Blast furnace charging optimization using multi-objective evolutionary and genetic algorithms

Charging programs giving rise to desired burden and gas distributions in the ironmaking blast furnace were detected through an evolutionary multi-objective optimization strategy. The Pareto optimality condition traditionally used in such studies was substituted by a recently developed k-optimality criterion that allowed for simultaneous optimization of a large number of objectives, leading to a significant improvement over the results of earlier studies. A large number of optimum charging strategies were identified through this procedure and thoroughly analyzed, in view of an efficient blast furnace operation.     

基于遗传算法的散热器优化设计

根据电子设备散热装置缩小体积和增强散热性能的设计要求,分析散热器在强迫风冷条件下的散热性能优化问题。建立了热阻优化目标函数,采用遗传算法对强迫风冷情况下的散热器进行优化设计,计算出散热器的优化尺寸和优化前后的热阻,优化结果表明散热器体积和热阻可同时减小。     

Engineering design optimization using species-conserving genetic algorithms

The species conservation technique described here, in which the population of a genetic algorithm is divided into several groups according to their similarity, is inspired by ecology. Each group with similar characteristics is called a species and is centred on a dominating individual, called the species seed. A genetic algorithm based on this species conservation technique, called the species-conserving genetic algorithm (SCGA), was established and has been proved to be effective in finding multiple solutions of multimodal optimization problems. In this article, the SCGA is used to solve engineering design optimization problems. Different distance measures (measures of similarity) are investigated to analyse the performance of the SCGA. It is shown that the Euclidean distance is not the only possible basis for defining a species and sometimes may not make sense in engineering applications. Two structural design problems are used to demonstrate how the choice of a meaningful measure of similarity will help the exploration for significant designs.     

Optimal packetization scheme for embedded multimedia bitstreams using genetic algorithms

The problem of packetizing embedded multimedia bitstreams into fixed‐size packets is investigated and an optimal packetization scheme using a genetic algorithm is presented. In the proposed method, each individual packetization instance is represented by a decision sequence and mapped to a chromosome. A steady‐state genetic algorithm is applied to search for the optimal chromosome that minimizes the distortion between the original and the reconstructed media. In addition, we propose a fast method to calculate the fitness value based on the distortion associated with each chromosome to speed up the evolution process. The computer simulation results show that our proposed packetization scheme has high compression efficiency and provides error resiliency to packet losses with a relatively fast speed. © 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 77–84, 2006     

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.     

遗传算法在两分频扬声器系统设计中的应用

文章论述了运用遗传算法优化设计两分频扬声器分频网络的方法。使用实测的扬声器阻抗、频响数据,模拟扬声器接入分频网络后扬声器系统的响应,并运用遗传算法对系统响应进行优化设计。文中给出的实例表明,设计结果和实测的曲线是一致的。     

An adaptive penalty scheme for genetic algorithms in structural optimization

A parameter‐less adaptive penalty scheme for genetic algorithms applied to constrained optimization problems is proposed. Using feedback from the evolutionary process the procedure automatically defines a penalty parameter for each constraint. The user is thus relieved from the burden of having to determine sensitive parameter(s) when dealing with every new constrained optimization problem. The procedure is shown to be effective and robust when applied to test problems from the evolutionary computation literature as well as several optimization problems from the structural engineering literature. Copyright © 2003 John Wiley & Sons, Ltd.     

Towards the optimal design of an uncemented acetabular component using genetic algorithms

Aseptic loosening of the acetabular component (hemispherical socket of the pelvic bone) has been mainly attributed to bone resorption and excessive generation of wear particle debris. The aim of this study was to determine optimal design parameters for the acetabular component that would minimize bone resorption and volumetric wear. Three-dimensional finite element models of intact and implanted pelvises were developed using data from computed tomography scans. A multi-objective optimization problem was formulated and solved using a genetic algorithm. A combination of suitable implant material and corresponding set of optimal thicknesses of the component was obtained from the Pareto-optimal front of solutions. The ultra-high-molecular-weight polyethylene (UHMWPE) component generated considerably greater volumetric wear but lower bone density loss compared to carbon-fibre reinforced polyetheretherketone (CFR-PEEK) and ceramic. CFR-PEEK was located in the range between ceramic and UHMWPE. Although ceramic appeared to be a viable alternative to cobalt–chromium–molybdenum alloy, CFR-PEEK seems to be the most promising alternative material.     

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.     

Solving structural optimization problems with genetic algorithms and simulated annealing

In this paper we study the performance of two stochastic search methods: Genetic Algorithms and Simulated Annealing, applied to the optimization of pin‐jointed steel bar structures. We show that it is possible to embed these two schemes into a single parametric family of algorithms, and that optimal performance (in a parallel machine) is obtained by a hybrid scheme. Examples of applications to the optimization of several real steel bar structures are presented. Copyright © 1999 John Wiley & Sons, Ltd.     

基于遗传算法的20 K氦制冷机的优化设计及其热力学分析

通过计算机程序实现10 k W@20 K的大型氦制冷机的热力学过程,在此基础上分析了流程中压缩机和透平膨胀机入口压力对系统制冷系数和效率的影响。同时与Matlab编写的遗传算法接口相连交互数据,从而实现针对该制冷机热力学参数的优化。选取制冷量和系统效率的加权值为优化目标,压缩机和透平膨胀机入口压力为待优化参数,通过遗传算法(GA)的优化计算得到了稳定的最优解,表明遗传算法在优化大型制冷系统过程中可以快速高效搜索到满足目标要求的全局最优解。     

Visual data association for real‐time video tracking using genetic and estimation of distribution algorithms

In this article, an efficient and novel approach for video data association is developed. This new method is formulated as a search across the hypotheses space defined by the possible association among tracks and detections, carried out for each frame of a video sequence. The full data association problem in visual tracking is formulated as a combinatorial hypotheses search with a heuristic evaluation function taking into account structural and specific information such as distance, shape, color, etc. To guarantee real‐time performance, a time limit is set for the search process explore alternative solutions. This time limit defines the upper bound of the number of evaluations depending on search algorithm efficiency. Estimation distribution algorithms are proposed as an efficient evolutionary computation technique to search in this hypothesis space. Finally, an exhaustive comparison of the performance of alternative algorithms is carried out considering complex representative situations in real video sets. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 208–220, 2009     

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。     

Optimisation of a set of or principal components control charts using genetic algorithms

When a multivariate process is to be monitored, there are the options of employing a set of univariate control charts or a single multivariate chart. This paper shows how to effectively design a multivariate control scheme consisting of two or three X charts, using genetic algorithms to optimise the charts parameters. The procedure is implemented using software tools, which we designed. A complete performance comparison of the scheme with the Hotelling's T ² control chart can be made in order to help the user in choosing the most adequate option for the process under consideration. Also, if the user prefers to employ charts based on principal components rather than on the original variables, the software can be used in the same way to optimise a set of two or three control charts based on these components, and to compare their performance with the performance of the T ² chart on the principal components.     

多学科设计优化的数值算法及其组合算法比较的研究

针对多学科设计优化的数值算法比较研究上存在的不足,提出了算法在进行优化时所需的时间、解决问题个数及选用目标函数的相对精度等三项评估标准相结合的三维算法比较方法,首次将精度作为算法比较的一个重要指标,由此得到的算法比较三维模型,为算法选择提供了更加合理的理论依据.在理论研究的基础上,对组合算法和数值算法进行了比较,突破了传统算法比较局限在数值算法的不足.结果表明,在时间变化不大的情况下,组合算法的精度比单纯的数值算法有大幅度的提高,为工程应用提供了更全面的支持.在此基础上给出了数值算法及其组合的算法选择流程.最后,通过手机的多学科设计优化实例,验证了所提出的算法选择流程的合理性和可行性.     

Optimal shape control of functionally graded smart plates using genetic algorithms

This paper deals with optimal shape control of functionally graded smart plate containing patches of piezoelectric sensors and actuators. The genetic algorithm (GA) is designed to search for optimal actuator voltage and displacement control gains for the shape control of the functionally graded material (FGM) plates. The work extends the earlier finite element formulations of the two leading authors, so that it can be readily treated using genetic algorithms. Numerical results have been obtained to study the effect of the shape control of the FGM plates under a temperature gradient by optimising (i) the voltage distribution for the open loop control, and (ii) the displacement control gain values for the closed loop feedback control. The effect of the constituent volume fractions of zirconia, through varying the volume fraction exponent n, on the optimal voltages and gain values has also been examined.     

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。     

Feature-based design for process planning of machining processes with optimization using genetic algorithms

The development of a feature-based design environment that can be applied in the concept-to-manufacturing stages of the machining process is explained. It is broadly divided into four modules, namely, feature-based design (FBD) environment, virtual factory environment (VFE), operation-based feature mapping (OBFM) and optimization using genetic algorithms (GA). The feature-based design environment module is used for the design, modelling, synthesis, representation and validation of the components for machining application. It uses integrated features, which are predefined as feature templates in the feature library. While instancing these integrated features, they get/derive the information required for the design, modelling, process planning and manufacturing stages of the components as their attributes, from the user/knowledge base. After creating the component, integrated features present in it are validated with respect to its application, namely machining process. The VFE module defines the mathematical model of the factory in the computer, which provides the database for operations, machines, cutting tools, work pieces, etc. The knowledge base maps validated features of the component into operation sets in the first phase of the OBFM stage. Each operation in the operation sets can be carried out using different machines and cutting tools in the factory. All these possible choices are obtained in the second phase of OBFM. GA is used to find the optimal sequence of operations, machines and cutting tools for different criteria. Provisions are also available to generate NC codes for operations, which are to be carried out with NC or CNC machines, if selected. Thus, the optimal process plan for the selected criteria with respect to the given factory environment is found for the modelled component. The feature-based design system developed is built on existing CAD, programming and spread-sheet software tools, namely CATIA®, MS-Visual Basic® and MS-Excel®, which not only save developmental effort, but also make full use of the functionalities of these commercial softwares. This paper explains the developed system with a case study.     

Generating experimental designs involving control and noise variables using genetic algorithms

Efficient estimation of response variables in a process is an important problem that requires experimental designs appropriated for each specific situation. When we have a system involving control and noise variables, we are often interested in the simultaneous optimization of the prediction variance of the mean (PVM) and the prediction variance of the slope (PVS). The goal of this simultaneous optimization is to construct designs that will result in the efficient estimation of important parameters. We construct new computer‐generated designs using a desirability function by transforming PVM and PVS into one desirability value that can be optimized using a genetic algorithm. Fraction of design space (FDS) plots are used to evaluate the new designs and six cases are discussed to illustrate the procedure. Copyright © 2009 John Wiley & Sons, Ltd.     

基于改进自适应遗传算法的钢框架非线性优化设计

遗传算法在建筑钢结构设计中的应用不仅改变了传统的设计概念,而且可以丰富钢结构优化设计理论和方法,在经济建设中取得显著效益．在比较、分析、综合现有的一些改进方法的基础上,对基本遗传算法提出了一系列改进措施,形成了一种高效综合的遗传算法．改进算法采用最优保存策略和比例选择相结合的选择思路,设计了与进化代数相关的交叉概率和与个体适应度相关的变异概率．将该算法应用于以质量（造价）最小为优化目标的钢框架结构的优化设计中,结果表明,改进的遗传算法不仅可以获得满意的优化效果,而且还有效地提高了计算速度.