Concurrent optimization of stacking sequence and stiffener layout of a composite stiffened panel

This article is to optimally design laminated composite stiffened panels by optimizing both stacking sequences of the panel skin and stiffeners as well as the layout of stiffeners. Starting from initial designs of stiffener layout and stacking sequences for each stiffener and the panel skin, the problem is formulated with discrete and continuous variables, where discrete 0/1 variables represent the absence/presence of each layer in initial stacking sequences, and continuous variables represent layer thicknesses. A first-level approximate problem is established to make the problem explicit. Genetic algorithm is used to determine the existence of each layer in the laminates. When the number of retained layers in stiffener becomes zero, that stiffener can be seen as unnecessary and removed. For individual fitness calculation, a second-level approximate problem is constructed to optimize continuous ply thicknesses of retained layers. Correspondingly, laminated stacking sequences and stiffener layout are concurrently optimized.     

基于遗传算法的离散型结构拓扑优化设计

采用遗传算法求解包括桁架结构和框架结构的离散型结构拓扑优化问题。在遗传算法的基础上,通过引入拓扑变量并修改被删除杆件的材料弹性模量,提出了一个受多工况荷载作用,能同时考虑应力、稳定及位移等约束的离散型结构拓扑优化问题统一数学模型。该模型不但能同时适用于桁架结构和框架结构等离散型结构拓扑优化问题,而且还能解决奇异最优解问题。结合上述统一数学模型和遗传算法,给出了求解离散型结构拓扑优化问题的优化方法。算例结果表明,采用该文提出的拓扑优化方法可有效、方便地对桁架结构、框架结构等离散型结构进行拓扑优化设计。     

Simultaneous partial topology and size optimization of a wing structure using ant colony and gradient based methods

This article presents a methodology and process for a combined wing configuration partial topology and structure size optimization. It is aimed at achieving a minimum structural weight by optimizing the structure layout and structural component size simultaneously. This design optimization process contains two types of design variables and hence was divided into two sub-problems. One is structure layout topology to obtain an optimal number and location of spars with discrete integer design variables. Another is component size optimization with continuous design variables in the structure FE model. A multi city-layer ant colony optimization (MCLACO) method is proposed and applied to the topology sub-problem. A gradient based optimization method (GBOM) built in the MSC.NASTRAN SOL-200 module was employed in the component size optimization sub-problem. For each selected layout of the wing structure, a size optimization process is performed to obtain the optimum result and feedback to the layout topology process. The numerical example shows that the proposed MCLACO method and a combination with the GBOM are effective for solving such a wing structure optimization problem. The results also indicate that significant structural weight saving can be achieved.     

Availability allocation to repairable systems with genetic algorithms: a multi-objective formulation

This paper describes a methodology based on genetic algorithms (GA) and experiments plan to optimize the availability and the cost of reparable parallel-series systems. It is a NP-hard problem of multi-objective combinatorial optimization, modeled with continuous and discrete variables. By using the weighting technique, the problem is transformed into a single-objective optimization problem whose constraints are then relaxed by the exterior penalty technique. We then propose a search of solution through GA, whose parameters are adjusted using experiments plan technique. A numerical example is used to assess the method.     

Optimization of truss topology using tabu search

A design procedure for integrating topological considerations in the framework of structural optimization is presented. The proposed approach is capable of considering multiple load conditions, stress, displacement and local/global buckling constraints, and multiple objective functions in the problem formulation. Further, since the proposed method permits members to be added to or deleted from an existing topology and the topology is not defined by member areas, the difficulty of not being able to reach singular optima is also avoided. These objectives are accomplished using a discrete optimization procedure which uses 0–1 topological variables to optimize alternate designs. Since the topological variables are discrete in nature and the member cross-sections are assumed to be continuous, the topological optimization problem has mixed discrete-continuous variables. This non-linear programming problem is solved using a memory-based combinatorial optimization technique known as tabu search. Numerical results obtained using tabu search for single and multiobjective topological optimization of truss structures are presented. To model the multiple objective functions in the problem formulation, a cooperative game theoretic approach is used. The results indicate that the optimum topologies obtained using tabu search compare favourably, and in some instances, outperform the results obtained using the ground–structure approach. However, this improvement occurs at the expense of a significant increase in computational burden owing to the fact that the proposed approach necessitates that the geometry of each trial topology be optimized.     

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.     

An optimization technique using the characteristics of genetic algorithm

Optimization problems could happen often in discrete or discontinuous search space. Therefore, the traditional gradient‐based methods are not able to apply to this kind of problems. The discrete design variables are considered reasonably and the heuristic techniques are generally adopted to solve this problem, and the genetic algorithm based on stochastic search technique is one of these. The genetic algorithm method with discrete variables can be applied to structural optimization problems, such as composite laminated structures or trusses. However, the discrete optimization adopted in genetic algorithm gives rise to a troublesome task that is a mapping between each strings and discrete variables. And also, its solution quality could be restricted in some cases. In this study, a technique using the genetic algorithm characteristics is developed to utilize continuous design variables instead of discrete design variables in discontinuous solution spaces. Additionally, the proposed algorithm, which is manipulating a fitness function artificially, is applied to example problems and its results are compared with the general discrete genetic algorithm. The example problems are to optimize support positions of an unstable structure with discontinuous solution spaces.     

Optimization of truss bridges within a specified design domain using evolution strategies

This article reports and investigates the application of evolution strategies (ESs) to optimize the design of truss bridges. This is a challenging optimization problem associated with mixed design variables, since it involves identification of the bridge’s shape and topology configurations in addition to the sizing of the structural members for minimum weight. A solution algorithm to this problem is developed by combining different variable-wise versions of adaptive ESs under a common optimization routine. In this regard, size and shape optimizations are implemented using discrete and continuous ESs, respectively, while topology optimization is achieved through a discrete version coupled with a particular methodology for generating topological variations. In the study, a design domain approach is employed in conjunction with ESs to seek the optimal shape and topology configuration of a bridge in a large and flexible design space. It is shown that the resulting algorithm performs very well and produces improved results for the problems of interest.     

New conceptual design of aeroelastic wing structures by multi-objective optimization

Internal structural layouts and component sizes of aircraft wing structures have a significant impact on aircraft performance such as aeroelastic characteristics and mass. This work presents an approach to achieve simultaneous partial topology and sizing optimization of a three-dimensional wing-box structure. A multi-objective optimization problem is assigned to optimize lift effectiveness, buckling factor and mass of a structure. Design constraints include divergence and flutter speeds, buckling factor and stresses. The topology and sizing design variables for wing internal components are based on a ground element approach. The design problem is solved by multi-objective population-based incremental learning (MOPBIL). The Pareto optimum results lead to unconventional wing structures that are superior to their conventional counterparts.     

A nodal variable method of structural topology optimization based on Shepard interpolant

A method for topology optimization of continuum structures based on nodal density variables and density field mapping technique is investigated. The original discrete‐valued topology optimization problem is stated as an optimization problem with continuous design variables by introducing a material density field into the design domain. With the use of the Shepard family of interpolants, this density field is mapped onto the design space defined by a finite number of nodal density variables. The employed interpolation scheme has an explicit form and satisfies range‐restricted properties that makes it applicable for physically meaningful density interpolation. Its ability to resolve more complex spatial distribution of the material density within an individual element, as compared with the conventional elementwise design variable approach, actually provides certain regularization to the topology optimization problem. Numerical examples demonstrate the validity and applicability of the proposed formulation and numerical techniques. Copyright © 2011 John Wiley & Sons, Ltd.     

Topology optimization of planar linkage mechanisms

In spite of increasing interest in gradient‐based topology optimization of linkage mechanisms, it is still difficult to solve practical, realistic problems. Besides the apparent difficulty resulting from high nonlinearity, the optimization problem faces other major difficulties: difficulty to satisfy the discrete DOF condition with continuous design variables and lack of intrinsic mechanisms to generate distinct black‐and‐white layouts. To deal with the DOF issue, we propose a new formulation, which maximizes a single objective function, the energy transmittance efficiency. It is shown that the efficiency function maximization handles DOF redundancy and deficiency simultaneously. To obtain distinct linkage layouts, a common practice is to introduce an artificial mass constraint and/or to remove unnecessary links during optimization. However, we do not use any artificial mass constraint but post‐process the optimized result to obtain the final layout by a special post‐processing algorithm. In this study, the linkage design model consists of nonlinear ground bars and zero‐length springs. The springs are used to fix bar‐connecting nodes to the ground, generating pinned joints. After verifying the effectiveness of the proposed approach for four‐bar linkage synthesis, we synthesize an automobile steering mechanism satisfying the Ackermann condition. The steering mechanism problem is solved here for the first time. Copyright © 2014 John Wiley & Sons, Ltd.     

An enhanced genetic algorithm for structural topology optimization

Genetic algorithms (GAs) have become a popular optimization tool for many areas of research and topology optimization an effective design tool for obtaining efficient and lighter structures. In this paper, a versatile, robust and enhanced GA is proposed for structural topology optimization by using problem‐specific knowledge. The original discrete black‐and‐white (0–1) problem is directly solved by using a bit‐array representation method. To address the related pronounced connectivity issue effectively, the four‐neighbourhood connectivity is used to suppress the occurrence of checkerboard patterns. A simpler version of the perimeter control approach is developed to obtain a well‐posed problem and the total number of hinges of each individual is explicitly penalized to achieve a hinge‐free design. To handle the problem of representation degeneracy effectively, a recessive gene technique is applied to viable topologies while unusable topologies are penalized in a hierarchical manner. An efficient FEM‐based function evaluation method is developed to reduce the computational cost. A dynamic penalty method is presented for the GA to convert the constrained optimization problem into an unconstrained problem without the possible degeneracy. With all these enhancements and appropriate choice of the GA operators, the present GA can achieve significant improvements in evolving into near‐optimum solutions and viable topologies with checkerboard free, mesh independent and hinge‐free characteristics. Numerical results show that the present GA can be more efficient and robust than the conventional GAs in solving the structural topology optimization problems of minimum compliance design, minimum weight design and optimal compliant mechanisms design. It is suggested that the present enhanced GA using problem‐specific knowledge can be a powerful global search tool for structural topology optimization. Copyright © 2005 John Wiley & Sons, Ltd.     

Simultaneous optimal design of topology and size for a flexure-hinge-based guiding mechanism to minimize mass under stiffness and frequency constraints

The guiding mechanism based on flexure hinges (FHs) is widely used in micro/nano-manufacturing technology. Both the stiffness and the frequency of FHs play significant roles in their dynamic performance, so the design task of such a structure is to find the optimal topology and corresponding size of FHs under stiffness and frequency constraints. However, the existing optimization methods pay more attention to the stiffness than to the frequency constraint owing to difficulties in dynamic topology optimization. In this article, with the symmetrical layout assumption of FHs and the analytical equivalent stiffness and mass expression of a single FH, the simultaneous topology and size optimization problem is converted to an analytical optimization formula with both discrete and continuous variables. Finally, the tension stiffening effect is used to compensate for manufacturing errors. A design case is used to illustrate the efficiency of the proposed method.     

输电塔结构离散变量优化设计方法

鉴于格构式输电塔结构具有杆件众多、型式复杂等显著特点,所以建设和发展既安全可靠,又经济合理的此类结构一直是工程界的研究热点和难点。因此,该文提出了一套完整的基于蚁群优化算法的输电塔结构离散变量优化设计方法。该方法是在结构的截面、拓扑和形状变量统一转化为离散变量的基础上,将4类不同层次的优化问题统一为不同规模的标准化旅行商问题,并最终采用蚁群算法实现输电塔结构的优化设计。通过对某一实际输电塔结构的优化设计表明:该文方法不仅可以简单高效的求解输电塔结构的截面、拓扑、形状和布局优化问题,而且清晰明确的阐述了不同优化内容的物理意义和优化准则,实现了优化方法和思路的统一。此外,基于蚁群算法的输电塔结构离散变量优化方法通用性强、易于程序化,而且具有非常好的工程应用前景。     

A new harmony search algorithm for solving mixed–discrete engineering optimization problems

In general design optimization problems, it is usually assumed that the design variables are continuous. However, many practical problems in engineering design require considering the design variables as integer or discrete values. The presence of discrete and integer variables along with continuous variables adds to the complexity of the optimization problem. Very few of the existing methods can yield a globally optimal solution when the objective functions are non-convex and non-differentiable. This article presents a mixed–discrete harmony search approach for solving these nonlinear optimization problems which contain integer, discrete and continuous variables. Some engineering design examples are also presented to demonstrate the effectiveness of the proposed method.     

Lightweight design of bus frames from multi-material topology optimization to cross-sectional size optimization

A comprehensive solution for bus frame design is proposed to bridge multi-material topology optimization and cross-sectional size optimization. Three types of variables (material, topology and size) and two types of constraints (static stiffness and frequencies) are considered to promote this practical design. For multi-material topology optimization, an ordered solid isotropic material with penalization interpolation is used to transform the multi-material selection problem into a pure topology optimization problem, without introducing new design variables. Then, based on the previously optimal topology result, cross-sectional sizes of the bus frame are optimized to further seek the least mass. Sequential linear programming is preferred to solve the two structural optimization problems. Finally, an engineering example verifies the effectiveness of the presented method, which bridges the gap between topology optimization and size optimization, and achieves a more lightweight bus frame than traditional single-material topology optimization.     

基于梯度优化方法的钢结构标准截面选型优化设计

实际的工程应用中,钢框架的基本构件大多是根据钢结构设计规范要求,从标准型钢库中选取,所组成的框架结构的截面尺寸非连续变化。因此,钢结构截面优化设计是典型的离散设计变量优化问题。若采用基于启发式的算法(如遗传算法等)进行求解,当可选截面类型较多时,其计算量巨大,求解效率低下。该文通过引入高维拉格朗日插值函数对该离散设计问题进行连续化,建立了可采用梯度优化方法进行求解的钢结构标准截面选型设计模型,并且使得连续化以后的设计变量个数大幅度减少。对给定截面类型种数为2n个的可选截面集合,其设计变量只需n个即可。具体算例表明:与基于遗传算法的优化方法相比,该方法的计算效率提高1~2个数量级,并且在结构性能基本相当的情况下,得到的型钢种类更少,便于工程应用。     

桁架结构智能布局优化设计

结构的布局优化由于涉及尺寸、形状和拓扑三个层次的综合设计而成为优化问题中的难点,结合桁架结构提出了一个基于多个初始基结构的布局优化方法。以智能生成的、型式多样合理的基结构代替传统模型中的单一基结构,然后从不同基结构下的拓扑优化结果中找出最优设计。在克服传统基结构法有可能限制求解空间而丢失最优解这一局限性的同时,将形状和拓扑优化设计有效分离,降低了求解的难度,并且结合拓扑变化法,实现了桁架结构从选型生成、分析计算到优化设计的一体化智能设计过程。算例表明:利用该文提出的方法进行桁架结构的最优布局设计是可靠有效的。