空间桁架结构拓扑优化设计的线性规划方法

本文以杆件内力为设计变量,构造了多工况作用下空间桁架结构拓扑优化的线性规划模型,考虑了应力和位移约束,能够避免奇异最优拓扑和不稳定结构的产生。     

钢桁架结构几何优化的新方法

本文通过适当变量代换,将钢桁结构几何优化模型表达为序列二次规划问题,并以最大熵的迭代格式表示之,最后以实例验证了该方法的可靠性。     

基于智能优化算法的机场航站楼桁架结构设计

进行更加安全稳定和轻质量的桁架结构设计,对机场航站楼的安全稳定高效运营具有十分重要的意义。该文从宏观万有引力出发,对桁架结构中粒子级别的应力关系进行了分析,建立了启发式的智能优化算法。针对桁架结构设计和优化过程进行数学建模,以质量更轻为优化目标,配置应力条件、位移条件、横截面条件以及频率条件等组合约束。最后以10杆桁架结构为研究对象进行试验研究,试验结果显示：该文提出的优化设计方法可以获得更轻的桁架结构和更快的收敛速度。     

混合整数双层线性规划的全局优化算法

本文讨论了上层决策变量为整数变量、下层决策变量为连续变量的混合整数双层线性规划问题,利用其可行解均落在约束域边界上的性质,提出了一种求解混合整数双层线性规划全局最优解的算法,并举例说明了算法的执行过程。     

基于改进蚁群的测试序列优化算法

针对故障诊断中的测试序列优化问题,提出一种改进蚁群算法的解决方法.该方法根据二值属性系统的特点,定义状态集向量及测试向量,将故障测试隔离过程转化为向量的位运算过程,将序列优化问题转化为一种最小代价的动态树构造问题,设计灵活的状态转移规则,并根据动态树的分层结构特点,提出一种分层加权和遗传变异相结合的信息素更新策略,解决这种动态树结构的寻优问题.仿真结果表明:该算法以较高的效率收敛到已知最优解,高效实用,为大规模复杂系统的测试优化问题提供了一条新的解决途径,具有一定的工程应用价值.     

基于蚁群算法的多值属性系统测试序列优化

针对系统故障诊断中的多值属性系统测试序列优化问题,该文提出一种改进的蚁群算法,将成功运用在二值属性系统中的蚁群算法扩展到多值属性系统中,根据多值属性系统特点,设计相应的状态转移规则和信息素更新机制,并采用蚁群算法和遗传算法相融合的联合优化策略,解决了多值属性系统的序列优化问题,为多值属性系统的测试优化问题提供了一条新的解决途径。     

基于信息熵改进的粒子群算法在桁架结构

提出了信息熵改进的粒子群优化算法用于解决有应力约束、位移约束的桁架结构杆件截面尺寸优化设计问题.首先介绍了信息熵基本理论和基本粒子群优化算法理论,然后对粒子群优化算法作了合理的参数设置,并将信息熵引入粒子群优化算法的适应函数和停机判别准则中.最后对2个经典的优化问题进行求解并与其他算法进行了比较.数据结果表明信息熵改进后的粒子群优化算法在桁架结构优化设计中优于其他同类算法.     

基于演化算法的带频率约束的桁架结构形状和尺寸优化

具有频率约束的桁架结构形状和尺寸优化设计是一个难度大的非线性动力优化问题。形状和尺寸变量的耦合通常导致收敛困难,而频率约束则使得动力灵敏度分析困难,传统的优化准则法和数学规划法难于求解。将单纯形算法、子空间变维技术、均匀变异有机融入郭涛算法,提出一种混合演化算法,避开繁琐的动力灵敏度分析,简单、有效地求解这类桁架形状和尺寸优化问题。典型的桁架算例验证了算法的有效性和可靠性。     

基于遗传算法和梯度算法的桁架结构动力学形状优化

本文提出了一种基于遗传算法和梯度算法的桁架动力学形状优化方法 ,该方法综合了梯度算法的快速收敛与遗传算法的全局搜索能力。算例表明了本文方法的有效性和工程适应性。     

谈大跨拱桁架结构优化技术

大跨度空间的结构设计技术已是当今发展最快的建筑结构类型。大跨度的建筑物设计代表着一个国家建筑科技的水平。就建筑结构的造价而言,在建筑工程中占具着很大的比例,通过大跨度的结构设计优化技术的应用可以产生很可观的经济效益。在建筑结构的设计中相关部门与设计人员应该严格去遵守以：经济,适用和合理为原则进行设计。通过理代化的建筑科学技术手段,选择合理的设计方案来实现并降低其建筑工程的造价,取得更好的经济效益。本文将对大跨拱桁架结构的优化技术进行深入探讨。     

A move limit strategy for level set based structural optimization

A move limit strategy is proposed for level set based structural optimization, which offers a tool to restrict the allowable change of the free boundary of a structure in each iterative step of optimization. To realize the move limit strategy, a function for modifying the extended design velocity is proposed. Application of the move limit strategy is demonstrated by several numerical examples of 2D structures. The results show that the move limit strategy is helpful to improve the stability and convergence rate of level set based structural optimization.     

Optimization of large‐scale truss structures using sparse SAND formulations

Sparsity features of simultaneous analysis and design (SAND) formulations are studied and exploited for optimization of large‐scale truss structures. Three formulations are described and implemented with an existing analysis code. SAND formulations have large number of variables; however, gradients of the functions and Hessian of the Lagrangian are quite sparsely populated. Therefore, this structure of the problem is exploited and an optimization algorithm with sparse matrix capability is used to solve large‐scale problems. An existing analysis software is integrated with an optimizer based on a sparse sequential quadratic programming (SQP) algorithm to solve sample problems. The formulations and algorithms work quite well for the sample problems, and their performances are compared and discussed. For all the cases considered, the SAND formulations out perform the conventional formulation except one case. Further research is suggested to fully study and utilize sparse features of the alternative SAND formulations and to develop more efficient sparse solvers for large‐scale and more complex applications. Copyright © 2006 John Wiley & Sons, Ltd.     

Enhanced multiobjective particle swarm optimization in combination with adaptive weighted gradient-based searching

This article proposes a new multiobjective optimization method for structural problems based on multiobjective particle swarm optimization (MOPSO). A gradient-based optimization method is combined with MOPSO to alleviate constraint-handling difficulties. In this method, a group of particles is divided into two groups—a dominated solution group and a non-dominated solution group. The gradient-based method, utilizing a weighting coefficient method, is applied to the latter to conduct local searching that yields superior non-dominated solutions. In order to enhance the efficiency of exploration in a multiple objective function space, the weighting coefficients are adaptively assigned considering the distribution of non-dominated solutions. A linear optimization problem is solved to determine the optimal weighting coefficients for each non-dominated solution at each iteration. Finally, numerical and structural optimization problems are solved by the proposed method to verify the optimization efficiency.     

A new approach to solving stochastic programming problems with recourse

A new numerical approach to the solution of two-stage stochastic linear programming problems is described and evaluated. The approach avoids the solution of the first-stage problem and uses the underlying deterministic problem to generate a sequence of values of the first-stage variables which lead to successive improvements of the objective function towards the optimal policy. The model is evaluated using an example in which randomness is described by two correlated factors. The dynamics of these factors are described by stochastic processes simulated using lattice techniques. In this way, discrete distributions of the random parameters are assembled. The solutions obtained with the new iterative procedure are compared with solutions obtained with a deterministic equivalent linear programming problem. It is concluded that they are almost identical. However, the computational effort required for the new approach is negligible compared with that needed for the deterministic equivalent problem.     

High-Fidelity Computational Optimization for 3-D Flexible Wings: Part I—Simultaneous Aero-Structural Design Optimization (SASDO)

The formulation and implementation of a multidisciplinary optimization method called Simultaneous Aerodynamic and Structural Design Optimization (SASDO) is presented and applied to a simple, isolated, 3-D wing in inviscid flow. The method aims to reduce the computational expense incurred in performing shape and sizing optimization using existing state-of-the-art Computational Fluid Dynamics (CFD) flow analysis, FEM structural analysis and sensitivity analysis tools. Results show that the method finds the same local optimum as a conventional optimization method with as much as 50% reduction in the computational cost and without significant modifications to the analysis tools.     

Three algorithms for bicriteria integer linear programs

A problem in multiobjective programming is to determine all efficient solutions. As a first approach we present a basic algorithm where only one of the objective functions is minimized and the second objective function is taken as a restriction. In the next algorithm the maximum of both objective functions is minimized. In the third algorithm this minimax function is replaced by a continuous quadratic objective function.     

A SEQUENTIAL QUADRATIC PROGRAMMING ALGORITHM UTILIZING QR MATRIX FACTORIZATION

Some recent studies indicate that the sequential quadratic programming (SQP) approach has a sound theoretical basis and promising empirical results for solving general constrained optimization problems. This paper presents a variant of the SQP method which utilizes QR matrix factorization to solve the quadratic programming subproblem which result from taking a quadratic approximation of the original problem. Theoretically, the QR factorization method is more robust and computationally efficient in solving quadratic programs. To demonstrate the validity of this variant, a computer program named SQR is coded in Fortran to solve twenty-eight test problems. By comparing with three other algorithms: one multiplier method, one GRG-type method, and another SQP-type method, the numerical results show that, in general, SQR as devised in this paper is the best method as far as robustness and speed of convergence are concerned in solving general constrained optimization problems.