现代优化计算方法在材料最优化设计中的应用

简要介绍三种现代优化算法 ,综合利用主成分降维技术、人工神经网络技术和遗传算法技术 ,在V PTC材料介电性能和五个影响因素之间建立神经网络模型 ,然后应用遗传算法搜索最高电阻值和相应的配方。结果表明 :现代优化算法在分析合理选择的样本数据 ,总结其中的数值规律 ,进而对材料性能进行优化设计方面 ,具有重要的应用价值。     

Hybrid neural network model for the design of beam subjected to bending and shear

There is no direct method for design of beams. In general the dimensions of the beam and reinforcement are initially assumed and then the interaction formula is used to verify the suitability of chosen dimensions. This approach necessitates few trials for coming up with an economical and safe design. This paper demonstrates the applicability of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) for the design of beams subjected to moment and shear. A hybrid neural network model which combines the features of feed forward neural networks and genetic algorithms has been developed for the design of beam subjected to moment and shear. The network has been trained with design data obtained from design experts in the field. The hybrid neural network model learned the design of beam in just 1000 training cycles. After successful learning, the model predicted the depth of the beam, area of steel, spacing of stirrups required for new problems with accuracy satisfying all design constraints. The various stages involved in the development of a genetic algorithm based neural network model are addressed at length in this paper.     

大规模物流网络的组合遗传算法研究

在对遗传算法、生成树遗传算法和混合进化方法进行比较的基础上,提出组合遗传算法来解决大规模基本物流网络设计问题.该问题抽象为"选址-分派问题",并进一步分解,且将"分派"镶嵌在"选址"中."选址"和"分派"染色体分别使用二进制编码和矩阵编码、适值采用物流费用.运算结果显示该方法比混合进化方法结果更精确,且在大规模问题求解方面速度优于通常的遗传算法,因此是一种设计大规模物流网络的较好方法.     

一种混合优化策略在工步排序中的应用

结合创成式ＣＡＰＰ系统中工步优化问题，介绍了由遗传算法（ＧｅｎｅｔｉｃＡｌｇｏｒｉｔｈｍｓ，ＧＡ）和模拟退火算法（ＳｉｍｕｌａｔｅｄＡｎｎｅａｌｉｎｇＡｌｇｏｒｉｔｈｍｓ，ＳＡ）构成的混合寻优策略。最后以一箱体的工艺规划过程为例，将基于混合寻优策略的工步排序融入以加工中心为主要加工设备的ＣＡＰＰ工艺决策过程。     

基于逆问题求解的汽车操纵性能优化

给出一种基于逆问题求解的人-车-路闭环系统操纵性能优化的方法.利用径向基函数神经网络建立了汽车侧向位移与方向盘转角及其它响应之间的映射关系,由跟踪路径反求出方向盘转角及汽车的其它响应,进而计算闭环系统的操纵性能评价指标并进行优化.该方法是在不同汽车方案具有相同实际行驶路径的基础上对操纵性能进行分析并优化,从而得到的最优汽车方案在跟踪某一典型路径时具有最好的操纵性能.     

基于计算智能的包装件非线性特性识别研究进展

将人工神经网络方法,模糊自适应控制技术与进化计算应用于包装件缓冲垫层非线性特性的识别问题,可以给出一种为评价、论证与设计缓冲包装提供理论依据的新途径,概述了有关该问题的若干进展情况,讨论了存在的问题并指出了有关的研究发展方向。     

量子神经计算和量子遗传算法的理论分析和应用

经过比较研究发现,在量子计算与神经网络和遗传算法之间,不论在计算思想上还是模型表达上,都存在着许多相似之处,这些相似性启发人们去研究基于量子理论的神经网络和遗传算法模型,一方面探索神经网络和遗传算法在量子系统上的实现方法,另一方面研究量子理论启发下的新的神经网络与遗传算法模型。本文总结了本课题组近年来在量子计算与神经网络和遗传算法相结合领域的研究工作,包括量子系统实现神经计算的理论分析,量子神经网络物理模型的研究,基于量子概率表达的量子遗传算法及其应用研究等,并对今后的发展提出了展望。     

浅海环境参数宽带匹配场反演研究

宽带匹配场反演是获取海洋环境参数信息的有效方法 ,选择合适的反演目标函数对环境参数的估计结果十分重要。文章研究了浅海环境参数宽带匹配场反演的各种目标函数建立方法 ,同时结合亚洲海实验 (ASIAEX2 0 0 1)数据采用遗传全局寻优算法对该海域环境参数进行了反演 ,并对反演结果尤其是沉积层参数的合理性进行了分析。     

Impact Location and Quantification on a Composite Panel using Neural Networks and a Genetic Algorithm

Abstract: The problem of impact detection in composite panels using artificial neural networks is addressed in this paper. The data were taken from an experiment in which time dependent strain data were recorded on a network of surface-mounted piezoceramic sensors when the plate was impacted. Neural networks were trained to locate and quantify the impact event when presented with features extracted from the measured data. An important problem for detection systems like this is that of optimal sensor placement; this is solved here by means of a Genetic Algorithm. The study shows that a relatively small number of sensors can be used to detect reliably impacts on a composite plate.     

Scalability of a Hybrid Extended Compact Genetic Algorithm for Ground State Optimization of Clusters

We analyze the utility and scalability of extended compact genetic algorithm (eCGA)—a genetic algorithm (GA) that automatically and adaptively mines the regularities of the fitness landscape using machine learning methods and information theoretic measures—for ground state optimization of clusters. In order to reduce the computational time requirements while retaining the high reliability of predicting near-optimal structures, we employ two efficiency-enhancement techniques: (1) hybridizing eCGA with a local search method, and (2) seeding the initial population with lowest energy structures of a smaller cluster. The proposed method is exemplified by optimizing silicon clusters with 4-20 atoms. The results indicate that the population size required to obtain near-optimal solutions with 98% probability scales sub linearly (as Θ(n^0.83)) with the cluster size. The total number of function evaluations (cluster energy calculations) scales sub-cubically (as Θ(n^2.45)), which is a significant improvement over exponential scaling of poorly designed evolutionary algorithms.     

嫦娥1号降质月面影像的傅里叶频率域遗传优化增强

本文提出了一种可以衡量带通傅里叶频率系数的低频含量大小的环形条带对比度定义,以该对比度为基础的影像增强方法有效改善了降质月面影像视觉效果,有利于降质月面影像数据的后续处理及应用.借助改进的遗传算法实现了综合考虑绝对质量测度及相对质量测度的增强参数的自适应优化选取.实验结果表明本文方法不仅保持了原始数据的基本信息还提高了其各项绝对质量测度,但由于遗传算法优化处理时计算量较大,本文方法还存在计算速度较慢的不足.     

Computational Intelligence-Based Process Optimization for Tandem Cold Rolling

The requirements for the manufacturers of steel have been increased in many respects in recent years. Harsh competition among manufacturers demands a continuous reduction of production costs and improvement of product quality. The work presented herein describes maximizing rolling mill throughput and minimizing processing costs and crop losses by computational intelligence-based process modeling and optimization. In this article, an intelligent searching mechanism is introduced to optimize the rolling schedule by assessing rolling constraints and the combined cost function of tension, shape, and power distribution. The optimization results have been compared with current rolling practices based on empirical models. It is shown that the proposed model can significantly reduce the power distribution cost, maximize the safe level of strip tension, and obtain good strip shape. The proposed model is generic for complex engineering problem optimization, and is capable of multiple-objective problem solving.     

A hybrid neural network–genetic algorithm approach for permutation flow shop scheduling

The objective of this paper is to find a sequence of jobs for the permutation flow shop to minimise the makespan. The shop consists of 10 machines. A feed-forward back-propagation artificial neural network (ANN) is used to solve the problem. The network is trained with the optimal sequences for five-, six- and seven-job problems. This trained network is then used to solve a problem with a greater number of jobs. The sequence obtained using the neural network is used to generate the initial population for the genetic algorithm (GA) using the random insertion perturbation scheme (RIPS). The makespan of the sequence obtained by this approach (ANN-GA-RIPS) is compared with that obtained using GA starting with a random population (ANN-GA). It was found that the ANN-GA-RIPS approach performs better than ANN-GA starting with a random population. The results obtained are compared with those obtained using the Nawaz, Enscore and Ham (NEH) heuristic and upper bounds of Taillard's benchmark problems. ANN-GA-RIPS performs better than the NEH heuristic and the results are found to be within 5% of the upper bounds.     

微弧等离子喷涂AT13纳米涂层的工艺优化

根据微弧等离子喷涂在不同工艺参数下制备的AT13纳米涂层结合强度和显微硬度的测试结果, 运用遗传神经网络算法对喷涂工艺参数与涂层结合强度和显微硬度之间的非线性关系进行建模与仿真, 并应用遗传算法和多目标优化理论对AT13纳米涂层的性能进行了优化. 研究结果表明, 仿真值与实验值相一致, 相对误差<0.5%; 喷涂电流、Ar流量和Ar压力分别为150A、64m³/h和0.38MPa时, AT13纳米涂层综合性能最佳.     

遗传算法在项目进度计划中的应用

在项目进度计划中,将工序的前后约束关系变换成一个关联矩阵,从可执行的工序集合中随机产生初始化种群,采用改进型的双点交叉算子,并提出了基于关系矩阵的邻位变异算子,避免了不可行个体的产生。文章给出一个3种资源约束的多项目进度计划实例以说明该算法的有效性。     

动力响应约束下的桁架结构拓扑优化

采用自适应遗传算法求解了以脉冲激励下的动力响应作为约束条件、以结构重量最小化为目标函数的桁架结构拓扑优化问题。其中遗传算子分别采用轮盘赌选择算子以及自适应的交叉和变异算子。定义了一些启发式准则来引导优化过程中杆件和节点的删除，另外引入了刚度矩阵的奇异性判断以防止桁架在杆件删除过程中变为机构。算例表明，用此方法可以得到桁架结构在脉冲激励下的最优拓扑形式，且能在满足动力响应约束要求的前提下极大地减轻结构重量，达到优化的目的。     

遗传算法在网络计划费用优化中的应用

人们对网络计划费用优化问题的研究主要是基于传统的网络计划技术和数学规划的方法，但是这些方法都存在一些缺陷。为解决传统优化方法中存在的问题，将经过改进的遗传算法应用于费用优化当中，从而可以得到一种更精确、求解效率更高的优化方法。