An efficient multiobjective differential evolution algorithm for engineering design

Solving engineering design and resources optimization via multiobjective evolutionary algorithms (MOEAs) has attracted much attention in the last few years. In this paper, an efficient multiobjective differential evolution algorithm is presented for engineering design. Our proposed approach adopts the orthogonal design method with quantization technique to generate the initial archive and evolutionary population. An archive (or secondary population) is employed to keep the nondominated solutions found and it is updated by a new relaxed form of Pareto dominance, called Pareto-adaptive ϵ-dominance (paϵ-dominance), at each generation. In addition, in order to guarantee to be the best performance produced, we propose a new hybrid selection mechanism to allow the archive solutions to take part in the generating process. To handle the constraints, a new constraint-handling method is employed, which does not need any parameters to be tuned for constraint handling. The proposed approach is tested on seven benchmark constrained problems to illustrate the capabilities of the algorithm in handling mathematically complex problems. Furthermore, four well-studied engineering design optimization problems are solved to illustrate the efficiency and applicability of the algorithm for multiobjective design optimization. Compared with Nondominated Sorting Genetic Algorithm II, one of the best MOEAs available at present, the results demonstrate that our approach is found to be statistically competitive. Moreover, the proposed approach is very efficient and is capable of yielding a wide spread of solutions with good coverage and convergence to true Pareto-optimal fronts.     

Topology optimization of structures with integral compliant mechanisms for mid-frequency response

The purpose of this paper is to present a method for developing new truss-like sandwich structures that exhibit desirable mid-frequency vibratory characteristics. Specifically, a genetic algorithm optimization routine is used to determine candidate small scale structural topologies, i.e. unit cells, that may be used in the design of larger scale periodic sandwich structures. This multi-scale procedure is demonstrated starting with several unit cell topology optimization examples. From these examples a specific optimal unit cell is selected for further investigation and integration into a periodic sandwich beam. Computational results indicate that the proposed optimization approach is effective when used to design new structures for reduced mid-frequency vibratory response.     

抗量子计算公钥密码需求分析与技术路线

本文对国外量子计算机研究进展情况进行了分析,并针对量子计算机对于信息安全的挑战,讨论了国内下一代公钥密码技术的需求、发展思路和技术路线。     

图像边缘检测在DM642上的应用研究

采用TMS320DM642EVM作为数字图像处理硬件平台,利用CCS开发环境来构建程序并对其进行优化;通过对改进后的Log算子和小波多尺度方法提取到的边缘图像进行数据融合,得到一幅新的边缘图像。实验结果表明,该方法提高了边缘检测的运算速度,满足了实时性要求;并能有效地抑制噪声的影响,保留了图像边缘细节。     

智能交通系统中的最短路径算法分析

智能交通涉及到交通领域的多个方面,如何寻找最短路径是其核心问题之一。文章着重讨论了智能交通系统功能实现中的关键技术一求解最短路径。对于国内外一些求解最短路径的经典算法的复杂度问题进行了分析。     

一种货运列车编组调度模型及算法研究

根据货运列车编组调度的基本要求,建立了配流模型。利用编组场待编组车节信息,结合到达场列车的实时信息,设计了信息反馈模型。根据配流模型和反馈模型,对列车进行解体和组编,考虑了军需物资和灾需物资的车辆信息,通过调整模型参数,使得模型继续适用那些由于铁路中断,需要绕行的货运列车。改变了传统算法只考虑局部车组信息,而忽视全局车组信息的弊端。因此,该模型具有灵活性。研究了基本的编组调度算法,使用贪心算法和动态规划算法相结合来求解,并分析了该算法的可行性。     

ID3算法及其改进

文章对ID3算法的基本概念和原理进行了相应的详细阐述以及解释说明,并针对ID3算法倾向于取值较多的属性的缺点,引进信息增益率对ID3算法作了改进,并通过实验对改进前后的算法进行了比较,实验表明,改进后的算法行之有效。     

一种基于完全独立集的排课算法

班级、教师、课程、教室、时间是课表的基本要素,通过把排课转化为求图的完全独立集,解决了时间冲突问题和合班课问题;根据教室资源限制,对独立集的个数和大小进行控制,解决了教室完全匹配问题和一门课程的不同教室安排问题.为实现排课软件提供了一个面向实际的算法.     

Genetic based sensorless hybrid intelligent controller for strip loop formation control between inter-stands in hot steel rolling mills

Safe operating environment is essential for all complex industrial processes. The safety issues in steel rolling mill when the hot strip passes through consecutive mill stands have been considered in this paper. Formation of sag in strip is a common problem in the rolling process. The excessive sag can lead to scrap runs and damage to machinery. Conventional controllers for mill actuation system are based on a rolling model. The factors like rise in temperature, aging, wear and tear are not taken into account while designing a conventional controller. Therefore, the conventional controller cannot yield a requisite controlled output. In this paper, a new Genetic-neuro-fuzzy hybrid controller without tension sensor has been proposed to optimize the quantum of excessive sag and reduce it. The performance of the proposed controller has been compared with the performance of fuzzy logic controller, Neuro-fuzzy controller and conventional controller with the help of data collected from the plant. The simulation results depict that the proposed controller has superior performance than the other controllers.     

Controller certification

A modern problem from aerospace control involves the certification of a large set of potential controllers with either a single plant or a fleet of potential plant systems, with both plants and controllers being MIMO and, for the moment, linear. Experiments on a limited number of controller/plant pairs should establish the stability and a certain level of margin of the complete set. We consider this certification problem for a set of controllers and provide algorithms for selecting an efficient subset for testing. This is done for a finite set of candidate controllers and, at least for SISO plants, for compact infinite set. In doing this, the ν-gap metric will be the main tool. Computational examples are given, including one of certification of an aircraft engine controller. The overarching aim is to introduce truly MIMO margin calculations and to understand their efficacy in certifying stability over a set of controllers and in replacing legacy single-loop gain and phase margin calculations.