Finocyl Grain Design and Optimization Using Sequential Quadratic Programming

Design technique of 3D Finocyl grain configuration for Solid Rocket Motors, including its performance prediction and optimization is discussed. In doing so, the design objectives and constraints are set, geometric parameters of Finocyl grain are identified, and performance prediction parameters are calculated, thereafter the preliminary design is completed and optimal design is reached. For every grain design, it is necessary that the minimum possible mass of propellant is used to produce the required thrus...     

Reliability-Based Optimization Using Evolutionary Algorithms

Uncertainties in design variables and problem parameters are often inevitable and must be considered in an optimization task if reliable optimal solutions are sought. Besides a number of sampling techniques, there exist several mathematical approximations of a solution's reliability. These techniques are coupled in various ways with optimization in the classical reliability-based optimization field. This paper demonstrates how classical reliability-based concepts can be borrowed and modified and, with integrated single and multiobjective evolutionary algorithms, used to enhance their scope in handling uncertainties involved among decision variables and problem parameters. Three different optimization tasks are discussed in which classical reliability-based optimization procedures usually have difficulties, namely (1) reliability-based optimization problems having multiple local optima, (2) finding and revealing reliable solutions for different reliability indices simultaneously by means of a bi-criterion optimization approach, and (3) multiobjective optimization with uncertainty and specified system or component reliability values. Each of these optimization tasks is illustrated by solving a number of test problems and a well-studied automobile design problem. Results are also compared with a classical reliability-based methodology.     

组合优化多智能体进化算法

提出了一种新的组合优化方法——组合优化多智能体进化算法．该方法将智能体固定在网格上,而每个智能体为了增加自身能量将与其邻域展开竞争,同样智能体也可进行自学习来增加能量．理论分析证明算法具有全局收敛性．在实验中,作者分别用强联接、弱联接、重叠联接等各种类型的欺骗函数对算法的性能进行了全面的测试,并将算法用于解决具有树状等级结构的问题．比较结果表明文中算法所需的计算量远远小于其它方法,具有较快的收敛速度．为了测试算法解决大规模问题的能力,作者还将算法用于解决上千维的欺骗问题和等级问题,结果表明该文算法的计算复杂度与问题规模成多项式的关系．此外,将算法用于上千维的欺骗问题和等级问题,在国内外还均未见报到．     

进化设计中拓扑搜索和参数的协同优化算法

为了解决基于遗传编程(GP)的动态系统进化设计过程中拓扑和参数协同优化的问题,讨论了基于GP的进化设计种群拓扑多样性保存策略,提出了一种拓扑适应值共享-拥挤协同搜索算法。该算法避免计算小生境半径、通过自适应适应度函数来惩罚拓扑子群,保证了拓扑多样性和阻止局部收敛的发生。实验结果表明,该算法保证了动态系统进化设计中拓扑和参数同步搜索的平衡,有效地克服了局部收敛,能确保获得理想的设计结果。     

一个用于多目标优化的进化规划算法

进化计算的群体搜索机制为多目标优化问题的直接求解提供了途径。本文将多目标遗传算法中的一些技术用于进化规划,提出一个多目标进化规划算法,并给出计算实例。     

Evolutionary Optimization of Dynamics Models in Sequential Monte Carlo Target Tracking

This paper describes a new method for the online parameter optimization of various models used to represent the target dynamics in particle filters. The optimization is performed with an evolutionary strategy algorithm, by using the performance of the particle filter as a basis for the objective function. Two different approaches to forming the objective function are presented: the first assumes knowledge of the true source position during the optimization, and the second uses the position estimates from the particle filter to form an estimate of the current ground-truth data. The new algorithm has low computational complexity and is suitable for real-time implementation. A simple and intuitive real-world application of acoustic source localization and tracking is used to highlight the performance of the algorithm. Results show that the algorithm converges to an optimum tracker for any type of dynamics model that is capable of representing the target dynamics.      

一个用于多目标优化的进化规划算法

进化计算的群体搜索机制为多目标优化问题的直接求解提供了途径.本文将多目标遗传算法中的一些技术用于进化规划,提出一个多目标进化规划算法,并给出计算实例.     

基于目标分解的高维多目标并行进化优化方法

高维多目标优化问题普遍存在且难以解决, 到目前为止, 尚缺乏有效解决该问题的进化优化方法. 本文提出一种基于目标分解的高维多目标并行进化优化方法, 首先, 将高维多目标优化问题分解为若干子优化问题, 每一子优化问题除了包含原优化问题的少数目标函数之外, 还具有由其他目标函数聚合成的一个目标函数, 以降低问题求解的难度; 其次, 采用多种群并行进化算法, 求解分解后的每一子优化问题, 并在求解过程中, 充分利用其他子种群的信息, 以提高Pareto非被占优解的选择压力; 最后, 基于各子种群的非被占优解形成外部保存集, 从而得到高维多目标优化问题的Pareto 最优解集. 性能分析表明, 本文提出的方法具有较小的计算复杂度. 将所提方法应用于多个基准优化问题, 并与NSGA-II、PPD-MOEA、ε-MOEA、HypE和MSOPS等方法比较, 实验结果表明, 所提方法能够产生收敛性、分布性, 以及延展性优越的Pareto最优解集.     

优化设计中的多目标进化算法

近十多年来多目标进化算法是人工智能领域的一个相当活跃的研究热点。该文从非Pareto方法、基于Pareto方法及贝叶斯多目标优化算法等角度对当今多目标进化算法进行了分析,归纳了新出现的各种方法和技术,探讨了这个领域发展中存在的问题,并进一步给出了发展方向。此外文中分别对后两类提出了解决一般问题的计算效果较好的改进算法和新的算法。     

基于进化计算的模糊控制规则优化

在改进的遗传算法基础上，提出了模糊控制规则的一种优化方法，模糊规则的优化基于类似最优控制的二次型性能指标，不需要任何被控系统的先验知识，因而适合于一大类复杂非线性系统的控制，仿真结果表明，这种方法是有效的。     

利用Visual C++开发系统进程管理器

介绍了利用Visual C 系统快照实现对系统当前进程的枚举,并根据枚举结果对进程进行管理.     

Design Optimization of Multibody Systems by Sequential Approximation

Design optimization of multibody systems is usually established by a direct coupling of multibody system analysis and mathematical programming algorithms. However, a direct coupling is hindered by the transient and computationally complex behavior of many multibody systems. In structural optimization often approximation concepts are used instead to interface numerical analysis and optimization. This paper shows that such an approach is valuable for the optimization of multibody systems as well. A design optimization tool has been developed for multibody systems that generates a sequence of approximate optimization problems. The approach is illustrated by three examples: an impact absorber, a slider-crank mechanism, and a stress-constrained four-bar mechanism. Furthermore, the consequences for an accurate and efficient accompanying design sensitivity analysis are discussed.     

计算机辅助橡塑工艺设计系统的优化研究

信息技术在制造业中的广泛应用,使企业进入了一个信息化跨越发展的时代.利用信息化手段高效管理大量的工艺文件和工艺数据,实现企业的信息系统数据资源共享等问题也越来越受到重视.文章通过分析橡塑制品企业使用的计算机辅助工艺设计系统在使用过程中的一些具体问题,结合企业对橡塑工艺辅助设计系统的基本要求,进行详细的系统需求分析,设计系统构架.通过对企业引进的CAPP软件和AutoCAD软件的分析,结合橡塑工艺的特点,依托AutoCAD图形平台,对AutoCAD下VBA宏语言二次开发,实现了橡塑工艺的辅助设计系统软件的开发优化及对工艺知识的有效管理.     

Evolutionary Algorithms and Theirs Use in the Design of Sequential Logic Circuits

In this paper an approach based on an evolutionary algorithm to design synchronous sequential logic circuits with minimum number of logic gates is suggested. The proposed method consists of four main stages. The first stage is concerned with the use of genetic algorithms (GA) for the state assignment problem to compute optimal binary codes for each symbolic state and construct the state transition table of finite state machine (FSM). The second stage defines the subcircuits required to achieve the desired functionality. The third stage evaluates the subcircuits using extrinsic Evolvable Hardware (EHW). During the fourth stage, the final circuit is assembled. The obtained results compare favourably against those produced by manual methods and other methods based on heuristic techniques.     

邯钢冷轧厂精整线计算机过程监控系统的设计与实现

详细介绍了使用美国Rockwell公司PLC组成的一条横切生产线和一条亘卷生产线。该系统具有三层网络，上层为以太网实现各生产线间的数据交换；中间控制网负责PLC系统通信；底层设备网或远程I／O；计算机系统采用客户／服务器结构，为工艺过程优化，分析各种操作，预防各种事故的发生打下了良好的基础。该系统整体设计程序严谨、条理清晰，将分散于现场的检测单元，操作站和上位机有机的结合、协调控制，具有动态性好，跟随性强等特点。此系统提高了产品质量，降低了生产成本，增强了企业的竞争力。     

冷连轧机轧制过程数据采集系统

主要介绍了冷连轧机轧制过程数据采集系统的结构设计和功能设计。系统通过RDA和RTSI方式解决了远程数据采集。多卡同步采集和采集设备的自诊断等问题，现场应用表明，该系统能按所要求的采集频率和采集精度实现数据采集，并可以完成数据的提交和浏览等功能，实现了数量巨大的数据的高速采集，存储和管理。     

A Drug Candidate Design Environment Using Evolutionary Computation

This paper describes the candidate design environment we developed for efficient identification of promising drug candidates. Developing effective drugs from active molecules is a challenging problem which requires the simultaneous satisfaction of many factors. Traditionally, the drug discovery process is conducted by medicinal chemists whose vital expertise is not readily quantifiable. Recently, in silico modeling and virtual screening have been emerging as valuable tools despite their mixed results early on. Our approach combines the capabilities of computational models with human knowledge using a genetic algorithm and interactive evolutionary computation. We enable the chemist's expertise to play a key role in every stage of the discovery process. Our evolved structures are guaranteed to be within the chemistry space specified by the medicinal chemist, thereby making the results plausible. In this paper, we describe our approach, introduce a case study to test our methodology, and present our results.     

Quad-Rotor Directional Steering System Controller Design Using Gravitational Search Optimization

Directional Steering System (DSS) has been established for well drilling in the oilfield in order to accomplish high reservoir productivity and to improve accessibility of oil reservoirs in complex locations. In this paper, a novel feedback linearization controller to cancel the nonlinear dynamics of a DSS is proposed. The proposed controller design problem is formulated as an optimization problem for optimal settings of the controller feedback gains. Gravitational Search Algorithm (GSA) is developed to search for optimal settings of the proposed controller. The objective function considered is to minimize the tracking error and drilling efforts. In this study, the DSS considered has 4 downhole motors. The robustness of the proposed GSA-based approach for the controller design is demonstrated. The simulation results of the considered 4-rotor DSS is presented and the effectiveness of the proposed controller is confirmed.     

Clearance of Nonlinear Flight Control Laws Using Hybrid Evolutionary Optimization

The application of two evolutionary optimization methods, namely, differential evolution and genetic algorithms, to the clearance of nonlinear flight control laws for highly augmented aircraft is described. The algorithms are applied to the problem of evaluating a nonlinear handling quality clearance criterion for a simulation model of a high-performance aircraft with a delta canard configuration and a full-authority flight control law. Hybrid versions of both algorithms, incorporating local gradient-based optimization, are also developed and evaluated. Statistical comparisons of computational cost and global convergence properties reveal the benefits of hybridization for both algorithms. The differential evolution approach in particular, when appropriately augmented with local optimization methods, is shown to have significant potential for improving both the reliability and efficiency of the current industrial flight clearance process     

采用不可微精确罚函数的约束优化演化算法

针对多数已有的采用罚函数的约束优化遗传算法存在优化效果差的问题 ,提出了一种新的求解约束优化问题的演化算法 .借助不可微精确罚函数把约束问题转化为单个无约束问题来处理 .采用混合杂交和间歇变异来提高算法的搜索能力 .数值实验结果表明了新算法的优化效果远远优于已有的几种采用罚函数的遗传算法