遗传算法用于衍射光学元件的优化设计

提出了一种基于遗传算法的衍射光学元件优化设计方法;在衍射光学元件设计中遗传算法运行参数对遗传算法性能有一定的影响:采用较大的群体规模,遗传算法越容易获得最优解;交叉算子越大,遗传算法全局搜索能力越强;选择算子对遗传算法的影响不是太大;如果要进一步提高解的精度,可选取较大的终止代数。数值计算结果表明,用遗传算法优化设计的衍射光学元件,其误差小于 5.2%,衍射效率达到 91.2%。遗传算法很适合衍射光学元件的优化设计。     

A robust Island Parallel Genetic Algorithm for the Quadratic Assignment Problem

The Quadratic Assignment Problem (QAP) is a difficult and important problem studied in the domain of combinatorial optimisation. It is possible to solve QAP instances with 10--20 facilities using exhaustive parallel algorithms within a few days on a cluster machine. However, large QAP instances with more than 100 facilities are not solvable using exhaustive techniques. We have explored a variety of Genetic Algorithm crossover operators for this problem and verified its performance experimentally using well-known instances from the QAPLIB library. By increasing the number of processors, generations and population sizes we have been able to find solutions that are the same as (or very close to) the best reported solutions for large QAP instances in QAPLIB. In order to parallelise the Genetic Algorithm we generate and evolve separate solution pools on each cluster processor, using an island model. This model exchanges 10% of each processor’s solutions at the initial stages of optimisation. We show experimentally that both execution times and solution qualities are improved for large QAP instances by using our Island Parallel Genetic Algorithm.     

一种改进的k-means文档聚类初值选择算法

提出了一种改进的基于最小最大原则的k-means文档聚类初始值选择算法.该方法首先构造相似度矩阵,然后利用最小最大原则对相似度矩阵进行分析,从而选择初始聚点并自动确定聚类k值.实验结果表明利用该方法找到的k值比较接近真实值.     

Genetic algorithms and simulated annealing for scheduling in agile manufacturing

In this paper, genetic algorithms and simulated annealing are applied to scheduling in agile manufacturing. The system addressed consists of a single flexible machine followed by multiple identical assembly stations, and the scheduling objective is to minimize the makespan. Both genetic algorithms and simulated annealing are investigated based on random starting solutions and based on starting solutions obtained from existing heuristics in the literature. Overall, four new algorithms are developed and their performance is compared to the existing heuristics. A 2³ factorial experiment, replicated twice, is used to compare the performance of the various approaches, and identify the significant factors that affect the frequency of resulting in the best solution and the average percentage deviation from a lower bound. The results show that both genetic algorithms and simulated annealing outperform the existing heuristics in many instances. In addition, simulated annealing outperforms genetic algorithms with a more robust performance. In some instances, existing heuristics provide comparable results to those of genetic algorithms and simulated annealing with the added advantage of being simpler. Significant factors and interactions affecting the performance of the various approaches are also investigated.     

Genetic algorithms and Monte Carlo simulation for optimal plant design

We present an approach to the optimal plant design (choice of system layout and components) under conflicting safety and economic constraints, based upon the coupling of a Monte Carlo evaluation of plant operation with a Genetic Algorithms-maximization procedure. The Monte Carlo simulation model provides a flexible tool, which enables one to describe relevant aspects of plant design and operation, such as standby modes and deteriorating repairs, not easily captured by analytical models. The effects of deteriorating repairs are described by means of a modified Brown–Proschan model of imperfect repair which accounts for the possibility of an increased proneness to failure of a component after a repair. The transitions of a component from standby to active, and vice versa, are simulated using a multiplicative correlation model. The genetic algorithms procedure is demanded to optimize a profit function which accounts for the plant safety and economic performance and which is evaluated, for each possible design, by the above Monte Carlo simulation.In order to avoid an overwhelming use of computer time, for each potential solution proposed by the genetic algorithm, we perform only few hundreds Monte Carlo histories and, then, exploit the fact that during the genetic algorithm population evolution, the fit chromosomes appear repeatedly many times, so that the results for the solutions of interest (i.e. the best ones) attain statistical significance.     

Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)—the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets.     

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.     

利用遗传算法优化透明质酸发酵培养基

运用遗传算法对透明质酸(HA)产生菌--马链球菌兽瘟亚种ATCC 39920发酵培养基的6种组份进行了优化研究.每个长度为36位的染色体编码一种培养基配方,以HA产量为适应度函数值对其进行评价.经过4代的进化,各参数的取值范围收敛于最优区域.最终以40个实验样本完成了6种培养基成分、64个浓度水平的优化选择.优化后的培养基的构成为:葡萄糖44.0g/L,酵母膏5.2g/L,蛋白胨8.4g/L,牛肉膏9.8g/L,KH2PO41.45g/L,MgSO42.8g/L.采用优化培养基的HA产量达0.395g/L,较原培养基提高了31.2%,生产成本也大幅度降低.     

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.     

Genetic algorithms for high-precision reconstructions of three-dimensional topographies using stereo fractographs

A software is developed which enables reconstruction of the three-dimensional (3D) shape of fracture surfaces without human assistance. It is based upon computer image processing and pattern recognition techniques by using a stereo-pair of scanning electron micrographs. The processing consists of two subprocesses: searching the matching points between two images; and computation of heights using the relative shift of the matching points. By using the previously developed system, some mismatches were inevitable in the search process, in particular, for low-contrast SEM images, e.g. striations, intergranular facets, etc. In order to improve the accuracy of the search, a genetic algorithm (GA) was implemented into the developed system. By using the GA method, the 3D shapes of a wide variety of fracture surfaces including cleavage failures, intergranular cracking, dimples and fatigue striations, were successfully reconstructed with sufficient accuracy. The searching processes by the GA method and the previously developed two-step algorithm of coarse and close searching were compared. These proved that the GA method has both the advantage of accuracy in the searching process and a short run-time. A detailed 3D shape, of more than a 120 × 120 reconstructed point-sized shape, was thus obtained with sufficient accuracy and with a relatively short run-time.     

基于遗传模糊C-均值聚类算法的除湿机故障诊断

针对除湿机系统的故障诊断问题及其特点,以CFTZ21型除湿机为对象,应用模糊C-均值聚类（FCM）算法进行了研究;引入遗传算法对传统模糊C-均值聚类算法进行了改进,克服了传统算法的不足;结合实验采集到的数据样本,对改进后的遗传模糊C-均值聚类算法进行检验,结果达到预期效果,由此说明,将改进的FCM应用于除湿机故障诊断是可行的。     

Module partition for 3D CAD assembly models: a hierarchical clustering method based on component dependencies

Reusing previous CAD assembly models directly in new product development is almost impossible in One-of-a-Kind Production (OKP) in which customer requirements vary from one to another. As such, modularisation of CAD assembly models is required to facilitate modular design for OKP. However, to the authors’ best knowledge, there has been no research carried out on modularisation of CAD assembly models so far. To bridge this gap and make the best use of existing CAD models, this paper proposes a novel module partition approach, to group existing CAD assembly models into modules based on component dependencies. In this approach, an extraction algorithm was developed to extract assembly information from a given assembly model directly, by using automated programmable interfaces of CAD software tools. The extracted information is processed to generate the component design structure matrix (DSM) representing hierarchical relations and dependency strengths between components. Four popular hierarchical clustering methods were used to work with the component DSM to produce results of module partition. A case study was carried out to illustrate the proposed methods and demonstrate their feasibility. It enables OKP companies to respond rapidly to changing customer requirements and develop customised products in a short period.     

Combining neural networks and genetic algorithms for optimising the parameter design of the inter-metal dielectric process

An inter-metal dielectric (IMD) is deposited between metal layers to provide isolation capability to a device and separate the different metal layers that are unnecessary in the conduction of electricity. Owing to the complicated input/response relationships of the IMD process, the void problem results in electric leakage and causes wafer scraping. In the current study, we combined neural networks, genetic algorithms (GAs) and the desirability function in order to optimise the parameter settings of the IMD process. Initially, a backpropagation (BP) neural network was developed to map the complex non-linear relationship between the process parameters and the corresponding responses. Moreover, the desirability function and GAs were employed to obtain the optimum operating parameters in respect to each response. The implementation of the proposed approach was carried out in a semiconductor manufacturing company in Taiwan, and the results illustrate the practicability of the proposed approach.     

改进遗传算法结合FLANN在加速度传感器动态建模中的应用

对遗传算法（CA）的交叉和变异操作进行改进，提出利用改进遗传算法（ICA）和函数连接型人工神经网络（FLANN）相结合实现加速度传感器的动态建模的新方法。该方法利用加速度传感器的动态标定数据，采用IGA和FLANN相结合搜索和优化动态模型参数。文中介绍动态建模原理以及算法，给出用IGA和FLANN相结合建立的加速度传感器动态数学模型。结果表明：上面提出的动态建模方法既保留了CA的全局搜索能力和FLANN结构简单的特点，又具有网络训练速度快、实时性好、建模精度高等优点，在动态测试领域具有重要应用价值。     

基于有向相似性联想的产品创新设计思维方法

提出发散与收敛交替的创新设计思维模型,分析基于实例的推理、遗传算法的不足并指出其深层原因是它们只能实现创新思维模型中的单一阶段.然后提出一种有向相似性联想模拟算法以实现部件间的产品需求相似度计算,应用有向相似性计算与遗传算法形成一个完整的创新思维模型,并进行了实例分析.     

Globally optimal tool paths for sculptured surfaces with emphasis to machining error and cutting posture smoothness

Global optimisation for manufacturing problems is mandatory for obtaining versatile benefits to facilitate modern industry. This paper deals with an original approach of globally optimising tool paths to CNC-machine sculptured surfaces. The approach entails the development of a fully automated manufacturing software interface integrated by a non-conventional genetic/evolutionary algorithm to enable intelligent machining. These attributes have been built using already existing practical machining modelling tools such as CAM systems so as to deliver a truly viable computer-aided manufacturing solution. Since global optimisation is heavily based on the formulation of the problem, emphasis has been given to the definition of optimisation criteria as crucial elements for representing performance. The criteria involve the machining error as a combined effect of chord error and scallop height, the tool path smoothness and productivity. Experiments have been designed considering several benchmark sculptured surfaces as well as tool path parameters to validate the aforementioned criteria. The new approach was implemented to another sculptured surface which has been extensively tested by previous research works. Results were compared to those available in the literature and it was found that the proposed approach can indeed constitute a promising and trustworthy technique for the global optimisation of sculptured surface CNC tool paths.     

Optimization of Semiempirical Quantum Chemistry Methods via Multiobjective Genetic Algorithms: Accurate Photodynamics for Larger Molecules and Longer Time Scales

Excited-state photodynamics is important in numerous varieties of important materials applications (e.g., liquid crystal display, light emitting diode), pharmaceuticals, and chemical manufacturing processing. We study the effectiveness of multiobjective genetic and evolutionary algorithms in multiscaling excited-state direct photodynamics via rapid reparameterization of semiempirical methods. Using a very limited set of ab initio and experimental data, semiempirical parameters are reoptimized to provide globally accurate potential energy surfaces, thereby eliminating the need for expensive ab initio dynamics simulations. Through reoptimization, excited-state energetics are predicted accurately via semiempirical methods, while retaining accurate ground-state predictions. In our initial study of small photo-excited molecules, our results show that the multiobjective evolutionary algorithm consistently yields solutions that are significantly better—up to 384% lower error in the energy and 87% lower error in the energy-gradient—than those reported previously. As verified with direct quantum dynamical calculations, multiple high-quality parameter sets obtained via genetic algorithms show near-ideal behavior on critical and untested excited-state geometries. The results demonstrate that the reparameterization via evolutionary algorithms is a promising way to extend direct dynamics simulations of photochemistry to multi-picosecond time scales and to larger molecules, with promise in more application beyond simple molecular chemistry.