混合变量桁架结构的拟分层遗传算法优化设计

混合变量桁架结构形状优化中采用并行计算思想,在单台计算机上实现了一种类似分层遗传算法的拟分层遗传算法。该算法能够产生更加平等的竞争机会,提供更多的优良个体,提高了种群多样性,同时不用人为的控制信息交换,再加上多层分级控制,一定程度上避免了标准遗传算法容易出现的"早熟"现象,加快了收敛速度,具有很高的搜索效率。用拟分层遗传算法解决25空间杆桁架结构形状优化问题的结果表明,这是一种解决具有连续、离散混合变量的桁架结构优化设计问题的很有效方法。     

基于改进萤火虫算法的杆系结构拓扑优化

基于萤火虫算法的基本原理及相应的改进策略,研究了离散变量杆系结构的拓扑优化问题。通过对初始萤火虫位置和位置更新公式进行离散,以使该算法适用于离散变量优化问题;同时,为克服在优化初期寻优速度慢的问题,对公式中的吸引力项进行改进,提出一种兼顾寻优效果和收敛速度的改进萤火虫算法。以基结构法为结构初始拓扑生成方法,以杆件截面为优化变量,以结构质量最轻为优化目标,提出了一种基于改进萤火虫算法的杆系结构拓扑优化方法。利用该方法分别对平面桁架和空间桁架结构进行拓扑优化分析,证明了所提方法的可行性和高效性。     

微分演化算法在桁架形状优化中的应用

为了获得全局最优和解决具有应力约束、几何约束以及局部稳定性约束的桁架形状优化问题中2类不同设计变量耦合给优化带来的困难,将1种新型智能优化算法——微分演化(Differential Evolution,DE)应用于桁架结构的形状优化问题中。给出了考虑节点坐标和截面面积两类不同性质的设计变量的桁架结构优化的数学模型,并对几个经典的桁架结构进行优化,将所得结果与其他优化算法结果进行了比较。数值结果表明了DE算法具有良好的收敛性和稳定性,可以有效地进行桁架结构的形状优化设计。     

一种应用于预应力钢结构的混合优化设计算法

提出了一种综合运用数学规划法、满应力准则法和遗传算法的混合优化设计算法,解决预应力钢结构优化设计中两类变量、三个方面的优化设计问题,分别为连续变量、非连续变量和形状优化、截面优化、预应力优化。通过算法原理分析和算例计算,表明该优化算法有一定的应用价值。     

混合遗传算法在离散变量结构优化中的应用

针对遗传算法在离散变量结构优化设计中的缺陷,将进退搜索算法同遗传算法相结合,提出了一种混合遗传算法。建立了离散变量结构优化模型,并对一11杆桁架结构进行了优化设计。算例结果表明,混合遗传算法收敛快、精度高,应用于离散变量结构优化设计是有效的。     

有支撑钢框架离散型拓扑优化设计

为了研究离散型拓扑优化理论在实际工程中的应用,在遗传算法和渐进结构优化算法的基础上对有支撑钢框架的离散型拓扑优化设计进行了分析.通过引入拓扑变量并修改无效杆件的弹性模量,提出了一个能适用于桁架结构、刚架结构和桁架刚架混合结构的离散型拓扑优化问题统一数学列式.该统一数学列式能解决桁架拓扑优化问题中以截面面积作为设计变量而...     

基于改进双向渐进结构优化法的桁架结构拓扑优化

为了将双向渐进结构优化法应用于桁架结构优化设计中,结合能量原理和满应力设计准则,推导了以结构最小应变能为目标函数的优化计算公式,提出了可以应用于桁架结构优化的桁架-双向渐进结构优化(T-BESO)法.T-BESO法以杆件截面面积为设计变量,以结构应变能为目标函数,以应力约束和满应力设计准则为约束条件.在T-BESO法中...     

基于离散变量的桁架结构截面优化及软件实现

以杆件横截面为设计变量,以结构重量为目标函数,采用基于离散变量的序列两级优化方法对桁架结构进行优化设计;在优化设计过程中采用了0阶修正,并且利用PCL语言在有限元软件MSC．NASTRAN和MSC．PATRAN上开发出了解决含应力约束和位移约束的桁架结构离散截面优化问题的程序。     

基于MATLAB的桁架结构优化设计

介绍了基于BP神经网络的全局性结构近似分析方法 ,解决了结构优化设计问题中变量的非线性映射问题。在此基础上 ,利用改进的遗传算法 ,对桁架结构在满足应力约束条件下进行结构最轻优化设计。利用Matlab的神经网络工具箱 ,编程求解了三杆桁架优化问题。     

基于改进型蚁群算法的结构优化设计

结构优化设计中通常含有离散设计变量,本文探讨了如何将改进型蚁群算法应用于含离散设计变量的结构优化问题。以十杆桁架结构离散设计变量优化问题为例进行仿真,仿真结果表明蚁群算法能比较有效地解决含离散设计变量的优化问题。     

Optimum design of steel truss arch bridges using a hybrid genetic algorithm

The design of steel truss arch bridges is formulated as an optimization problem. The objective function considered is the weight of the steel truss arch bridge. The objective function is minimized subjected to the design constraints of strength (stress) and serviceability (deflection). An efficient, accurate, and robust algorithm is proposed for optimal design of steel truss arch bridges. The proposed algorithm integrates the concepts of the genetic algorithm (GA) and the finite element method. A real-coded/integer-coded method is used to realistically represent the values of the design variables. Three GA operators consisting of constraint aggregate selection procedure, arithmetic crossover, and non-uniform mutation are proposed. Finite element method is used to compute values of implicit objective functions. A numerical example involving a detailed computational model of a long span steel truss arch bridge with a main span of 552 m is presented to demonstrate the applicability and merits of the proposed method.     

改进遗传算法在桁架优化设计中的应用

利用遗传算法对桁架进行优化时,将ANSYS作为有限元结构分析工具,把遗传算法优化程序与结构的有限元分析程序结合起来,在这两个程序中进行数据传递,能节约结构优化问题的求解时间,提高计算结果的可靠性.文中针对简单遗传算法具有"收敛早熟"、局部搜索能力差等缺点,提出了改进的遗传算法.算例表明此方法正确、可行,能充分发挥ANS...     

混合遗传算法在工程结构优化设计中的应用

根据工程实际 ,充分考虑规范规定的约束条件和各项技术标准要求 ,建立了建筑结构优化模型。并提出一种离散变量结构优化设计的斐波那契算法与标准遗传算法结合成混合遗传算法。优化设计结果表明 ,这种混合遗传算法既发挥了斐波那契算法省时、高效、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 ,是兼二者之长 ,弃二者之短的高效的理想的工程结构优化设计方法。     

混合遗传算法在桁架优化中的应用

应用遗传算法,并对基本遗传算法进行相应的改进,对空间桁架结构截面进行优化。在应用遗传算法的同时,考虑满应力解通常处在最优解附近的原理,将满应力解作为种群中的一个个体参与计算,并通过MATLAB编制相应的程序实现。算例表明,该方法能得到理想效果,并减少程序运算时间。     

Two-stage Optimization Based on Force Method for Damage Identification of Planar and Space Trusses

In this study, an efficient two-stage optimization procedure based on the force method is proposed to properly identify the sites and extents of multiple damages in the planar and space truss structures. In the first stage, the elements which have the higher probability of damages are selected by using the anti-optimization (AO) method and the weighed sum procedure. In the second stage, the genetic algorithm (GA) is performed to determine the actual damage sites and their extents based on the force method. The accuracy and effectiveness of the proposed method are proved through the planar and space truss structures. The results from the present study indicate that the combination of the first stage with the second one can provide a reliable tool to accurately and efficiently identify the multiple damages of truss structures.     

Multi-objective optimal design of braced frames using hybrid genetic and ant colony optimization

In this article, multi-objective optimization of braced frames is investigated using a novel hybrid algorithm. Initially, the applied evolutionary algorithms, ant colony optimization (ACO) and genetic algorithm (GA) are reviewed, followed by developing the hybrid method. A dynamic hybridization of GA and ACO is proposed as a novel hybrid method which does not appear in the literature for optimal design of steel braced frames. Not only the cross section of the beams, columns and braces are considered to be the design variables, but also the topologies of the braces are taken into account as additional design variables. The hybrid algorithm explores the whole design space for optimum solutions. Weight and maximum displacement of the structure are employed as the objective functions for multi-objective optimal design. Subsequently, using the weighted sum method (WSM), the two objective problem are converted to a single objective optimization problem and the proposed hybrid genetic ant colony algorithm (HGAC) is developed for optimal design. Assuming different combination for weight coefficients, a trade-off between the two objectives are obtained in the numerical example section. To make the final decision easier for designers, related constraint is applied to obtain practical topologies. The achieved results show the capability of HGAC to find optimal topologies and sections for the elements.     

Hybrid Genetic Algorithms for Optimization of Truss Structures

The genetic algorithm (GA) is a new optimization paradigm that models a natural evolution mechanism. The framework of the GA naturally corresponds to a discrete optimization problem. Although the GA is very robust, it is also very computationally intensive and hence slower than other methods. To speed up the convergence, this article proposes a hybrid GA that combines the concept of survival of the fittest with the concept of adaptation. The fully stressed design optimality criterion is employed to play the role of adaptation. Numerical examples show that even though the displacement constraints are active, (1) both average weight and minimum weight obtained by a hybrid GA are less than those obtained by a pure GA, (2) a hybrid GA is more stable than a pure GA, and (3) the speed of convergence of a hybrid GA is superior to that of a pure GA.     

基于GASA混合优化策略的双层规划模型求解算法研究

本文提出用遗传—模拟退火算法(GASA)混合优化策略来求解双层规划模型。混合优化策略结合了遗传算法的并行结构和模拟退火算法的概率突跳性,提高了找到全局最优解的可靠性和计算效率。数值模拟实验表明算法性能良好,GASA混合优化策略求得全局最优解时的进化代数比单一的遗传算法减少约35％,比模拟退火算法的迭代次数减少约50％。     

桁架尺寸、几何形状和拓扑优化的力法和遗传算法

结合能量和力法进行桁架重量的最小化,为此,采用遗传算法作为优化工具。提出的主要思想是减少输入变量。在采用遗传算法进行结构优化时,力法的引入非常有意义。该方法不需要求逆矩阵,只需在遗传算法中加入有限的变量,变量的个数等于结构超静定次数。通过几种不同类型的优化案例,验证了当前方法的有效性,并对不同优化技术的结果进行了比较。