具有函数的泛逼近性能的模糊控制器的设计

构造出9类具有函数的泛逼近性能的模糊控制器,这些模糊控制器均由模糊蕴涵算子构造而成.利用倒车仿真说明采用具有函数的泛逼近性能的模糊控制器可以用于实际的模糊控制系统中.     

遗传模糊聚类算法在图像边缘检测中的应用

将一种改进的遗传模糊c-均值聚类(GFGA)算法应用到图像的边缘检测中.我们将灰度图像中的每一个像素点看成是一个数据样本,将该点的灰度值经过Robert算子、Sobel算子和Prewitt算子处理构成它的特性向量,形成具有三维特征的数据集,然后对这个数据集应用遗传模糊聚类算法进行分类,自适应地检测出图像的边缘点,达到提取边缘的目的.实验结果表明,这种混合算法能得到很好的边缘效果,并且得到的结果无需再细化处理,提高了边缘定位的精度.     

Global Multiobjective Optimization Using Evolutionary Algorithms

Since the 60s, several approaches (genetic algorithms, evolution strategies etc.) have been developed which apply evolutionary concepts for simulation and optimization purposes. Also in the area of multiobjective programming, such approaches (mainly genetic algorithms) have already been used (Evolutionary Computation 3(1), 1–16).In our presentation, we consider a generalization of common approaches like evolution strategies: a multiobjective evolutionary algorithm (MOEA) for analyzing decision problems with alternatives taken from a real-valued vector space and evaluated according to several objective functions. The algorithm is implemented within the Learning Object-Oriented Problem Solver (LOOPS) framework developed by the author. Various test problems are analyzed using the MOEA: (multiobjective) linear programming, convex programming, and global programming. Especially for hard problems with disconnected or local efficient regions, the algorithms seems to be a useful tool.     

Optimization of Mechanically Coupled Composite Pipes by Genetic Algorithms

The optimization of composite components with regard to minimum weight and maximum load bearing capacity in consideration of multiple constraints is an optimization problem of rather high complexity. Genetic Algorithms are a good choice for solving such problems. In this paper the formulation of a Genetic Algorithm for the simultaneous optimization of two thin walled, mechanically coupled composite pipes subjected to a combination of thermal and mechanical loads is presented. The optimization goal is the minimization of the total mass of the pipe arrangement taking into account multiple design constraints. It is shown that Genetic Algorithms are valueable tools for solving optimization problems with a large number of parameters. Furthermore, it is possible to find additional, perhaps practicable, close‐to‐optimal configurations as a byproduct of the optimization process. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Multidimensional Optimization with a Fuzzy Genetic Algorithm

We present a new heuristic method to approximate the set of Pareto-optimal solutions in multicriteria optimization problems. We use genetic algorithms with an adaptive selection mechanism. The direction of the selection pressure is adapted to the actual state of the population and forces it to explore a broad range of so far undominated solutions. The adaptation is done by a fuzzy rule-based control of the selection procedure and the fitness function. As an application we present a timetable optimization problem where we used this method to derive cost-benefit curves for the investment into railway nets. These results show that our fuzzy adaptive approach avoids most of the empirical shortcomings of other multiobjective genetic algorithms.     

模糊推理的SIS算法

模糊推理的核心问题是求解模糊取式和模糊拒取式。算法的还原性是评价模糊推理好坏的重要标准之一。现有求解模糊取式和模糊拒取式问题的算法都不满足无条件的还原性。本文提出了一种新的模糊推理算法——SIS算法,证明了SIS算法是不需要附加任何条件的还原算法,并且还讨论了SIS算法的λ水平解。此算法必将对模糊推理和模糊控制等产生较大的影响。     

Performance Analysis of Adaptive Genetic Algorithms with Fuzzy Logic and Heuristics

In this paper, we propose some genetic algorithms with adaptive abilities and compare with them. Crossover and mutation operators of genetic algorithms are used for constructing the adaptive abilities. All together four adaptive genetic algorithms are suggested: one uses a fuzzy logic controller improved in this paper and others employ several heuristics used in conventional studies. These algorithms can regulate the rates of crossover and mutation operators during their search process. All the algorithms are tested and analyzed in numerical examples. Finally, a best genetic algorithm is recommended.     

基于遗传算法的多目标可靠性优化

在元件的体积、重量和造价的共同约束下的多级串并联系统的可靠性优化问题是一个具有多局部极值的、非线性的、同时具有整数和实数变量的混合优化问题.将遗传算法和多目标可靠性分配问题相结合,对可靠性分配问题进行求解,得到较好效果,从而得出结论,遗传算法在求解多目标可靠性优化问题中是一种行之有效的方法.     

基于遗传算法的生产批量优化问题

本文针对生产费用、生产准备费用和库存费用综合指标最小的最优的能力约束生产批量问题,建立基于遗传算法的数学模型,通过模拟试验及实际问题的计算验证了算法的可行性。     

Solving the Graphical Steiner Tree Problem Using Genetic Algorithms

We develop a genetic algorithm (GA) to solve the Steiner Minimal Tree problem in graphs. To apply the GA paradigm, a simple bit string representation is used, where a 1 or 0 corresponds to whether or not a node is included in the solution tree. The standard genetic operators — selection, crossover and mutation — are applied to both random and seeded initial populations of representations. Various parameters within the algorithm have to be set and we discuss how and why we have selected the values used. A standard set of graph problems used extensively in the comparison of Steiner tree algorithms has been solved using our resulting algorithm. We report our results (which are encouragingly good) and draw conclusions.     

基于遗传算法的公交智能调度与优化——以南岳衡山景区为例

南岳衡山(以下简称南岳)以“外秀于林,内秀于文”驰名中外,作为风景名胜区近几年游客人数不断攀升。本文利用遗传算法对景区内公交线路的调度模型进行求解。首先,根据景区现有的公共交通资源和旅客的出行规律,构建了以旅游公交营运成本、游客等待成本和游客流失成本三方面优化目标的模型;然后,使用线性加权方法实现对旅客与营运公司双方利益兼顾,进一步完善模型;最后,利用Matlab对实地调研数据的车辆调度方案求解。表明该模型改善了南岳景区旅游交通,满足游客的合理需求,提升了旅游公司经营策略,真正成为“寿岳独秀”的知名品牌。     

Optimization Strategies for Optimization Procedures and Algorithms

New theory of circle patterns (or packing) is now well developed. Uniformization result, the circle packing theorem [1], is based on analytic functions, while Doyle spirals are analoques of entire functions. Doyle constructed entire immersed hexagonal circle patterns analoques to the exponential map and conjectured that these immersed packings which came to be known as Doyle spirals, are the only entire immersed hexagonal circle packings [2, 3]. Schramm had found that a framework of circle patterns based on the square grid which is called SG patterns, is more tractable than the traditional hexagonal patterns [4]. We present a new approach concerned with a theory of discrete multistage optimization [5, 6]. Optimization strategies for optimization procedures are good tools for modelling. Consequently calculations are not far from empirical observations and experimental results. This clearly demonstrates the broad interest in our field and the role which mathematical modelling and analysis is expected to play with regard to the solution of the challenging problems that we are facing at the beginning of this millennium.     

Using Genetic Algorithms and Heuristics for Job Shop Scheduling with Sequence-Dependent Setup Times

In this work a new heuristic solution technique for the Resource-Constrained Project Scheduling Problem (RCPSP) is proposed. This technique is a hybrid multi-pass method that combines random sampling procedures with a backward–forward method. The impact of each component of the algorithm is evaluated through a step-wise computational analysis which in addition permits the value of their parameters to be specified. Furthermore, the performance of the new technique is evaluated against the best currently available heuristics using a well known set of instances. The results obtained point out that the new technique greatly outperforms both the heuristics and metaheuristics currently available for the RCPSP being thus competitive with the best heuristic solution techniques for this problem.     

区间自适应遗传算法优化无约束非线性规划问题

针对无约束非线性规划传统优化方法存在的问题,将区间自适应遗传算法引入无约束非线性规划优化中,算法可以利用当前进化信息,自适应移动搜索区间,找到全局最优解,故可缩短搜索区间长度,提高编码精度,降低算法计算量,解决了传统遗传算法处理优化问题时,给定区间必须包含最优解这一问题,这也是本算法有别于其他优化算法的独特优势,为某些最优解所在区间难以估计的无约束非线性规划问题的优化提供了一条有效可行的途径.系统阐述了区间自适应遗传算法的原理,给出了算法优化无约束非线性规划问题的步骤,以MatlabR2016b仿真方式对算法进行了实例测试,结果表明,方法是一种计算稳定、正确、有效、可靠实用的无约束非线性规划优化方法.     

Rollout Algorithms for Combinatorial Optimization

We consider the approximate solution of discrete optimization problems using procedures that are capable of magnifying the effectiveness of any given heuristic algorithm through sequential application. In particular, we embed the problem within a dynamic programming framework, and we introduce several types of rollout algorithms, which are related to notions of policy iteration. We provide conditions guaranteeing that the rollout algorithm improves the performance of the original heuristic algorithm. The method is illustrated in the context of a machine maintenance and repair problem.     

Adjustment of Residual Stresses in Unsymmetric Fiber-Reinforced Composites Using Genetic Algorithms

During the manufacturing process of multilayered fiber-reinforced composites with variable fiber orientations, residual stresses build up in these composites due to the directional expansion of single unidirectionally reinforced layers. Depending on the laminate lay-up, the inhomogeneous residual stresses, which are caused by thermal effects, moisture absorption, and chemical shrinkage, can lead to large multistable out-of-plane deformations. Instead of avoiding these curvatures, they can be advantageously used for technical applications following the near-net-shape technology. In order to adjust the deformations to the technical requirements, genetic algorithms in combination with a nonlinear calculation method have been developed, which can purposefully adapt the laminate lay-up depending on the loading and process parameters.     

二层随机规划基于随机模拟的遗传算法

本提出了二层随机规划模型，给出了求解二层随机规划问题的基于随机模拟的遗传算法。实际算例表明算法是可行的、有效的。     

Improved Estimation and Uncertainty Quantification Using Monte Carlo-Based Optimization Algorithms

In this article, we present a novel method to obtain both improved estimates and reliable stopping rules for stochastic optimization algorithms such as the Monte Carlo EM (MCEM) algorithm. By characterizing a stationary point, θ*, of the algorithm as the solution to a fixed point equation, we provide a parameter estimation procedure by solving for the fixed point of the update mapping. We investigate various ways to model the update mapping, including the use of a local linear (regression) smoother. This simple approach allows increased stability in estimating the value of θ* as well as providing a natural quantification of the estimation uncertainty. These uncertainty measures can then also be used to construct convergence criteria that reflect the inherent randomness in the algorithm. We establish convergence properties of our modified estimator. In contrast to existing literature, our convergence results do not require the Monte Carlo sample size to go to infinity. Simulation studies are provided to illustrate the improved stability and reliability of our estimator.     

Comparing Optimization Algorithms for Shape Optimization of Extrusion Dies

The classical approach to extrusion die design relies heavily on the experience of the die designer; Especially the designer's ability to create an initial die design from a product design, the designer's constructional knowledge and performance during the running-in trials. Furthermore, the relative unpredictability of the running-in trials combined with the additional resource usage introduce uncertainties and delays in the time-to-market of a given product. To lower these delays and resource usage, extrusion die design can benefit greatly from numerical shape optimization. In this application, however, plastics melts pose a difficult obstacle, due to their rather unintuitive and nonlinear behavior. These properties complicate the numerical optimization process, which mimics running-in trials and relies on a minimal number of optimization iterations. As part of the Cluster of Excellence Integrative Production Technologies for High-Wage Countries at the RWTH Aachen University, an effort is made to shorten the manual running-in process by the means of numerical shape optimization. Using an in-house numerical shape optimization framework, a set of optimization algorithms, consisting of global, derivative-free and gradient-based optimizers, are evaluated with respect to the best die quality and a minimal number of optimization iterations. This evaluation is an important step on the way to include more computationally intensive material models into the optimization framework and identify the best possible optimization strategy for the numerical design of extrusion dies. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

非光滑总体优化的区间算法[英文]

本文利用区间工具及目标函数的特殊导数，给出一个非光滑总体优化的区间算法，该算法提供了目标函数总体极小值及总体极小点的取值界限（在给定的精度范围内）。我们也将算法推广到并行计算中。数值实验表明本文方法是可靠和有效的。