桁架结构优化设计的遗传算法

本文提出了桁架结构系统优化设计的新方法遗传算法，它不同于常规优化算法的特点在于，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。     

用于结构优化设计的改进多目标群搜索算法

 在多目标群搜索算法（multi-objective group search optimization, MGSO）基本原理的基础上,结合Pareto最优解理论,提出了基于约束改进的多目标群搜索算法（IMGSO）,并应用于多目标的结构优化设计．算法的改进主要有3个方面：第一,引入过渡可行域的概念来处理约束条件;第二,利用庄家法来构造非支配解集;最后,结合禁忌搜索算法和拥挤距离机制来选择发现者,以避免解集过早陷入局部最优,并提高收敛精度．采用IMGSO优化算法分别对平面和空间桁架结构进行了离散变量的截面优化设计,并与MGSO优化算法的计算结果进行了比较,结果表明改进的多目标群搜索优化算法IMGSO与MGSO算法相比具有更好的收敛精度.通过算例表明：IMGSO算法得到的解集中的解能大部分支配MGSO算法的解,在复杂高维结构中IMGSO算法的优越性更加明显,且收敛速度也有一定的提高,可有效应用于多目标的实际结构优化设计．     

基于复合形算子的基础支护桩优化设计智能算法研究

本文通过遗传算法和传统复合形搜索法相结合,基于对遗传算法算子计算结构的调整,并将遗传算法与神经网络相结合,提出并研究了一种新的优化设计方法,协同求解复杂工程中的优化问题。并针对悬臂式支护桩的优化设计的数学模型,采用该算法进行了优化设计分析;计算结果表明,该算法可克服遗传算法最终进化至最优解较慢和人工神经网络易陷入局部解的缺陷,具有较好的全局性和收敛速度。     

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     

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，它模拟达尔遗传选择与自然进化的理论，根据“适生存”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。     

离散变量桁架结构拓扑优化设计的混合算法

将相对差商法和混沌优化结合起来,形成求解离散变量桁架结构拓扑优化设计的混合算法。利用相对差商法可以对离散变量快速寻优的特点,及混沌变量的全局遍历性,可以有效地跳出局部最优解,达到拓扑优化全局寻优的目的。通过采用和准最优解的对比及几何稳定性的判断等辅助性技术,降低了重分析次数。同时,高效的重分析方法的结合,提高了求解的效率,也避免了拓扑优化问题中求解的一些困难。算例表明,该算法对于离散变量的拓扑优化设计问题是快速有效的。     

GEP在桁架结构优化中的应用

介绍基因表达式编程(GEP)算法的基本原理和在参数优化中的实现过程,并将该算法应用于桁架结构的优化设计。针对标准GEP容易陷入局部最优解,且收敛速度慢的缺陷,对算法引入回溯机制,用停滞前一代的精英个体替换当前种群中所有适应度最差的个体,使较优个体有更多机会向不同方向进化,扩大最优解的搜索空间。25杆空间桁架的截面优化设计结果证明:算法改进后,搜索效率得到明显提高,并通过72杆空间桁架算例,证明了该方法在结构优化中的可行性和有效性。     

动力吸振器的多目标优化和多属性决策研究

在结构振动控制中,为了最大限度发挥吸振器的耗能减振作用.需要寻找吸振器的最优参数,即最优频率比、最优阻尼比和最优质量比,使得结构在不同的频率激励下获得最好的减振效果.本文将基于进化算法的多目标优化技术与多属性决策方法联合运用,针对主系统存在阻尼的减振系统,研究了动力吸振器的优化和决策同题.对于多目标优化问题,采用改进的非支配解排序的多目标进化算法(NSGA Ⅱ),求出Pareto最优解,由这些Pareto最优解构成决策矩阵,使用客观赋权的信息熵方法对最优解的属性进行权值计算.然后用逼近理想解的排序方法(TOPSIS)进行多属性决策(MADM)研究,对Pareto最优解给出排序.文中给出了4个设计参数、3个目标函数的动力吸振器优化设计算例.     

一种高层混凝土建筑结构的优化设计方法

本文提出了一种有效实用的，对多层及高层混凝土建筑结构进行自动优化设计的算法，这种算法以有限元分析为基础，有机地结合了目前较流行的优化方法－一种严格推导的优化准则进工程师强有力的设计工具，它将结构构件的截面尺寸作为设计变量，在构件单元的强度，多层间的偏转角和位移，以及构件单元尺寸的限制等约束条件下，求解以结构重量为目标函数的最优解，一些算例表明此算法十分有效，可靠，可适用于大型高层混凝建筑结构的分析     

考虑自振周期的高层混凝土建筑结构动力优化设计

风载对高层建筑结构会产生一定的动力效应，控制这种动力效应的有效方法之一是限制结构的自振周期。本文提出了一种以有限元分析和严格推导的优化准则方法为基础的动力优化设计方法，将结构构件的截面尺寸作为设计变量，在构件单元的强度、构件单元尺寸和自振周期的限制等约束条件下，求解以最小结构重量为目标的最优解。算例表明，此算法十分有效、可靠，可适用于大型高层混凝土建筑结构的优化设计。     

Improved genetic algorithm for design optimization of truss structures with sizing,shape and topology variables

This paper presents an improved genetic algorithm (GA) to minimize weight of truss with sizing, shape and topology variables. Because of the nature of discrete and continuous variables, mixed coding schemes are proposed, including binary and float coding, integer and float coding. Surrogate function is applied to unify the constraints into single one; moreover surrogate reproduction is developed to select good individuals to mating pool on the basis of constraint and fitness values, which completely considers the character of constrained optimization. This paper proposes a new strategy of creating next population by competing between parent and offspring population based on constraint and fitness values; so that lifetime of excellent gene is prolonged. Because the initial population is created randomly and three operators of GA are also indeterminable, it is necessary to check whether the structural topology is desirable. An improved restart operator is proposed to introduce new gene and explore new space, so that the reliability of GA is enhanced. Selected examples are solved; the improved numerical results demonstrate that the enhanced GA scheme is feasible and effective. Copyright © 2005 John Wiley & Sons, Ltd.     

An engineering method for complex structural optimization involving both size and topology design variables

This work presents an engineering method for optimizing structures made of bars, beams, plates, or a combination of those components. Corresponding problems involve both continuous (size) and discrete (topology) variables. Using a branched multipoint approximate function, which involves such mixed variables, a series of sequential approximate problems are constructed to make the primal problem explicit. To solve the approximate problems, genetic algorithm (GA) is utilized to optimize discrete variables, and when calculating individual fitness values in GA, a second-level approximate problem only involving retained continuous variables is built to optimize continuous variables. The solution to the second-level approximate problem can be easily obtained with dual methods. Structural analyses are only needed before improving the branched approximate functions in the iteration cycles. The method aims at optimal design of discrete structures consisting of bars, beams, plates, or other components. Numerical examples are given to illustrate its effectiveness, including frame topology optimization, layout optimization of stiffeners modeled with beams or shells, concurrent layout optimization of beam and shell components, and an application in a microsatellite structure. Optimization results show that the number of structural analyses is dramatically decreased when compared with pure GA while even comparable to pure sizing optimization.     

离散变量结构优化设计的拟满应力遗传算法

以力学准则法为基础,提出了一种求解离散变量结构优化设计的拟满应力方法;这种方法能直接求解具有应力约束和几何约束的离散变量结构优化设计问题。通过在遗传算法中定义拟满应力算子,建立了一种离散变量结构优化设计的混合遗传算法拟满应力遗传算法。算例表明:这种混合遗传算法对于离散变量结构优化设计问题具有较高的计算效率。     

Optimization of Construction of Tire Reinforcement by Genetic Algorithm

A new tire design procedure capable of determining the optimum tire construction was developed by combining a finite element method approach with mathematical programming and a genetic algorithm (GA). Both procedures successfully generated optimized belt structures. The design variables in the mathematical programming were belt angle and belt width. Using the merits of a GA which enabled the use of discrete variables, the design variables in the GA were not only the topology of the belt and belt angle but also the belt material. Furthermore, a discrete objective function such as the number of parts could be optimized in the GA. The optimized structure obtained by the GA was verified to increase the cornering stiffness more than 15 percent as compared with the control structure in an indoor drum test.     

A genetic algorithm approach to single and multiobjective structural optimization with discrete–continuous variables

A search procedure with a philosophical basis in molecular biology is adapted for solving single and multiobjective structural optimization problems. This procedure, known as a genetic algorithm (GA). utilizes a blending of the principles of natural genetics and natural selection. A lack of dependence on the gradient information makes GAs less susceptible to pitfalls of convergence to a local optimum. To model the multiple objective functions in the problem formulation, a co-operative game theoretic approach is proposed. Examples dealing with single and multiobjective geometrical design of structures with discrete–continuous design variables, and using artificial genetic search are presented. Simulation results indicate that GAs converge to optimum solutions by searching only a small fraction of the solution space. The optimum solutions obtained using GAs compare favourably with optimum solutions obtained using gradient-based search techniques. The results indicate that the efficiency and power of GAs can be effectively utilized to solve a broad spectrum of design optimization problems with discrete and continuous variables with similar efficiency.     

离散变量结构优化设计的最优综合效能法

针对结构优化问题的位移约束,引入关键约束的界约参数,提出了结构位移统一约束的缩减形式,从而简化了结构优化模型。根据离散变量结构优化问题的特点,提出了效能系数的概念,它衡量设计变量在离散邻域范围内变化对目标函数与约束函数值的影响,并研究了基于效能系数取值分类的四种主要调整方式。根据结构应力和位移约束的影响区域属性,以综合效能最大化为引导,提出了求解离散变量结构优化问题的最优综合效能法。算例结果显示该算法具有良好的优化效率,可求得问题的最优解或获得历史上的最优记录。     

Simultaneous distribution and sizing optimization for stiffeners with an improved genetic algorithm with two-level approximation

This article presents an improved genetic algorithm with two-level approximation (GATA) to optimize the distribution and size of stiffeners simultaneously. A novel optimization model of stiffeners, including two kinds of design variables, is established. The first level approximation problem transforms the original implicit problem to an explicit problem which involves the topology and size variables. Then, a genetic algorithm (GA) addresses the mixed variables. The individuals in the GA are coded by topology variables, and when calculating an individual’s fitness, the second level approximation problem is embedded to optimize the size variables. Considering the stiffeners’ optimization, several aspects of the initial GATA are updated, including the relationship between two kinds of variables, the weight and its sensitivity calculation and the GA strategy, to optimize the stiffeners’ size and distribution simultaneously. Numerical examples show that the improved GATA is effective in optimizing the stiffened shells’ topology and size variables simultaneously.     

Multiscale multiresolution genetic algorithm with a golden sectioned population composition

A new genetic algorithm (GA) strategy called the multiscale multiresolution GA is proposed for expediting solution convergence by orders of magnitude. The motivation for this development was to apply GAs to a certain class of large optimization problems, which are otherwise nearly impossible to solve. For the algorithm, standard binary design variables are binary wavelet transformed to multiscale design variables. By working with the multiscale variables, evolution can proceed in multiresolution; converged solutions at a low resolution are reused as a part of individuals of the initial population for the next resolution evolution. It is shown that the best solution convergence can be achieved if three initial population groups having different fitness levels are mixed at the golden section ratio. An analogy between cell division and the proposed multiscale multiresolution strategy is made. The specific applications of the developed method are made in topology optimization problems. Copyright © 2007 John Wiley & Sons, Ltd.     

基于遗传算法和拓扑优化的结构多孔洞损伤识别

鉴于拓扑优化和遗传算法在结构损伤识别中各自的优点,本文将遗传算法、有限元和拓扑优化三种方法相结合,提出了一种用于二维结构多损伤识别的新方法。这种方法将拓扑优化的设计变量和遗传算法的参数统一化,将拓扑优化中的目标函数和约束方程与遗传算法的适应度函数联系起来,并以拓扑优化的约束方程作为控制条件参与整个遗传运算的控制。采用二进制编码遗传算法代替连续变量拓扑优化的方式对发生孔洞损伤形式的二维结构进行损伤识别,避免了利用连续变量拓扑优化进行损伤识别时参数阈值的确定可能给识别结果带来的不良影响。通过对两个二维结构模型的多损伤识别仿真计算,结果显示本方法能够很好地识别二维结构中多个位置的损伤,对于仅用拓扑优化法很难识别的轻微孔洞损伤情况,该方法也能得出与实际情况吻合良好的结果。