Application of the sequential parametric convex approximation method to the design of robust trusses

We study an algorithm recently proposed, which is called sequential parametric approximation method, that finds the solution of a differentiable nonconvex optimization problem by solving a sequence of differentiable convex approximations from the original one. We show as well the global convergence of this method under weaker assumptions than those made in the literature. The optimization method is applied to the design of robust truss structures. The optimal structure of the model considered minimizes the total amount of material under mechanical equilibrium, displacements and stress constraints. Finally, Robust designs are found by considering load perturbations.     

Formal solution of an interval system of linear equations with an application in static responses of structures with interval forces

We are concerned with the so called formal solution of an interval system of linear equations. We focus on the case where the coefficient matrix is deterministic (real) and the right-hand side is an interval vector. We show that the set of formal solutions represents a convex polyhedral set. We propose new properties of the formal solution related to its existence, uniqueness and robustness. As particular classes of problems we investigate also the situation where the coefficient matrix is an M-matrix or H-matrix. Example problems related to the structures, such as 6-bar truss and a rectangular sheet, are solved to illustrate the computational aspects of the methods.     

多重约束下空间桁架结构抗风优化

目前复杂结构的抗风优化研究大多集中于高层建筑,很少针对风敏感的大跨屋盖结构．考虑强度、刚度和几何尺寸等多重约束,基于虚功原理和Lagrange乘子将抗风优化转化为无约束问题,编制数值程序整合有限元计算和优化分析两部分,然后对杆件数为10080的实际双层柱面网壳进行优化设计,讨论了设计变量可行域、初始值和调整步选择等对优化结果的影响．研究表明,采用本文方法可实现对空间桁架结构进行多重约束下的高效抗风优化设计,网壳总重降低约37%,风致响应分布不均使得有必要设定可行域下限,而设计变量初值和调整步选择不影响最后的优化结果．     

A practical fractional numerical optimization method for designing economically and environmentally friendly super-tall buildings

Due to the complexity of super tall buildings, many well-known optimization algorithms are not well applicable. Using structural lateral system of super tall buildings as engineering background, the paper developed a practical fractional numerical optimization method (FNOM), which applies fractional strategy and quasi-constant assumption, to reduce material cost and embodied carbon cost by searching the optimal structural dimensions. Firstly, two kinds of relationships among optimization variables (structural dimensions), driven design constraints (the interstory drift and the natural period) and optimization objective (cost including material cost and embodied carbon cost) are mathematically modelled. Genetic algorithm (GA) is then introduced to search the optimal structural dimensions based on the quasi-constant assumption of virtual work and internal work of the inactive components. Thirdly, fractional strategy is applied to create assemblies composed of different structural component sets, and the assemblies are then to be optimized in proper sequences. Fourthly, FNOM is implemented as a user-friendly software called C-FNO to practically support the preliminary design of super-tall buildings. Finally, a 700 m high super-tall building is employed to illustrate FNOM by using C-FNO, and the results show that only three design constraints of the interstory drift, the natural period and the stress ratio need to be solved during each optimization step. Belt truss, mega column, outrigger truss and shear wall of the super tall building should be optimized in sequence to save more cost. A great amount of cost can be still saved for the super tall building with the normal traditional design.     

群对称桁架振动设计的半正定模型与降维问题(英)

桁架振动优化设计可描述为：在给定振动系统最低频率的约束条件下,设计用材最省的桁架结构. 本文针对具有某种结构对称性的桁架,利用有限群描述这一特性,在已有桁架设计的半正定规划模型基 础上,运用最近提出的矩阵代数方法对半正定规划问题的决策变量和数据进行降维,给出了构造有限群 表示的两个充分条件,并实现了一类群对称桁架振动优化设计的半正定模型降维.基于问题的实际背景, 我们又考虑了一个具有八根弹性棒的桁架设计实例,进一步说明在实际问题中根据群对称构造群表示以 及对应不可约表示的具体方法.     

Topology Optimization of Trusses—Random Cost Method Versus Evolutionary Algorithms

The recently proposed random cost method is applied to the topology optimization of trusses. Its performance is compared to previous genetic algorithm and evolution strategy simulations. Random cost turns out to be an optimization method with attractive features. In comparison to the genetic algorithm approach of Hajela, Lee and Lin, random cost turns out to be simpler and more efficient. Furthermore it is found that in contrast to evolution strategy, the random cost strategy's ability to find optima, is independent of the initial structure. This characteristic is related to the important capacity of escaping from local optima.     

Robust truss topology optimization via semidefinite programming with complementarity constraints: a difference-of-convex programming approach

The robust truss topology optimization against the uncertain static external load can be formulated as mixed-integer semidefinite programming. Although a global optimal solution can be computed with a branch-and-bound method, it is very time-consuming. This paper presents an alternative formulation, semidefinite programming with complementarity constraints, and proposes an efficient heuristic. The proposed method is based upon the concave–convex procedure for difference-of-convex programming. It is shown that the method can often find a practically reasonable truss design within the computational cost of solving some dozen of convex optimization subproblems.     

Local buckling design problem for topology optimized truss girders

The paper presents practical applying of structural topology optimization algorithm based on the formulation of optimal design with compliance minimization. Problem was presented and discussed for several 2D examples. Analysis of local buckling problem was made for various results obtained by the algorithm and approximated into the truss structures. Real bridge load was considered for girders. Topology based optimal girders were compared to typical truss structure. Relation between rods thicknesses was considered. Stress level including buckling in compressed members was examined. (© 2009 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Optimization Design of Holding Poles Based on the Response Surface Methodology and the Improved Arithmetic Optimization Algorithm北大核心CSCD

抱杆优化设计需要耗费大量有限元分析计算时间,难以确定可行域.该文采用响应面法(response surface method,RSM)来模拟抱杆结构的真实响应,提出了改进的算术优化算法(improved arithmetic optimization algorithm,IAOA)对抱杆结构进行优化设计.将分数阶积分引入算术优化算法(arithmetic optimization algorithm,AOA),改善了算法的开发能力.采用拉丁超立方抽样,选取抱杆结构杆件截面试验样本,利用最小二乘法对样本点进行分析,构建了抱杆结构应力和位移关于杆件截面尺寸的二阶响应面代理模型.建立以抱杆质量最小化为优化目标,许用应力和位移为约束条件的优化模型,采用IAOA对其进行求解.结果表明:二阶响应面模型能够准确预测抱杆结构的响应值,IAOA的求解精度得到显著提升,代理模型可大幅降低有限元分析所需的计算代价,优化后抱杆结构质量减轻了8.2%.联合使用RSM和IAOA可有效求解大型空间杆系结构的优化设计问题.     

基于绳索作动器的大型太空望远镜桁架结构的振动主动控制

薄膜衍射是一种新型的太空望远镜的成像方式,它具有轻质、易折叠与展开、光学成像精度高等许多优点,是当今太空望远镜技术的研究热点.该文针对一类薄膜衍射太空望远镜桁架结构的振动主动控制进行了研究,提出了一种基于绳索作动器的振动主动控制策略.首先建立了望远镜桁架结构的动力学模型,然后采用粒子群优化算法研究了绳索作动器的优化布置...     

Truss topology optimization including unilateral contact

This work extends the ground structure approach of truss topology optimization to include unilateral contact conditions. The traditional design objective of finding the stiffest truss among those of equal volume is combined with a second objective of achieving a uniform contact force distribution. Design variables are the volume of bars and the gaps between potential contact nodes and rigid obstacles. The problem can be viewed as that of finding a saddle point of the equilibrium potential energy function (a convex problem) or as that of minimizing the external work among all trusses that exhibit a uniform contact force distribution (a nonconvex problem). These two formulations are related, although not completely equivalent: they give the same design, but concerning the associated displacement states, the solutions of the first formulation are included among those of the second but the opposite does not necessarily hold.In the classical noncontact single-load case problem, it is known that an optimal truss can be found by solving a linear programming (LP) limit design problem, where compatibility conditions are not taken into account. This result is extended to include unilateral contact and the second objective of obtaining a uniform contact force distribution. The LP formulation is our vehicle for proving existence of an optimal design: by standard LP theory, we need only to show primal and dual feasibility; the primal one is obvious, and the dual one is shown by the Farkas lemma to be equivalent to a condition on the direction of the external load. This method of proof extends results in the classical noncontact case to structures that have a singular stiffness matrix for all designs, including a case with no prescribed nodal displacements.Numerical solutions are also obtained by using the LP formulation. It is applied to two bridge-type structures, and trusses that are optimal in the above sense are obtained.This work was supported by The Center for Industrial Information Technology (CENIIT) and the Swedish Research Council for Engineering Sciences (TFR).     

A new multi-objective discrete robust optimization algorithm for engineering design

This paper proposes a novel multi-objective discrete robust optimization (MODRO) algorithm for design of engineering structures involving uncertainties. In the present MODRO procedure, grey relational analysis (GRA), coupled with principal component analysis (PCA), was used as a multicriteria decision making model for converting multiple conflicting objectives into one unified cost function. The optimization process was iterated using the successive Taguchi approach to avoid the limitation that the conventional Taguchi method fails to deal with a large number of design variables and design levels. The proposed method was first verified by a mathematical benchmark example and a ten-bar truss design problem; and then it was applied to a more sophisticated design case of full scale vehicle structure for crashworthiness criteria. The results showed that the algorithm is able to achieve an optimal design in a fairly efficient manner attributable to its integration with the multicriteria decision making model. Note that the optimal design can be directly used in practical applications without further design selection. In addition, it was found that the optimum is close to the corresponding Pareto frontier generated from the other approaches, such as the non-dominated sorting genetic algorithm II (NSGA-II), but can be more robust as a result of introduction of the Taguchi method. Due to its independence on metamodeling techniques, the proposed algorithm could be fairly promising for engineering design problems of high dimensionality.     

Incorporating inventory and routing costs in strategic location models

We consider a supply chain design problem where the decision maker needs to decide the number and locations of the distribution centers (DCs). Customers face random demand, and each DC maintains a certain amount of safety stock in order to achieve a certain service level for the customers it serves. The objective is to minimize the total cost that includes location costs and inventory costs at the DCs, and distribution costs in the supply chain. We show that this problem can be formulated as a nonlinear integer programming model, for which we propose a Lagrangian relaxation based solution algorithm. By exploring the structure of the problem, we find a low-order polynomial algorithm for the nonlinear integer programming problem that must be solved in solving the Lagrangian relaxation sub-problems. We present computational results for several instances of the problem with sizes ranging from 40 to 320 customers. Our results show the benefits of having an integrated supply chain design framework that includes location, inventory, and routing decisions in the same optimization model.     

Optimization of transit route network,vehicle headways and timetables for large-scale transit networks

This paper presents a metaheuristic method for optimizing transit networks, including route network design, vehicle headway, and timetable assignment. Given information on transit demand, the street network of the transit service area, and total fleet size, the goal is to identify a transit network that minimizes a passenger cost function. Transit network optimization is a complex combinatorial problem due to huge search spaces of route network, vehicle headways, and timetables. The methodology described in this paper includes a representation of transit network variable search spaces (route network, headway, and timetable); a user cost function based on passenger random arrival times, route network, vehicle headways, and timetables; and a metaheuristic search scheme that combines simulated annealing, tabu, and greedy search methods. This methodology has been tested with problems reported in the existing literature, and applied to a large-scale realistic network optimization problem. The results show that the methodology is capable of producing improved solutions to large-scale transit network design problems in reasonable amounts of time and computing resources.     

考虑路径风险的需求不确定应急物流定位-路径问题

针对自然灾害对路网造成的破坏性和受灾点物资需求量的不确定性,综合考虑应急物流路径风险和物资分配的时间公平性,将路径风险定量化为路径通行能力风险、路径损毁风险和路径复杂性,基于鲁棒优化理论,以最小化车辆最长配送时间和应急物流系统总成本为目标,建立了双目标多物资定位-路径优化模型,采用遗传算法对其求解,以汶川地震应急救援相关数据为例进行了数据仿真实验。实验结果的对比分析表明:在考虑路径风险下,随着控制系数的增加,系统总成本和最长配送时间目标值均呈现增大的趋势;受灾点的物资需求扰动越大,构建应急物流系统的总成本越高。算例结果也验证了鲁棒优化方法在处理不确定需求方面的有效性,为解决自然灾害后应急设施点的开设和救援物资的安全及时准确配送提供了有效的方法。     

Fuzzy mixture two warehouse inventory model involving fuzzy random variable lead time demand and fuzzy total demand

This paper considers a two-warehouse fuzzy-stochastic mixture inventory model involving variable lead time with backorders fully backlogged. The model is considered for two cases—without and with budget constraint. Here, lead-time demand is considered as a fuzzy random variable and the total cost is obtained in the fuzzy sense. The total demand is again represented by a triangular fuzzy number and the fuzzy total cost is derived. By using the centroid method of defuzzification, the total cost is estimated. For the case with fuzzy-stochastic budget constraint, surprise function is used to convert the constrained problem to a corresponding unconstrained problem in pessimistic sense. The crisp optimization problem is solved using Generalized Reduced Gradient method. The optimal solutions for order quantity and lead time are found in both cases for the models with fuzzy-stochastic/stochastic lead time and the corresponding minimum value of the total cost in all cases are obtained. Numerical examples are provided to illustrate the models and results in both cases are compared.     

GLOBAL CONVERGENCE AND IMPLEMENTATION OF NGTN METHOD FOR SOWING LARGE-SCALE SMRSE NONLINEAR PROGRAMMING PROBLEMS

1. IntroductionConsider the following NLP problemwhere the function f: Re --+ RI and gi: Re - R', j E J are twice continuously dtherentiable.In particular, we discuss the cajse, where the nUmber of variables and the nUmber of constraintsin (1.1) are large and second derivatives in (1.1) are sparse.There are some methods whiCh can solve largesscale problems, e.g. Lancelot in [2] andTDSQPLM in [9]. But they can not take adVantage of sparse structtire of the problem. A newefficient meth…     

Reliability sensitivity analysis method based on subset simulation hybrid techniques

This study proposes a new reliability sensitivity analysis approach using an efficient hybrid simulation method that is a combination of subset simulation, importance sampling and control variates techniques. This method contains a probability term (a fast-moving by subset simulation) and an adaptive weighting part that improves the calculated probability. The Finite Difference Method is used to obtain reliability sensitivities, and the related formulation is derived. Five numerical examples (four-branch model, beam-cable system, one-story frame, ring-stiffened cylinder buckling, and 25-bar steel truss) are presented to describe the applications of the proposed method. The results are compared with those obtained by the available techniques. The results revealed that the proposed method efficiently and accurately solves rare-event, system-level, and real-world engineering problems with explicit and implicit limit state functions.     

基于混合算法的实时订货信息下的车辆调度优化

实时订货信息下的车辆调度是随机性车辆调度中货物需求量、需求点均不确定的情况下的车辆调度.针对该问题,本文构建了配送总成本最小的目标函数,提出了采用混合算法求解的思路.即以局部搜索法求得初始解,采用遗传算法优化初始解,并在送货时间更新后,利用禁忌搜索法求解速度快的特点改进调度方案,得到订货信息不断更新的条件下的车辆调度方案.通过实例分析,本方法既可解决电子商务条件下实时订货的车辆调度问题,也具有求解结果可靠、求解过程快速的特点.     

Optimal Sequential Sampling Policy of Partitioned Random Search and Its Approximation

The paper studies the optimal sequential sampling policy of the partitioned random search (PRS) and its approximation. The PRS is a recently proposed approach for function optimization. It takes explicitly into consideration computation time or cost, assuming that there exist both a cost for each function evaluation and a finite total computation time constraint. It is also motivated at improving efficiency of the widely used crude random search. In particular, the PRS considers partitioning the search region of an objective function into K subregions and employing an independent and identically distributed random sampling scheme for each of K subregions. A sampling policy decides when to terminate the sampling process or which subregion to be sampled next.