基于数字序列编码遗传算法的高层结构粘滞阻尼器优化布置

提出一种基于数字序列编码遗传算法的高层结构黏滞阻尼器优化布置方法,解决了允许各层阻尼器安装数量不同时,二进制编码的标准遗传算法不能完备表达求解空间的问题。数字序列编码用染色体的一个基因位表示一个阻尼器的安装位置,其数值表示该阻尼器的安装层数。在这一编码方案下,优化问题基因型空间中的染色体和表现型空间中的可选布置方案一一对应,编码满足严格的合法性、完备性、Lamarckian性质以及强因果性。与数字序列编码方式相对应,离散重组交叉算子保证了种群的有效进化。此外,基于染色体目标函数值的相对大小构造适应度函数,能够充分体现种群中染色体的适应度差异,加速种群的进化,进而获得优化问题的全局最优解。对多遇地震下20层Benchmark结构的阻尼器布置方案进行了优化,计算结果表明了该方法的有效性。     

基于特征的钣金折弯工序优化研究

针对钣金折弯工艺的特点,提出基于零件结构的FE图以及折弯工艺特征合并的二步折弯数据模型的构建方法．再依据启发式搜索规则、干涉约束等条件将零件折弯方案缩减在一个有限的折弯工序组合可行空间．最后考虑模具更换、工步数目、工件翻面和掉头次数等因素,可以直接计算得出最优的折弯工序．     

Performance comparison of several optimization algorithms in matched field inversion

Optimization efficiencies and mechanisms of simulated annealing, genetic algorithm, differential evolution and downhill simplex differential evolution are compared and analyzed. Simulated annealing and genetic algorithm use a directed random process to search the parameter space for an optimal solution. They include the ability to avoid local minima, but as no gradient information is used, searches may be relatively inefficient. Differential evolution uses information from a distance and azimuth between individuals of a population to search the parameter space, the initial search is effective, but the search speed decreases quickly because differential information between the individuals of population vanishes. Local downhill simplex and global differential evolution methods are developed separately, and combined to produce a hybrid downhill simplex differential evolution algorithm. The hybrid algorithm is sensitive to gradients of the object function and search of the parameter space is effective. These algorithms are applied to the matched field inversion with synthetic data. Optimal values of the parameters, the final values of object function and inversion time is presented and compared.     

几种优化算法在浅海匹配场反演中的性能比较

在分析模拟退火算法、遗传算法、差异进化算法、下山单纯形差异进化算法的优化机理的基础上,定量比较了上述算法在浅海匹配场反演中的效率差异。模拟退火算法与遗传算法只使用目标函数值信息在参数空间搜索全局最优值,效率低且易受参数间耦合的影响。差异进化算法使用种群中个体间的距离与方位信息在参数空间中搜索全局最优值,优化效率随着优化过程的进行而下降。下山单纯形差异进化算法将下山单纯形算法融入差异进化算法,增强了差异进化算法的寻优能力,混合算法对目标函数梯度信息敏感的特性使得这一算法具有较强的解耦能力。浅海匹配场反演仿真算例从最优参数反演结果、最终目标函数值、反演时间等方面检验了上述算法的反演效率。     

Coordination of set-ups between two stages of a supply chain using multi-objective genetic algorithms

A multi-objective genetic algorithm (MOGA) solution approach for a sequencing problem to coordinate set-ups between two successive stages of a supply chain is presented in this paper. The production batches are processed according to the same sequence in both stages. Each production batch has two distinct attributes and a set-up occurs in the upstream stage every time the first attribute of the new batch is different from the previous one. In the downstream stage, there is a set-up when the second attribute of the new batch is different from that of the previous one. Two objectives need to be considered in sequencing the production batches including minimizing total set-ups and minimizing the maximum number of set-ups between the two stages. Both problems are NP-hard so attainment of an optimal solution for large problems is prohibited. The solution approach starts with an initialization stage followed by evolution of the initial solution set over generations. The MOGA makes use of non-dominated sorting and a niche mechanism to rank individuals in the population. Selected individuals taken from a given population form the succeeding generation using four genetic operators as: reproduction, crossover, mutation and inversion. Experiments in a number of test problems show that the MOGA is capable of finding Pareto-optimal solutions for small problems and near Pareto-optimal solutions for large instances in a short CPU time.     

Parallel Optimization of Program Instructions Using Genetic Algorithms

This paper describes an efficient solution to parallelize software program instructions, regardless of the programming language in which they are written. We solve the problem of the optimal distribution of a set of instructions on available processors. We propose a genetic algorithm to parallelize computations, using evolution to search the solution space. The stages of our proposed genetic algorithm are: The choice of the initial population and its representation in chromosomes, the crossover, and the mutation operations customized to the problem being dealt with. In this paper, genetic algorithms are applied to the entire search space of the parallelization of the program instructions problem. This problem is NP-complete, so there are no polynomial algorithms that can scan the solution space and solve the problem. The genetic algorithm-based method is general and it is simple and efficient to implement because it can be scaled to a larger or smaller number of instructions that must be parallelized. The parallelization technique proposed in this paper was developed in the C# programming language, and our results confirm the effectiveness of our parallelization method. Experimental results obtained and presented for different working scenarios confirm the theoretical results, and they provide insight on how to improve the exploration of a search space that is too large to be searched exhaustively.     

遗传算法在桁架结构优化设计中的应用

本文提出桁架结构系统优化设计的新方法—遗传算法。它与常规化算法的不同之处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。该算法不必求导计算，编程简单、快捷，尤其适用于具有离散变量的结构优化设计问题。     

基于相对适应度遗传算法的高层结构粘滞阻尼器优化布置

提出基于相对适应度遗传算法的高层结构粘滞阻尼器优化布置方法,综合考虑多种地震波作用与多个不同优化目标。较标准遗传算法,相对适应度遗传算法基于染色体目标函数值相对大小构造适应度函数能充分体现种群中染色体适应度差异,促进种群的有效进化,获得优化问题的全局最优解。在讨论目标函数加权系数对优化结果影响基础上,对目标函数进行归一化处理,并考虑多种地震波作用。通过用该方法对20层框架结构的粘滞阻尼器布置方案进行优化,数值计算结果证明该方法的有效性及可用性。     

Prediction of the Bending Capability of Rolled Metal Sheet by Genetic Programming

The paper proposes genetic programming (GP) to predict the bending capability of rolled titanzinc metal sheet. In this study ZnTiCu alloy with ∼ 0.1% Cu and ∼ 0.1% Ti was used for production of metal sheet. Three groups of independent input variables were measured: (1) chemical composition of the ZnTiCu alloy during casting (percentage of Cu, Ti, and Fe), (2) parameters of hot rolling (temperature of ingot before rolling, time of rolling, temperature of plate after rolling, time of cooling), and (3) parameters of cold rolling (temperature of plate before rolling, temperature of sheet after rolling). Therefore, nine input variables (parameters) influence the bending capability of the sheet metal. On the basis of the experimental data, several models for prediction of the bending capability of titanzinc metal sheet were developed by the simulated evolution. The influence of individual input variables on bending capability was also studied. The most accurate model was verified with an independent testing data set. The results showed that GP is a powerful tool for predicting the bending capability of metal sheet.     

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.     

Automatic determination of bending sequences for sheet metal parts with parallel bends

A method for solving the problem of bend sequencing in sheet metal manufacturing is presented. The algorithm developed divides the part into basic shapes (channels and spirals) and determines the partial sequences associated with them. The complete bending sequences associated with the complete part were obtained from the combination of these partial sequences. To make this combination possible, several strategies were defined to reduce the number of solutions and, therefore, the searching time. All sequences (partial and complete) were checked considering possible part-tool collisions and tolerance constraints. The last stage was to order the sequences by taking into account the total process time. To attain the required process time accuracy, a robot was used for the handling operations. Finally, the sequence associated with the lower process time was selected as the optimal solution.     

Selecting the best sheet length for the steel stock used in circular blank production

Silicon steel circular blanks are often used in the manufacture of electric motors. Most factories use rectangular sheets to make these blanks, however, the silicon steel is usually produced in rolls and thus these rolls must be cut at the steelworks into sheets with user-specified lengths. An algorithm to determine the optimal sheet length is presented in this paper. It considers the sheet lengths in an ascending order, and selects the best one of them. The optimal solution for a specific length is then taken to be the initial solution of the next length. A cutting pattern usually consists of one or two segments. Strips in a segment have the same length and direction, which is either horizontal or vertical. More than one row of identical blanks can be produced from a strip. The algorithm uses dynamic programming to determine the optimal strip layouts on segments, and finds the optimal segment lengths through implicit enumeration. The computational results indicate that the algorithm is efficient both in computation time and in material usage. Finally, the solution to an example is presented.     

Fuzzy programming for optimal product mix decisions based on expanded ABC approach

In sheet metal fabrication, bending is used in order to obtain rigidity and to obtain a part of desired shape and function. In analysing a sheet metal part, an important consideration is how to unfold the part after a bending operation or series of bending operations. The unfolding process is the first major step in process planning for generating NC paths for a sheet metal blank. This paper addresses the problem of determining whether or not a part can be unfolded. A graph-based approach using the face-edge of the sheet metal part is at the heart of the algorithm presented here. Using this algorithm one can determine which faces are unfoldable and which cannot be unfolded. This algorithm can be used to help facilitate the process of NC path generation. It also sheds some light on the kind of design practices that make a part easily unfoldable or otherwise.     

考虑初始残余应力的板料弯曲回弹理论分析

在实际弯曲加工过程中,板料内部如果带有初始残余应力,将与弯曲应力发生叠加,对板料的回弹产生一定的影响。由于传统的回弹理论都没有考虑初始残余应力的影响,该文基于平面应变假设,采用服从Mises屈服准则和线性强化材料模型,推导了考虑初始残余应力的板料弯曲回弹角近似公式并基于有限元软件ABAQUS进行了残余应力板料弯曲回弹仿真对比分析。理论计算与仿真结果具有较好的一致性,验证了理论模型的正确性。研究结果表明,残余应力和厚度对板料回弹均有较大影响:沿宽度方向,不同初始残余应力处的板料回弹并不均匀;增大初始残余应力峰值和减小板料厚度均使不同初始残余应力处板料的回弹差值增大。     

Parameter Identification Method of Large Macro-Micro Coupled Constitutive Models Based on Identifiability Analysis

Large and complex macro-micro coupled constitutive models, which describe metal flow and microstructure evolution during metal forming, are sometimes overparameterized with respect to given sets of experimental datum. This results in poorly identifiable or non-identifiable model parameters. In this paper, a systemic parameter identification method for the large macro-micro coupled constitutive models is proposed. This method is based on the global and local identifiability analysis, in which two identifiability measures are adopted. The first measure accounts for the sensitivity of model results with respect to single parameters, and the second measure accounts for the degree of near-linear dependence of sensitivity functions of parameter subsets. The global identifiability analysis adopts a sampling strategy with only a limited number of model evaluations, and the strategy is a combination of Latin-hypercube sampling, one-factor-at-a-time sampling and elitism preservation strategy. The global identifiability index is the integration of the corresponding local index. A hybrid global optimization method is designed to identify the parameter. Firstly, the genetic algorithm is adopted to identify the model parameter rudely, and then the obtained parameter is further refined through the improved Levenberg-Marquardt algorithm. The niching method is used to maintain the population diversity and to choose the initial value for the Levenberg-Marquardt algorithm. A transition criterion between the genetic algorithm and the Levenberg-Marquardt algorithm is proposed, through the improvement on the average objective function value of the chromosomes and the objective function value of the best chromosome. During optimization by the Levenberg-Marquardt algorithm, the local identifiability analysis is taken at the beginning stage of each iteration, and then the variable with poor identifiability remains unchanged in this iteration; the problem of violation constraint for some solution is solved through adjusting the search step length. At last, taking Ti-6Al-4V as an example, a set of satisfactory material parameters is obtained. The calculated results agree with the experimental results well. The identified results show that some parameters involved in the model are poorly identifiable; at the same time, the identifiability analysis method can provide a guide to experiment design.     

Shell element formulation of multi‐step inverse analysis for axisymmetric deep drawing process

Inverse analysis today is generally performed with membrane models in analysing sheet metal forming processes. Given the final desired configuration, it usually estimates the deformation in a one‐step calculation. However, for some practical problems where the bending effect is significant and the strain history departs from a linear path, this calculation becomes not good enough to provide the optimal design values. In this paper, an axisymmetric shell element for the multi‐step inverse analysis is developed for more accurate prediction of design variables such as the initial blank shape, strain distributions, and intermediate shapes, etc.The algorithm has been applied to deep drawing processes for both thin and relatively thick sheet metal. Numerical examples demonstrate that the proposed combination of shell element and multi‐step inverse analysis can provide more precise results than the previous algorithms used in inverse analysis. Copyright © 2001 John Wiley & Sons, Ltd.     

遗传算法的改进及其在水库优化调度中的应用研究

遗传算法是通过对样本中个体的不断改进来寻找各类问题的最优解。由于标准遗传算法（SGA）存在收敛性及个体适应度求解方面的困难，在研究中，通过对SGA中遗传算子改进，特别是对选择算子的改进，提出了一种改进遗传算法（AGA），并将它应用于水库优化调度中。改变通常以水位变化序列为基础的遗传算法编码方案，通过数组存储水库库容状态，并以各库容状态对应的数组下标为基础进行遗传算法编码，通过实例，表明AGA对水库优化调度问题具有良好的适应性，同时结合数组存储理论的遗传算法编码方法简化了水库优化调度遗传算法的实现过程。     

一种用于匹配场反演的遗传算法

遗传算法在接近全局最优解时,存在搜索速度变慢、过早收敛、个体的多样性减少很快、甚至陷入局部最优解等问题。通过在遗传算法中引入模拟退火因子、混沌因子和多样性测度因子,在很大程度上克服了原有遗传算法的早熟、局部搜索能力差的缺点。同时,又能发挥原有遗传算法的强大的全局搜索能力,保证了改进后的混合遗传算法能较好地收敛于其全局最优值。     

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.     

一种改进的广义遗传算法及其在结构动力优化问题中的应用

该文提出了一种改进的广义遗传算法。算法中引入了异种机制以提高种群的多样性,在保证收敛速度的同时防止早熟收敛。该方法应用于随机风载荷作用下有应力约束的多参数结构动力响应优化问题,数值算例表明:异种机制能够有效地提高广义遗传算法收敛于全局最优解的概率并加快收敛速度;带有异种机制的广义遗传算法能够有效地求解复杂的结构动力优化问题。