模糊关系方程的解的模糊遗传导优算法

在一般遗传算法ＧＡ的基础上，基于模糊集理论中的模糊关系方程的解的寻优问题提出了模糊遗传算法ＦＧＡ，它能有效地找出模糊关系方程的解的寻优问题的近似最优解。美学 给出了一个重要的定理：模糊模式定理。     

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。     

混合单调算子方程的解及其对非线性积分方程的应用

证明了有关混合单调算子方程解的几十存在定理，并将所得结果满意地直用于非线性mrnmerstein型积分方程和Voltem型积分方程的遗代求解。     

一类二阶非线性差分方程的多重周期解

本文研究了一类二阶非自治非线性差分方程多重周期解的存在性问题.将这类方程的周期解转化为定义在一个适当空间上泛函的临界点,利用变分原理和Clark定理,得到了此类方程周期解个数的下界估计.     

基于改进免疫遗传优化蚁群算法的移动机器人路径寻优研究

针对移动机器人路径规划中使用蚁群算法(ACO)易陷入局部最优和收敛速度慢的问题,提出了一种适用于机器人静态路径寻优的改进免疫遗传优化蚁群算法(IMGAC)。该算法可以根据实际情况自动调整变异概率和变异方式,以及自动调节个体免疫位的长度,将通过改进的变异算子和免疫算子嵌入蚁群算法来提高全局寻优能力与收敛速度。仿真及实验表明:相比于经典ACO算法以及最大最小蚂蚁系统,IMGAC算法收敛速度更快,全局寻优能力更强。利用该算法寻找移动机器人最优路径,提高了静态路径寻优的效果和效率。     

完备Brouwerlan格上有限@-Fuzzy关系方程有解的一个充要条件

本文在论域为有限集时讨论了完备Brouwenan格上有限@-Fuzzy关系方程（其中@表示inf-α合成），给出了@-Fuzzy关系方程有解的一个充要条件并构造出了方程的极大解，进一步确定了方程的解集。     

模糊偏序关系模型的信息融合方法

在偏序集上给出模糊偏序关系模型，讨论了各种评估决策与模糊偏序关系模型之间的关系，给出了模糊偏序关系模型的信息融合方法，并指出该方法符合公共选择中阿罗不可能性定理的一致性原则。     

关于Liénard方程周期解的不稳定性

本文主要研究了一类特殊类型的Liénard方程.在恢复力为线性,摩擦项定号的前提下,我们采用体隐函数定理的方法给出了这类方程周期解的存在唯一性.并采用线性化方法.通过排除方程相应的线性化方程的2阶次调和解的存在性,确保这类方程不会产生倍周期分歧,借助于同伦的方法来判别何时其线性化方程的Floquet乘子有一个位于单位圆外,进而给出该方程的不稳定性.     

弹性介质模糊有限元控制方程的快速解法

线弹性模糊有限元方法是分析弹性介质体模糊特性对结构响应产生不确定性影响的有效方法。即使对弹性介质体而言,模糊有限元控制方程的求解时间问题也是困扰其推广应用的主要障碍。为获得可靠可行的模糊有限元控制方程的快速求解方法,在深入研究弹性介质体的模糊源特点基础上,提出当引起结构模糊特性的力学参数为单源模糊数时,可以利用单源模糊数的运算特点来求解模糊有限元的控制方程,进而利用合成运算求解结构的模糊位移和模糊应力的分布。推导了基于单源模糊数运算的弹性介质模糊应力和模糊位移的计算表达式。应用模糊有限元求解的区间解法和快速解法对算例进行比较分析,结果表明了快速解法的正确性。     

时滞Liénard型方程的周期解

利用重合度理论讨论一类时滞Lienard型方程的周期解的存在性,得到了简便的判别条件。     

糊糊限定微分方程及定解问题

模糊微分方程是指未知模糊值函数及模糊值导数与已知模糊值函数（或已知模糊数）间的条件等式。由于模糊数和模糊值函数关于加减法运算不存在逆关系,致使模糊微分方程的求解远远比普通微分方程求解困难得多,目前已有一些研究成果,但是,他们无法被应用到采场渗流等实际问题之中。文中提出了模糊限定微分方程的概念,它与模糊微分方程不同的是,方程的定义是利用扩张原理的形式给出的。模糊限定微分方程具有广泛的工程应用背景。同时,也得到了模糊限定微分方程的具体求解方法以及模糊限定微分方程解的可表示性的判定方法。     

A Genetic Algorithm Hybrid for Constructing Optimal Response Surface Designs

Hybrid heuristic optimization methods can discover efficient experiment designs in situations where traditional designs cannot be applied, exchange methods are ineffective, and simple heuristics like simulated annealing fail to find good solutions. One such heuristic hybrid is GASA (genetic algorithm–simulated annealing), developed to take advantage of the exploratory power of the genetic algorithm, while utilizing the local optimum exploitive properties of simulated annealing. The successful application of this method is demonstrated in a difficult design problem with multiple optimization criteria in an irregularly shaped design region. Copyright © 2004 John Wiley & Sons, Ltd.     

Hybrid Approach to Optimal Packing Using Genetic Algorithm and Coulomb Potential Algorithm

It is difficult and computationally time-consuming to find the best possible solutions for blank packing problems, because they include a lot of underlying combinational conditions. This paper presents two approaches for packing two-dimensional irregular-shaped polygonal elements—a real-encoded genetic algorithm and a hybrid algorithm using a real-encoded genetic algorithm and a local optimization algorithm. The local optimization algorithm presented is a novel one utilizing the Coulomb potential technique.

In the hybrid approach, the real-encoded genetic algorithm generates the order of the polygons while the coulomb potential algorithm determines the embodiment layout under the fixed combinations so as to minimize the scrap. The hybrid genetic algorithm is found to give better results for problems of larger size although it takes more computational time.     

Solving Job-Shop Scheduling Problems by Genetic Algorithms Based on Building Block Hypothesis

In this paper, we propose a new genetic algorithm for job-shop scheduling problems （JSP）. The proposed method uses the operation-based representation, based on schema theorem and building block hypothesis, a new crossover is proposed ： By selecting short, low order highly fit schemas to genetic operator, the crossover can exchange meaningful ordering information of parents effectively and can search the global optimization. Simulation results on MT benchmark problem coded by C ＋ ＋ show that our genetic operators are very powerful and suitable to job-shop scheduling problems and our method outperforms the previous GA-based approaches.     

基于模糊逻辑控制器的自适应遗传算法

针对遗传算法中交叉概率和变异概率的缺陷，设计了一种基于模糊逻辑控制器的自适应遗传算法，实验结果表明，该算法具有较好的在线性能。     

遗传算法的改进及其在水库优化调度中的应用研究

遗传算法是通过对样本中个体的不断改进来寻找各类问题的最优解。由于标准遗传算法（SGA）存在收敛性及个体适应度求解方面的困难，在研究中，通过对SGA中遗传算子改进，特别是对选择算子的改进，提出了一种改进遗传算法（AGA），并将它应用于水库优化调度中。改变通常以水位变化序列为基础的遗传算法编码方案，通过数组存储水库库容状态，并以各库容状态对应的数组下标为基础进行遗传算法编码，通过实例，表明AGA对水库优化调度问题具有良好的适应性，同时结合数组存储理论的遗传算法编码方法简化了水库优化调度遗传算法的实现过程。     

Optimal Building Frame Column Design Based on the Genetic Algorithm

Building structure is like the skeleton of the building, it bears the effects of various forces and forms a supporting system, which is the material basis on which the building depends. Hence building structure design is a vital part in architecture design, architects often explore novel applications of their technologies for building structure innovation. However, such searches relied on experiences, expertise or gut feeling. In this paper, a new design method for the optimal building frame column design based on the genetic algorithm is proposed. First of all, in order to construct the optimal model of the building frame column, building units are divided into three categories in general: building bottom, main building and building roof. Secondly, the genetic algorithm is introduced to optimize the building frame column. In the meantime, a PGA-Skeleton based concurrent genetic algorithm design plan is proposed to improve the optimization efficiency of the genetic algorithm. Finally, effectiveness of the mentioned algorithm is verified through the simulation experiment.     

A Nontrivial Solution to a Stochastic Matrix Equation

If A is a nonsingular matrix such that its inverse is a stochastic matrix, the classic Brouwer fixed point theorem implies that the matrix equation AXA = XAX has a nontrivial solution. An explicit expression of this nontrivial solution is found via the mean ergodic theorem, and fixed point iteration is considered to find a nontrivial solution.     

基于遗传算法的半主动分支电路的优化设计

应用遗传算法,对采用半主动分支电路控制悬臂梁振动的结构优化设计问题做了研究.考虑到在半主动分支电路振动控制中,所选电器元件的参数对振动控制的效果至关重要,重点对其做优化设计.结果对进一步研究半主动分支电路有一定的参考价值.