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。     

一种基于新改进的Price算法的混合遗传算法求解约束优化问题

新改进的Price算法能够求解多峰、多维,以及不可微目标函数的全局优化问题.把新改进的Price算法作为局部搜索算子,并入到实数编码遗传算法中,构成一个混合遗传算法,求解约束优化问题.该混合算法增强了全局寻优能力,提高了函数值的精度,并减少了计算量.通过对13个约束标准测试函数的仿真实验,并和已有算法的比较,结果表明本文提出的混合遗传算法是有效的.     

基于改进粒子群算法求解单级多资源约束生产批量计划问题

本文提出了用于求解单级多资源约束的生产批量计划问题的改进二进制粒子群算法,阐明了算法的具体实现过程。通过对其它文献中的例子进行计算和结果比较,表明了该算法在寻优能力、求解速度和稳定性方面都明显优于文献中的传统遗传算法和退火惩罚混合遗传算法。     

车辆战术储备器材包装单元装载优化研究

目的通过对车辆战术储备器材包装单元化装载问题展开研究,为提高部队车辆战术储备器材包装单元装载能力提供技术与方法依据。方法分析装载优化模型的空间、质量、装载平衡和摆放稳定性等主要约束以及约束的处理方法,运用启发式算法对模型求解流程进行合理设计,并提出启发式算法和遗传算法相结合的混合遗传算法,以托盘装载为例说明具体装载过程。结果算例结果表明,托盘空间利用率达到了98.3%以上,装载单元集装稳定性高。结论该算法可减少装载容器数量,提升容器装载能力,优化器材包装单元装载,对提高车辆战术储备器材快速保障能力具有重要意义。     

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。     

求解约束优化问题的退火遗传算法

针对基于罚函数遗传算法求解实际约束优化问题的困难与缺点,提出了求解约束优化问题的退火遗传算法。对种群中的个体定义了不可行度,并设计退火遗传选择操作。算法分三阶段进行,首先用退火算法搜索产生初始种群体,随后利用遗传算法使搜索逐渐收敛于可行的全局最优解或较优解,最后用退火优化算法对解进行局部优化。两个典型的仿真例子计算结果证明该算法能极大地提高计算稳定性和精度。     

基于可靠性的复合材料结构稳定性约束优化设计

基于结构的可靠性, 研究了复合材料结构的稳定性约束优化设计方法。考虑材料及载荷的不确定性, 通过结构可靠性分析的响应面法和有限元法的结合, 对复合材料结构稳定性进行可靠性分析; 利用优化软件iSIGHT集成可靠性分析程序, 实现了以铺层层数及铺层角度为设计变量的复合材料结构稳定性约束问题的可靠性优化方法。对层合板及层合圆柱进行算例分析, 验证了本文中可靠性优化方法的有效性, 为工程实际中的复合材料结构稳定性约束优化设计问题提供借鉴。     

基于变环肋间距的碳纤维/环氧树脂复合材料格栅加筋截顶圆锥壳体稳定性

研究均布外压作用下具有非均匀特征的碳纤维/环氧树脂复合材料格栅加筋（AGS）圆锥壳体构型优化。首先,充分考虑复合材料格栅圆锥壳体中格栅非均匀分布造成结构小端材料利用不充分问题,提出变环肋铺设间距的优化分布方式,使格栅在截顶圆锥壳体结构上小端疏大端密。之后,基于考虑格栅非均匀分布及变环肋间距铺设特征的等效刚度模型,并采用最小势能原理得到环肋铺设优化后的AGS圆锥壳体临界载荷值解析式。针对典型锥壳的有限元验证表明解析算法的误差在1%左右,证实了本文提出的分析方法的可靠性和有效性。最后,通过对环肋间距优化圆锥壳体的参数分析,发现优化环肋分布方式可以使AGS锥壳结构的外压稳定性大幅上升。本文研究内容为碳纤维/环氧树脂复合材料AGS圆锥壳体的优化设计提供了一种具有较高承载力的构型,并为此类结构的计算提供了解析算法。     

基于混合遗传算法的生产工艺规划与决策问题

零件生产加工过程中,由于各加工特征有多个加工工艺而不同工艺方法又有不同的机器选择,以及受工艺约束的工序特征排序问题,使得柔性工艺规划问题具有NP难特性.通过对可选工序和机器进行分段编码;并用约束调整算法解决受工艺约束的工序排序问题;对于问题的多目标特性,采用随机权重来设置适应度函数,用外部精英保留策略并引入k-means聚类算法裁剪精英集来保持群体多样性,该方法通过该混合遗传算法的交差,变异等操作,能有效解决受工序约束的多工艺路线的优化与决策问题.以实例的形式论证了该算法在求解柔性工艺规划问题的有效可行性.     

一种求解船舶配载问题的混合遗传算法

通过对生产实际中散装货船的多品种、大批量货物的配载问题的分析,总结了散装船舶配载问题的特点和钢铁产品的货物堆装规则;以船舶舱容限制、允许重量和货物堆装规则为约束条件,以提高仓容利用率和装载效率为优化目标,构造了一种混合遗传算法来解决此类多目标、多约束的组合优化问题,即①用启发式算法确定各舱货物的具体摆放方式,满足货物装载质量要求,②遗传算法调用启发式算法,计算配载指标,来确定货物在各舱的分布,以满足各舱的仓容限制,提高仓容利用率和装载效率.     

A scatter search algorithm for stacking sequence optimisation of laminate composites

This paper explores the metaheuristic approach called scatter search for lay-up sequence optimisation of laminate composite panels. Scatter search is an evolutionary method that has recently been found to be promising for solving combinatorial optimisation problems. The scatter search framework is flexible and allows the development of alternative implementations with varying degree of sophistication. The main objective of this paper is to demonstrate the effectiveness of the proposed scatter search algorithm for the combinatorial problem like stacking sequence optimisation of laminate composite panels. Preliminary investigations have been carried out to compare the optimal stacking sequences obtained using scatter search algorithm for buckling load maximisation with the best known published results. Studies indicate that the optimal buckling load factors obtained using the proposed scatter search algorithm found to be either superior or comparable to the best known published results.

Later, two case studies have been considered in this paper. Thermal buckling optimisation of laminated composite plates subjected to temperature rise is considered as the first case study. The results obtained are compared with an exact enumerative study conducted on the problem to demonstrate the effectiveness and performance of the proposed scatter search algorithm. The second case study is optimisation of hybrid laminate composite panels for weight and cost with frequency and buckling constraints. The two objectives are considered individually and also collectively to solve as multi-objective optimisation problem. Finally the computational efficiency of the proposed scatter search algorithm has been investigated by comparing the results with various implementations of genetic algorithm customised for laminate composites. It was shown in this paper through numerical experiments that the scatter search is capable of finding practical solutions for optimal lay-up sequence optimisation of composite laminates and results are comparable and sometimes even superior to genetic algorithms.     

Evolutionary multi-objective concurrent maximisation of process tolerances

Concurrent tolerancing which simultaneously optimises process tolerance based on constraints of both dimensional and geometrical tolerances (DGTs), and process accuracy with multi-objective functions is tedious to solve by a conventional optimisation technique like a linear programming approach. Concurrent tolerancing becomes an optimisation problem to determine optimum allotment of the process tolerances under the design function constraints. Optimum solution for this advanced tolerance design problem is difficult to obtain using traditional optimisation techniques. The proposed algorithms (elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE)) significantly outperform the previous algorithms for obtaining the optimum solution. The average fitness factor method and the normalised weighting objective function method are used to select the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of the Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find the computational effort of the NSGA-II and MODE algorithms. Comparison of the results establishes that the proposed algorithms are superior to the algorithms in the literature.     

Optimal design of general stiffened composite circular cylinders for global buckling with strength constraints

A design strategy for optimal design of composite grid-stiffened cylinders subjected to global and local buckling constraints and strength constraints was developed using a discrete optimizer based on a genetic algorithm. An improved smeared stiffener theory was used for the global analysis. Local buckling of skin segments were assessed using a Rayleigh-Ritz method that accounts for material anisotropy. The local buckling of stiffener segments were also assessed. Constraints on the axial membrane strain in the skin and stiffener segments were imposed to include strength criteria in the grid-stiffened cylinder design. Design variables used in this study were the axial and transverse stiffener spacings, stiffener height and thickness, skin laminate stacking sequence and stiffening configuration, where stiffening configuration is a design variable that indicates the combination of axial, transverse and diagonal stiffener in the grid-stiffened cylinder. The design optimization process was adapted to identify the best suited stiffening configurations and stiffener spacings for grid-stiffened composite cylinder with the length and radius of the cylinder, the design in-plane loads and material properties as inputs. The effect of having axial membrane strain constraints in the skin and stiffener segments in the optimization process is also studied for selected stiffening configurations.     

Particle-swarm harmony search for water network design

The optimal design of water distribution networks is a non-linear, multi-modal, and constrained problem classified as an NP-hard combinatorial problem. Because of the drawbacks of calculus-based algorithms, the problem has been tackled by assorted stochastic algorithms, such as the genetic algorithm, simulated annealing, tabu search, shuffled frog-leaping algorithm, ant colony optimization algorithm, harmony search, cross entropy, and scatter search. This study proposes a modified harmony search algorithm incorporating particle swarm concept. This algorithm was applied to the design of four bench-mark networks (two-loop, Hanoi, Balerma, and New York City networks), with good results.     

DESIGN OF COMPOSITE LAMINATES BY A GENETIC ALGORITHM WITH MEMORY

Abstract

This paper describes the use of a genetic algorithm with memory for the design of minimum thickness composite laminates subject to strength, buckling and ply contiguity constraints. A binary tree is used to efficiently store and retrieve information about past designs. This information is used to construct a set of linear approximations to the buckling load in the neighbourhood of each member of the population of designs. The approximations are then used to seek nearby improved designs in a procedure called local improvement. The paper demonstrates that this procedure substantially reduces the number of analyses required for the genetic search. The paper also demonstrates that the use of genetic algorithms helps find several alternative designs with similar performance, thus giving the designer a choice of alternatives.     

Non-smooth/non-convex economic dispatch by a novel hybrid differential evolution algorithm

This paper presents a novel stochastic optimisation approach to determining the feasible optimal solution of the economic dispatch (ED) problem considering various generator constraints. Many practical constraints of generators, such as ramp rate limits, prohibited operating zones and the valve point effect, are considered. These constraints make the ED problem a non-smooth/non-convex minimisation problem with constraints. The proposed optimisation algorithm is called self-tuning hybrid differential evolution (self-tuning HDE). The self-tuning HDE utilises the concept of the 1/5 success rule of evolution strategies (ESs) in the original HDE to accelerate the search for the global optimum. Three test power systems, including 3-, 13-and 40-unit power systems, are applied to compare the performance of the proposed algorithm with genetic algorithms, the differential evolution algorithm and the HDE algorithm. Numerical results indicate that the entire performance of the proposed self-tuning HDE algorithm outperforms the other three algorithms.     

Application of a Mixed Optimization Strategy in the Design of a Pharmaceutical Solid Formulation at Laboratory Scale

The objective of this work was to develop an optimization strategy for the design of pharmaceutical formulations. The mixed strategy was used to optimize a dry powder blend containing 500 mg of alpha methyl dopa to be filled into hard gelatin capsules. The experimental plan consisted of assessing blend flow and dissolution rate using formulations manufactured at small laboratory scale, selecting the optimum formulation, and confirming the data. Two optimization techniques were used in the solid pharmaceutical product design: a genetic algorithm (GA) and a downhill simplex technique. The genetic algorithm used in this work was implemented in an interactive form. Data for each generation of formulations were introduced to the computer with the corresponding values of a fitness function, which was determined in experimental form for each individual formulation. The fitness function used to evaluate product performance (capsule) was defined in terms of the dissolution rate multiplied by a weight function that penalizes those formulations with flow index outside a predefined range. The formulation design contained variable concentrations and types of lubricants/glidants. There were 64 combinations of seven agents with discrete ranges of concentrations codified into a 16-bit chromosome. Crossing and mutation operations were implemented with relatively high probabilities, for generations with a relatively small number of individuals, due to the restrictions imposed by the experimental cost. The mixed formulation strategy based on genetic algorithms and downhill simplex was used to obtain sequentially improved formulations based on two desired targets: in vitro dissolution rate and flow properties. The basic downhill simplex method was used to obtain an optimal formulation on the regression response surface obtained from the genetic algorithm data. The results obtained in this work clearly illustrate the potential of the proposed mixed optimization strategy to obtain optimal formulations.     

Application of a Mixed Optimization Strategy in the Design of a Pharmaceutical Solid Formulation at Laboratory Scale

The objective of this work was to develop an optimization strategy for the design of pharmaceutical formulations. The mixed strategy was used to optimize a dry powder blend containing 500 mg of alpha methyl dopa to be filled into hard gelatin capsules. The experimental plan consisted of assessing blend flow and dissolution rate using formulations manufactured at small laboratory scale, selecting the optimum formulation, and confirming the data. Two optimization techniques were used in the solid pharmaceutical product design: a genetic algorithm (GA) and a downhill simplex technique. The genetic algorithm used in this work was implemented in an interactive form. Data for each generation of formulations were introduced to the computer with the corresponding values of a fitness function, which was determined in experimental form for each individual formulation. The fitness function used to evaluate product performance (capsule) was defined in terms of the dissolution rate multiplied by a weight function that penalizes those formulations with flow index outside a predefined range. The formulation design contained variable concentrations and types of lubricants/glidants. There were 64 combinations of seven agents with discrete ranges of concentrations codified into a 16-bit chromosome. Crossing and mutation operations were implemented with relatively high probabilities, for generations with a relatively small number of individuals, due to the restrictions imposed by the experimental cost. The mixed formulation strategy based on genetic algorithms and downhill simplex was used to obtain sequentially improved formulations based on two desired targets: in vitro dissolution rate and flow properties. The basic downhill simplex method was used to obtain an optimal formulation on the regression response surface obtained from the genetic algorithm data. The results obtained in this work clearly illustrate the potential of the proposed mixed optimization strategy to obtain optimal formulations.     

温度-机械混合载荷作用下先进复合材料格栅加筋截顶圆锥壳体的屈曲分析

基于均匀化原理,推导了考虑沿格栅加筋圆锥壳体随母线变化的等效刚度阵和等效热膨胀系数,并采用前屈曲薄膜理论给出了在温度和均布外压载荷作用下格栅加筋圆锥截顶壳体稳定性分析的总势能表达式。基于最小势能原理得到了该壳体总体失稳的临界载荷值解析表达式,对典型复合材料格栅加筋截顶圆锥壳体的稳定性计算结果与有限元法所得结果相比较,验证了本文中方法的适用性。基于文中提出的方法,通过对不同温度条件下具有不同顶锥角复合材料格栅加筋截顶圆锥壳体热-力屈曲分析结果的讨论,指出温度对复合材料格栅加筋截顶圆锥壳体稳定性的影响程度将随其顶锥角增加而增大。     

含多分层损伤的先进复合材料格栅加筋圆柱壳(AGS)的热-机耦合非线性屈曲分析

采用基于复合材料一阶剪切理论的有限元法研究了含多分层损伤的先进复合材料格栅加筋(AGS)板壳结构的热-机耦合屈曲性态,在屈曲分析中考虑了材料热物理、力学性质与温度相关特性和分层损伤处的上子板、下子板的接触效应。同时在分层前缘采用了位移约束条件以保证分层区域的各子板的变形相容要求。通过一含多分层损伤的典型复合材料格栅(AGS)圆柱壳结构算例分析,讨论了在热-机耦合作用下分层大小、个数和分层位置对该结构屈曲性态的影响。结果表明:复合材料格栅(AGS)圆柱壳结构具有较强的抗热屈曲的能力和良好的损伤容限性。该文提出的方法和所得结论将对AGS结构的热-机耦合屈曲能力的预测和损伤容限设计具有一定参考价值。