基于遗传算法和满意度函数的武器系统维修性分配规划问题研究

武器系统维修性分配是一个复杂的多目标规划问题.常规求解方法得到的结果难以满足其约束条件,并且难以体现不同目标间重要性的差别.据此,通过引入遗传算法(GA)和满意度函数解决了上述问题.首先利用GA求解单目标的最优解,建立各目标的满意度函数并综合为一个总满意度函数,最后利用GA求总满意度的最大值,即可获得模型的满意解.仿真算例表明,GA算法可适用于解决武器系统维修性分配问题,同时多目标规划中采用满意度函数法比线性加权法更可行.     

Flying-V型仓储布局货位分配优化方法研究

Flying-V是一种典型的非传统布局方式,根据其布局方式的特性,针对仓储货位分配优化问题,以货物出入库效率最高和货物存放的重心最低为优化目标,建立了货位分配多目标优化模型,并采用自适应策略的遗传算法(GA),以及粒子群算法(PSO)进行求解。根据货位分配的优化特点,在GA算法的选择、交叉和变异环节均采用自适应策略, 同时采用惯性权重线性递减的方法设计了PSO算法,有效地解决了两种算法收敛速度慢和易“早熟”的问题,提高了算法的寻优性能。为了更好地表现两种优化求解算法的有效性和优越性,结合具体的货位分配实例利用MATLAB软件编程实现。通过对比分析优化结果表明,PSO算法在收敛速度和优化效果方面相比于自适应GA算法更具有优势,更加合适于解决Flying-V型仓储布局货位分配优化问题。     

线性引力规范理论中时空的度规与联络的关系

本文系统地研究了GA(4,R)群的引力规范理论中时空的联络系数、度规张量及规范势之间的一般关系,推出了公式g_(μv;λ)=-φ_λg_(μv)-φ_(λμv),说明了在一般情形下GA(4,R)规范群所对应的时空不仅有挠而且具有Weyl向量场和变形张量场。     

关于GA凸函数差值估计的一个注记

利用闭区间上任意点处函数值与端点处单侧导数的关系,给出由GA凸函数的Hermite-Hadamard型不等式的右边部分生成的差值的估计,改进了相关的结果.     

模糊关系几何规划

提出一类目标函数为正项式,约束是模糊关系方程的优化问题。阐述模糊关系方程解集的结构以及求解的方法,基于目标函数中每个单项式的指数取值情况讨论最优解,并且给出解决此类优化问题的一个程序,为了说明该方法的有效性给出具体例子。     

基于GA优化BP网络输入的人力资本预测研究

人力资本是决定地区经济增长的重要因素之一,其形成受多种因素的综合影响。利用BP神经网络进行预测时,因人力资本形成因素众多,各因素之间信息独立性差,交叉重叠的信息使得神经网络的结构过于庞大,极大地影响了网络的性能。本文采用遗传算法(GA)对人力资本形成因素进行优化,化选出与人力资本关系最密切、反应最敏感的少数指标,再利用优化后的指标对我国31个地区的人力资本进行预测,有效地降低了网络输入维数,提高了网络精度。     

心理契约对目标绩效的影响研究:以工作满意为中介变量

构建了施工项目团队成员心理契约对目标绩效影响的结构模型,用交易型、关系型、管理型心理契约来衡量施工项目团队成员心理契约,用任务绩效、关系绩效来衡量目标绩效,把工作满意作为中介变量,运用结构方程模型的方法,分析结果表明:施工项目团队成员心理契约由交易型、关系型和管理型心理契约构成;工作满意对施工项目团队成员心理契约(交易型、关系型、管理型)满足程度与施工项目关系绩效的关系具有完全中介作用等结论。     

藤黄酸诱导肺癌细胞H1975凋亡的机制研究

目的 研究藤黄酸（GA）诱导人肺癌细胞H1975凋亡的分子机制,探讨氧自由基（ROS）和JNK信号通路在GA杀伤 肺癌细胞中的作用。方法 以人肺癌细胞H1975为研究对象,MTT法测定GA抑制细胞增殖的作用,Annexin V/PI 双染法测定细胞凋亡率,DCFH-DA 法测定ROS 含量,JC-1探针染色分析线粒体膜电位（MMP）,Western blot检测JNK 信号通路的激活和线粒体凋亡途径相关蛋白表达的变化。结果 GA 呈剂量依赖性抑制H1975细胞的增殖,各实验组细胞存活率与空白对照组比较,均有统计学差异（P＜0.05 或0.01）。1、2.5 和5滋mol/L GA 作用24h 后,细胞凋亡率分别为25.2%、51.8%和75.1%,剪切型凋亡相关蛋白cleaved caspase-9、cleaved caspase-3 和cleaved PARP 的表达随GA 浓度增高而显著增加,与空白对照组比较,均有统计学差异（P＜0.05或0.01）。GA 作用2h 后H1975细胞ROS 含量显著升高,磷酸化JNK（p-JNK）表达上调（P＜0.05或0.01）。GA 作用16h后各实验组细胞MMP 均明显降低（均P＜0.05）。GA 作用24h后实验组细胞线粒体凋亡途径相关蛋白Bax、Bak、Bik表达增加,而抗凋亡蛋白Bcl-2表达与空白对照组相比明显下降（P＜0.05 或0.01）。结论 GA 具有诱导H1975 细胞凋亡的作用,其可能机制是上调细胞内ROS含量,激活JNK 信号通路,进而引起MMP 降低和线粒体凋亡途径激活。     

取大乘积型模糊关系几何规划

本文提出了一类目标函数为正项式,约束是取大乘积型模糊关系方程的优化 问题,我们在本文中阐述了取大乘积型模糊关系方程解的结构以及求解的方法,基于目标 函数中每个单项式的指数取值情况讨论了最优解,并且给出了解决此类优化问题的一个程 序,为了说明该方法的有效性给出了两个具体例子．     

基于改进的思维进化算法与BP神经网络的AQI预测

思维进化算法(MEA)的趋同和异化操作带有太多的随机性,公告板的信息不能得到充分利用,使得效率下降,出现重复搜索.为了避免MEA算法的这个缺点,借鉴遗传算法(GA)和粒子群算法(PSO)的优点,提出改进的思维进化算法(MEA—PSO—GA).利用MEA—PSO—GA算法优化BP神经网络的初始权值和阈值进而预测太原的日常空气质量指数(AQI).通过与MEA—BP算法,MEA—PSO—BP算法和MEA—GA—BP算法比较,实验结果表明,提出的MEA—PSO—GA—BP算法在预测精度、误差率和可靠性方面搜索速度更优,更易于实现AQI预测,具有较好的有效性和可行性,有一定的现实意义.     

Using support vector machines to learn the efficient set in multiple objective discrete optimization

We propose using support vector machines (SVMs) to learn the efficient set in multiple objective discrete optimization (MODO). We conjecture that a surface generated by SVM could provide a good approximation of the efficient set. As one way of testing this idea, we embed the SVM-approximated efficient set information into a Genetic Algorithm (GA). This is accomplished by using a SVM-based fitness function that guides the GA search. We implement our SVM-guided GA on the multiple objective knapsack and assignment problems. We observe that using SVM improves the performance of the GA compared to a benchmark distance based fitness function and may provide competitive results.     

A fuzzy representation of data for relational databases

A structure for representing inexact information in the form of a relational database is presented. The structure differs from ordinary relational databases in two important respects: Components of tuples need not be single values and a similarity relation is required for each domain set of the database. Two critical properties possessed by ordinary relational databases are proven to exist in the fuzzy relational structure. These properties are (1) no two tuples have identical interpretations, and (2) each relational operation has a unique result.     

Integration of the grey relational analysis with genetic algorithm for software effort estimation

Accurate estimates of efforts in software development are necessary in project management practices. Project managers or domain experts usually conduct software effort estimation using their experience; hence, subjective or implicit estimates occur frequently. As most software projects have incomplete information and uncertain relations between effort drivers and the required development effort, the grey relational analysis (GRA) method has been applied in building a formal software effort estimation model for this study. The GRA in the grey system theory is a problem-solving method that is used when dealing with similarity measures of complex relations. This paper examines the potentials of the software effort estimation model by integrating a genetic algorithm (GA) to the GRA. The GA method is adopted to find the best fit of weights for each software effort driver in the similarity measures. Experimental results show that the software effort estimation using an integration of the GRA with GA method presents more precise estimates over the results using the case-based reasoning (CBR), classification and regression trees (CART), and artificial neural networks (ANN) methods.     

Registrar: a complete-memory operator to enhance performance of genetic algorithms

We examine the concept of storing all evaluated chromosomes and directly reuse them in Genetic Algorithms (GAs). This is achieved by a fully encapsulated operator, called Registrar, which is effortlessly placed between the GA and the objective function. The Registrar does not approximate the objective function. Instead, it replaces the chromosomes requested by the GA with similar ones taken from the registry, bypassing the function evaluation. Unlike other methods that use external memory to increase genetic diversity, our simple implementation encourages revisits in order to avoid evaluations in an aggressive manner. Significant increase in performance is observed which is present even at the early stages of evolution, in accordance with the Birthday Problem of probability theory. Implementation with Standard GA shows great promise, while the encapsulation of the code facilitates implementation with other Evolutionary Algorithms.     

Heuristic solution approaches for the cumulative capacitated vehicle routing problem

Cumulative capacitated vehicle routing problem (CCVRP) is an extension of the well-known capacitated vehicle routing problem, where the objective is minimization of sum of the arrival times at nodes instead of minimizing the total tour cost. This type of routing problem arises when a priority is given to customer needs or dispatching vital goods supply after a natural disaster. This paper focuses on comparing the performances of neighbourhood and population-based approaches for the new problem CCVRP. Genetic algorithm (GA), an evolutionary algorithm using particle swarm optimization mechanism with GA operators, and tabu search (TS) are compared in terms of required CPU time and obtained objective values. In addition, a nearest neighbourhood-based initial solution technique is also proposed within the paper. To the best of authors’ knowledge, this paper constitutes a base for comparisons along with GA, and TS for further possible publications on the new problem CCVRP.     

主客观组合赋权的灰色决策模型及其在消费者决策中的运用

现实中许多抽象系统的数值并不能精确地得到,在多属性决策过程中,其属性值多以模糊数或者区间灰数的形式表示,属性权重也受到主观因素和客观因素的影响,针对此类问题,以属性值为区间灰数的多属性决策问题为出发点,采用主客观结合的赋权方式,提出了基于区间距离的灰色相对关联度分析方法,构建了灰色决策模型,并通过消费者决策实例进行了分析,模型是对传统灰色决策模型的拓展,具有更广泛的应用性和实用性.     

Genetic algorithmic approach to find the maximum weight independent set of a graph

In this paper, Genetic Algorithm (GA) is used to find the Maximum Weight Independent Set (MWIS) of a graph. First, MWIS problem is formulated as a 0-1 integer programming optimization problem with linear objective function and a single quadratic constraint. Then GA is implemented with the help of this formulation. Since GA is a heuristic search method, exact solution is not reached in every run. Though the suboptimal solution obtained is very near to the exact one. Computational result comprising an average performance is also presented here.     

Entorno geométrico eficiente para el modelado de imágenes en 3D

The objective of this paper is exploring implementation of a realistic images reconstruction 3D using geometric algebra (GA). We illustrate the suitability of GA for representing structures and developing algorithms in computer graphics, especially for engineering applications as 3D images modeling. A first consequence is to propose an efficient framework model to be implemented in hardware programmable. The obtained results showed that using GA, the computations are less complex and shows as simple computations geometrical operations. The obtained model to hardware can be implemented as a next step in 3D image reconstruction. We also include the potential of GA for optimizations and highly efficient implementations.     

A hybrid genetic algorithm–neural network strategy for simulation optimization

Simulation optimization aims at determining the best values of input parameters, while the analytical objective function and constraints are not explicitly known in terms of design variables and their values only can be estimated by complicated analysis or time-consuming simulation. In this paper, a hybrid genetic algorithm–neural network strategy (GA–NN) is proposed for such kind of optimization problems. The good approximation performance of neural network (NN) and the effective and robust evolutionary searching ability of genetic algorithm (GA) are applied in hybrid sense, where NNs are employed in predicting the objective value, and GA is adopted in searching optimal designs based on the predicted fitness values. Numerical simulation results and comparisons based on a well-known pressure vessel design problem demonstrate the feasibility and effectiveness of the framework, and much better results are achieved than some existed literature results.