

建立扩展有限元法与遗传算法相结合的结构缺陷反演分析模型.扩展有限元法通过引入不连续位移模式使得网格剖分无需依赖结构内部的不连续界面.通过改变水平集函数表征结构缺陷（夹杂）的位置和大小，遗传算法在每次迭代过程中具有全局和局部搜索能力，通过评估响应测点的响应量适应度值决定是否进一步迭代.对带有单个圆形缺陷（夹杂）和多个缺陷（夹杂）的结构进行了反演分析，并就响应测点的布置进行了讨论.结果表明，建立的反演分析模型能准确地探测结构存在的单个甚至多个缺陷（夹杂）.     

基于混合编码遗传算法和有限元分析的压电结构载荷识别

与传统的优化算法相比,遗传算法不需要计算目标函数的导数信息,便于迭代,可实现全局寻优.因此,本文提出一种采用混合编码的遗传算法与有限元分析相结合,对复合材料层合板、壳进行载荷识别的新方法.在遗传算法求解过程中,设计变量的编码方法选择是其重要环节,二进制编码容易产生连续函数离散化时的映射误差,且其求解精度与染色体的编码长度紧密相关,过长的染色体描述虽可提高精度,但会显著降低算法的求解效率.为此,本文提出采用混合编码的方法进行载荷识别,即用二进制编码表征载荷作用位置,浮点数编码表示载荷的大小.这一方法大大降低了染色体的长度,并显著提高了计算效率和精度.     

Optimization of solar air collector using genetic algorithm and artificial bee colony algorithm

Thermal performance of solar air collector depends on many parameters as inlet air temperature, air velocity, collector slope and properties related to collector. In this study, the effect of the different parameters which affect the performance of the solar air collector are investigated. In order to maximize the thermal performance of a solar air collector genetic algorithm (GA) and artificial bee colony algorithm (ABC) have been used. The results obtained indicate that GA and ABC algorithms can be applied successfully for the optimization of the thermal performance of solar air collector.     

Evolutionary optimization techniques as versatile solvers for hard‐to‐converge problems in computational fluid dynamics

Evolutionary algorithms mimic the process of natural evolution governed by the ‘survival of the fittest’ principle. In this work, a genetic algorithm (GA) is successfully used to solve problems in potential flow in a gradual contraction, viscous flow over a backward facing step, and non‐Newtonian flow using the power law model. Specifically, the GA heuristically searches the domain for potential solutions, precluding many convergence difficulties associated with the stiffness of a problem. The GA was able to solve problems that the gradient‐based method could not mainly because of its relative indifference to regions of high gradients when performing the search and that systems of discretized equations are never actually solved. The GA exhibited excellent scalability properties for solving problems with a large number of nodes. These results show evolutionary techniques to be of great utility in solving stiff problems in fluid flow. Copyright © 2006 John Wiley & Sons, Ltd.     

基于Kriging代理模型的结构形状优化方法研究

建立了一种进行结构形状优化的工程方法。该方法利用试验设计法选取位置变量样本点,并对各个样本点对应的结构进行尺寸优化,然后根据样本点和尺寸优化结果建立Kriging代理模型,再利用遗传算法求出位置变量最优解,进而得到结构最优解。最后进行了15杆平面拱式桁架、悬臂梁和25杆空间桁架三个形状优化算例,算例分析结果表明:该方法思路清晰、适应性强、优化效果明显,具有较大的工程应用价值。     

基于ESGA遗传算法的水射流自驱旋转喷头优化设计

超高压水射流自驱旋转型喷头是目前广泛应用于船壁除锈的一种装置,其布局方式直接影响船壁除锈的效率和质量,目前喷头布局多依赖工程经验,缺少准确的理论分析和优化技术支持。针对水射流自驱旋转型喷头的布局优化问题,在传统遗传算法（genetic algorithm,GA）的基础上,提出一种基于“锦标赛选择”的精英策略遗传算法（elitist strategy genetic algorithm,ESGA）,该算法通过采用种群进化过程中精英个体直接保留到下一代的进化策略,从而有效提高算法的全局收敛能力和算法的鲁棒性。结合旋转喷头扫掠冲击性能和轨迹特征,以喷头移动路径垂直打击面上的能量分布均匀度为衡量标准,建立超高压水射流自驱旋转型喷头的螺旋扫掠冲击离散化时间优化模型,并分别利用两种遗传算法对其进行优化改进。对一字形水射流自驱旋转型喷头的布局优化研究发现,经ESGA算法优化的旋转喷头,其扫掠冲击能量分布均匀度较原喷头布局提升了47.2%,其收敛精度也高于GA算法。经对ESGA算法优化后的喷头实验验证发现,ESGA优化方案较原设计方案除锈效率提高了42.0%。改进的ESGA优化算法可行性强,能够在收敛...     

桁架拓扑优化的多点逼近遗传算法

提出一种基于多点逼近函数和遗传算法的桁架拓扑优化方法。该方法建立了包含连续尺寸和离散拓扑两类变量的优化模型，并通过构造多点逼近函数建立了结构优化问题的第一级序列显式近似，然后采用分层优化方法：在外层对拓扑变量采用遗传算法进行优化；在内层对尺寸变量通过可由对偶法求解的第二级序列近似问题进行优化。几个经典的桁架拓扑优化算例表明该方法能以较少的结构分析次数获得比较理想的概率意义上的最优解。     

基于遗传算法和参数化建模的非线性结构优化

提出了一种对存在接触关系的非线性结构(装配体)进行优化设计的新方法。该方法将遗传算法与结构几何及有限元参数化建模方法相结合，在通用CAE软件的二次开发编程环境中实现对带接触的结构装配体进行结构尺寸和形状优化设计。文中利用该方法对某浮动式闭气结构的重要结构参数和关键构件形状实施了优化设计，使其闭气性能得到大幅度提高，体现了本文方法在解决这类优化问题中的优势。本文的方法有利于拓宽结构优化技术在机械设计领域的应用范围。     

基于分段进化的遗传算法在机翼气动外形设计上的应用

与传统的优化方法相比,遗传算法以其极强的鲁棒性、随机搜索特性以及优化结果的全局性等特点而在工程优化中得到越来越广泛的应用.标准遗传算法中使用的二进制编码类似于生物染色体的组成,使算法易于用生物遗传学理论加以解释,同时也使交叉、变异等遗传操作易于实现.此外,使用二进制编码还有助于充分发挥算法隐含的并行性.本文对传统遗传算法加以改进,在二进制编码下引入分段进化的概念,再配以高效的交叉、变异算子,充分发挥二进制编码固有优势的同时在很大程度上提高了算法的优化效率,并与Euler方程数值解法相结合,对机翼外形进行了气动优化设计.优化后机翼的升阻比有了显著提高,表明建立的优化模型是合理有效的.     

遗传算法求解可行域分离的结构优化问题

应用遗传算法求解了两类可行域分离的结构优化问题：局部屈曲约束的桁架拓扑优化问题和动力响应约束优化问题．对第一类问题，提出了新的数学表达式，适合于遗传算法求解．采用了改进的适应度函数及约束处理方法、约束凝聚选择、交叉操作改进和竞争最优保留，提高了遗传算法的效率和可靠性．算例说明，该方法能够克服可行域分离给传统优化算法带来的困难，有效地在多连通可行域中搜索全局最优解．     

改进单向搜索遗传算法的工程结构优化设计

本文基于规范规定的约束条件和各项技术标准要求，建立了离散变量结构优化模型。针对遗传算法在迭代过程中经常出现未成熟收敛、振荡、随机性太大和迭代过程缓慢等缺点，提出一种新的遗传算子单亲遗传算子，用于对遗传算法的改进。并提出一种离散变量结构优化设计的单向搜索算法与遗传算法结合在一起解决问题。优化设计结果表明，这种改进单向搜索遗传算法的收敛特性得到了很好的改善，即发挥了单向搜索算法局部搜索能力强的特点，又发挥了遗传算法全局性好的特点。该方法是一种有效的工程结构优化设计方法。     

On the use of genetic algorithm for finding the neutral instability curve in plane Poiseuille flow

A novel method based on genetic algorithm (GA) is proposed, to the best of our knowledge for the first time, for finding the neutral instability curve of the Orr-Sommerfeld equation in (nearly) parallel flows. New concepts such as “proximity of parents” and “gender discrimination” are added to the conventional GA in order for this algorithm to find the neutral instability curve. Certain GA operators such as “crossover” and “mutation” will also be modified in such a way that this algorithm can meet this purpose. To check the applicability of the modified genetic algorithm (MGA) developed in this work in finding the neutral instability curve, the case of plane Poiseuille flow will be used as a benchmark. It will be shown that the modified genetic algorithm developed in this work is well capable of determining the neutral instability curve for this particular flow geometry.     

基于残余力向量法和改进遗传算法的结构损伤识别研究

提出了一种基于残余力向量法和改进遗传算法的结构损伤识别方法。文中首先对残余力向量法和遗传算法的基本理论进行了介绍。在无噪声的情况下,使用任意一阶模态数据,残余力向量法都能够对损伤进行准确定位。但是,振动测试数据中往往包含噪声,导致运用残余力向量法进行损伤识别完全不可行。考虑到这个问题,在常规模态分析的基础上,以节点的残余力向量构造用于遗传搜索优化的目标函数形式,然后利用改进的遗传算法重点进行了噪声条件下的结构损伤定位和定量研究。最后,本文用一个平面桁架模型进行了数值模拟,验证了所提出方法的有效性,并对方法应用中存在的一些问题进行了深入分析,得出了一些有益的结论。     

The worst response of mistuned bladed disk system using neural network and genetic algorithm

The objective of this paper is to develop an integrated approach using artificial neural networks (ANN) and genetic algorithms (GA) for predicting the worst response of mistuned bladed disk. ANN is used to predict the responses of bladed disk system which are used further in evaluation of fitness and constraint violation in GA process. A multilayer back-propagation neural network is trained with the results obtained from finite element model for different bladed disk configurations. Subsequently, GA is employed for arriving at optimum configuration of the bladed disk system by maximizing the blade responses. By integrating ANN with GA, the computational time required for obtaining optimal solution could be reduced substantially. The efficacy of this approach is demonstrated by carrying out studies on mistuned bladed disk systems for different sets of mistuning parameters, namely mistuning in modulus of elasticity and length of blades. Finally, the effect of adding shroud at the tip of blades in reducing the maximum response of the bladed disk system was investigated.     

基于改进遗传算法的桥梁结构损伤识别应用研究

提出了一种基于残余力向量和改进遗传算法的桥梁结构损伤识别方法。在无噪声的情况下,使用任意一阶模态数据,残余力向量法都能够对损伤进行准确定位。但是,振动测试数据中往往包含噪声,导致运用残余力向量法进行损伤识别完全不可行。考虑到这个问题,在常规模态分析的基础上,以节点的残余力向量构造目标函数,提出了一种用于遗传搜索优化的目标函数形式。利用改进遗传算法重点进行了噪声条件下的结构损伤定位和定量研究,并对遗传算法的参数选取问题进行了深入探讨。遗传算法的主要改进包括:采用浮点编码、采用基于标准化几何分布排名的选择策略、采用最优保存策略、采用算术交叉算子、采用自适应变异算子。最后,本文用一个连续梁桥模型进行了数值模拟,验证了所提出方法的有效性,并对方法应用中存在的一些问题进行了深入探讨。     

AN EFFICIENT GENETIC ALGORITHM FOR LARGE SCALE BUILT-UP STRUCTURAL OPTIMIZATION

In this paper,a new zigzag method for plate structures and a geneticalgorithm (GA) of dynamic source seed spaces are developed and a combination ofthem is used to deal with large scale built-up structural optimization.The new GAcombined with the zigzag method can work efficiently when coping with large scalestructural optimization included displacement and stress constraints.Examples showthat this GA is robust and can be used for many complex structural optimizationproblems.     

基于遗传算法的GPS姿态测量技术

提出了一种新的GPS载波相位姿态测量搜索技术。它是一种基于遗传算法（Genetic Algorithm,GA)，采用二值编码，最佳保留（Elitist Model)选择机制和模糊度函数法的适应度函数，并以分级方式（粗搜索GA和精搜索GA）进行最优搜索，实现GPS姿态快速测定。试验结果表明：该方法对复杂搜索之间的搜索效率高，速度快，并且对周跳不敏感。     

基于自适应遗传算法的Stewart平台结构双目标优化设计

探讨了大射电望远镜精调Stewart平台的设计准则,建立了综合衡量Stewart平台运动精度和质量的双目标优化模型,以其结构强度和固有频率为约束条件,采用自适应遗传算法对该优化问题求解。通过与采用标准遗传算法所得优化结果的比较,验证了自适应遗传算法在Stewart平台结构参数设计中的有效性。     

遗传算法改进及其在岩土参数反分析中的应用

本文的主要目的是开发基于实数编码的杂交遗传算法来识别土体的本构参数。该杂交遗传算法在经典遗传算法框架下开发,融合两个新开发的交叉算子,形成了一个新的杂交策略。为了保持种群的多样性,在算法中采用了一个动态随机变异算子。另外,为了提高算法收敛性,采用了一个基于混沌的局部搜索技术。分别基于室内试验和现场试验,通过识别土的本构参数来测试新算法的搜索能力和搜索效率。为了测试新开发算法的突出表现,特选用5种经典的随机类算法（遗传算法、粒子群算法、模拟退火算法、差分算法和蜂巢算法）,分析同样的案例进行比较。结果表明,在收敛速度和最优解的准确度方面,新改进的算法可以很好地处理岩土工程的参数反演。     

Correlation of viscosity in nanofluids using genetic algorithm-neural network (GA-NN)

An accurate and efficient artificial neural network based on genetic algorithm (GA) is developed for predicting of nanofluids viscosity. The genetic algorithm (GA) is used to optimize the neural network parameters. The experimental viscosity in eight nanofluids in the range 238.15–343.15 K with the nanoparticle volume fraction up to 9.4% was used. The obtained results show that the GA-NN model has a good agreement with the experimental data with absolute deviation 2.48% and high correlation coefficient (R ≥ 0.98). The Results also reveals that GA-NN model outperforms to the conventional neural nets in predicting the viscosity of nanofluids with the overall percentage improvement of 39%. Furthermore, the results have also been compared with Einstein, Batchelor and Masoumi et al. models. The findings demonstrate that this model is an efficient method and have better accuracy.