Optimizing Concurrent Product Development Process Using Genetic Algorithm

0　IntroductionTimesaving,timetomarketandcycletimereductionarethenewgoalsandaimsforenterprisesinordertosuccessfullycompeteintothemodernglobalmarket[1].Thereisnodoubtthatconcurrentconsiderationofproductandprocessdesignimprovesproductqualityandreducesthere-designwork,whichleadstoshorterproductlead-time.Buttheconcurrentdevelopmentfashionbringsaboutmoreobstaclessuchascomplextimeorder,informationandresourceconstrainednatureofrelationshipsbetweenactivitiestothedesignprocess,whichleadtothefactthatiti…     

非满载车辆调度问题的遗传算法

非满载车辆调度问题是车辆调度问题中的一个基本问题,由于它是一个典型的NP难题,传统方法的求解结果往往不能令人满意。本文采用最大保留交叉、交叉率和变异率自适应变换等技术,设计了基于自然数编码的遗传算法,用于求解非满载车辆调度问题,在实验分析中获得了较好的结果。     

时滞多变量时变系统的最优控制混合仿真研究

应用最优控制理论的极小值原理和参数优化理论 ,叙述了具有常数时滞的多变量时变系统的最优控制算法 ,基于该算法 ,提出了这类系统的数模混合仿真方法和实践方法。混合结果表明 :提出的算法是收敛的 ,且收敛速度优于全数字仿真结果。     

面向能耗的云工作流调度优化

云工作流调度直接决定了整个云工作流系统的性能,已成为一个重要研究内容.针对当前缺乏有效的面向能耗的云工作流调度优化方法,研究建立了面向能耗的云工作流过程模型、资源模型,提出了基于负载的能耗计算方法和面向能耗的云工作流调度优化算法.提出的方法考虑了能耗因素,在进行任务优先级计算及任务选择时不仅考虑了文件在不同虚拟机间传输的速度差异、同时考虑了虚拟机与本地共享数据库间的文件传输等因素,并从云工作流任务分配、主机负载和主机功耗关系的角度,进行能耗的计算与调度优化,更符合实际情况、使用范围更广.数值案例和仿真实验表明了提出的方法是可行的和有效的.     

Margin optimization algorithm for digital subscriber lines based on particle swarm optimization*

The margin maximization problem in digital subscriber line (DSL) systems is investigated. The particle swarm optimization (PSO) theory is applied to the nonconvex margin optimization problem with the target power and rate constraints. PSO is a new evolution algorithm based on the social behavior of swarms, which can solve discontinuous, nonconvex and nonlinear problems efficiently. The proposed algorithm can converge to the global optimal solution, and numerical example demonstrates that the proposed algorithm can guarantee the fast convergence within a few iterations.     

求解0-1动态优化问题的双概率原对偶遗传算法

在原对偶遗传算法(PDGA)的基础上,提出一种双概率原对偶遗传算法(DPPDGA).引入弱势基因位值与强势基因位值的概念,对二者赋予不同的对偶映射概率,并对两个对偶概率进行适应性调整.比较原始算法,改进算法使种群具有更理想的多样性,并利于种群较快地收敛到满意解.仿真结果表明,该算法在0-1动态优化问题的求解中具有更好的性能.     

基于递阶遗传算法的一类多旅行商问题优化

针对最小化单个旅行商路程的多旅行商问题,提出了一种递阶遗传算法和矩阵解码方法。该算法根据问题的特点,采用一种递阶编码方案,此编码与多旅行商问题一一对应。用递阶遗传算法优化多旅行商问题不需设计专门的遗传算子,操作简单,并且解码方法适于求解距离对称和距离非对称的多旅行商问题。计算结果表明,递阶遗传算法是有效的,能适用于优化多旅行商问题。     

模糊粗糙神经网络的结构与参数优化

基于模糊粗糙隶属函数,建立了一种五层结构的模糊粗糙神经网络（fuzzy rough neural network, FRNN）,对神经元之间的连接,引入一个开关函数,从而把结构优化和参数学习问题转化为单纯的函数优化问题。提出一种混合智能优化算法（hybrid intelligent optimization algorithm, HIOA）用于FRNN的结构和参数优化,适应度函数同时考虑模型的精确性和网络的节俭性。典型的实验结果表明,FRNN适用非线性系统建模,相对于普通神经网络及其优化方法能获得更高的精度和泛化能力。     

Study on the extending multi-attribute decision model of grey target

Based on the distance of interval numbers and the two-stage decision methods, this paper expands the decision model of grey target into some situation under which the decision information and target weights are the interval numbers at the same time. It also gives the optimization method of weights in the grey target. We get the optimum coordinated vector utilizing the combination assigning method, based on the local optimization of various schemes. So it can shift the weights of interval number into real number form and sequence it according to the weighted off-target distance. Finally the effectiveness and practicality of the model is proved by a real project.     

Robust H2 control for uncertain sampled-data systems

A new approach is proposed for robust H2 problem of uncertain sampled-data systems. Through introducing a free variable, a new Lyapunov asymptotical stability criterion with less conservativeness is established. Based on this criterion, some sufficient conditions on two classes of robust H2 problems for uncertain sampled-data control systems are presented through a set of coupled linear matrix inequalities, Finally, the less conservatism and potential of the developed results are illustrated via a numerical example.