Network-Wide Optimal Scheduling of Transit Systems Using Genetic Algorithms

The primary objective of any transit system is to provide a better level of service to its passengers. One of the good measures of level of service is the waiting time of passengers during their journey. The waiting time consists of an initial waiting time (the time a passenger waits to board a vehicle at his or her point of origin) and a transfer time (the time a passenger waits at a transfer station while transferring from one vehicle to another). An efficient schedule minimizes the overall transfer time (TT) of passengers transferring between different routes as well as the initial waiting time (IWT) of the passengers waiting to board the vehicle at their point of origin. This paper uses genetic algorithm (GA)—a search and optimization procedure—to find optimal/near-optimal schedules of vehicles in a transit network. The main advantage of using GA is that the transit network scheduling problem can be reformulated in a manner that is computationally more efficient than the original problem. Further, the coding aspect of GA inherently takes care of most of the constraints associated with the scheduling problem. Results from a number of test problems show that GAs are able to find optimal/near-optimal schedules with minimal computational resources.     

Optimal Design of Water Distribution Systems Using Genetic Algorithms

This article proposes an optimal design methodology for the design of water distribution systems based on genetic algorithms. The objective of the optimization is to minimize the capital cost, subject to ensuring adequate pressures at all nodes. The proposed method differs from those of previous workers who have applied genetic algorithms in that the strings in the genetic algorithm model are coded using real variables, and this avoids the problem of redundant states often found when using binary (and Gray) coding schemes. A fitness function is also proposed that incorporates a variable penalty coefficient that depends on the degree of violation of the pressure constraints. The method also differs from those of previous workers in that it does not require solution of the nonlinear equations governing the flows and pressures in the distribution system for each individual member within the population. Hence this method shows a significant advantage compared with previously published techniques in terms of computational efficiency. The method has been tested on several networks, including networks used for benchmark testing least-cost design algorithms, and has been shown to be very efficient and robust.     

遗传算法在给水管网优化设计中的应用

遗传算法是一种新型的进化算法，它是一种通过模拟自然进化过程搜索最优解的随机寻优的数学规划方法．阐述了应用遗传算法进行给水管网优化设计的原理、特点，并对青岛市城阳区某工业园给水管网工程进行优化求解．     

Optimal Layout of Bridge Trusses by Genetic Algorithms

In this paper we present an approach to the layout and shape-optimization problem of bridge truss structures using genetic algorithms. The objective is to find an optimal layout design that will have minimum weight or material volume, subject to performance constraints related to member stresses, joint displacements, and member buckling. An automated two-stage optimization search process, which integrates structural analysis by finite-element method, genetic algorithms, and cognitive topology patterns (domain knowledge), is developed to solve the optimal problem. Two examples concerning bridge truss structure are investigated to demonstrate the effectiveness of the proposed method in solving these layout-optimization problems.     

Optimum Design of Grillage Systems Using Genetic Algorithms

Genetic algorithms have attracted great attention due to their ability to provide a solution to discrete optimum design problems. In this study, a genetic algorithm is presented for the optimum design of grillage systems to decide the cross-sectional properties of members from a standard set of universal beam sections. The deflection limitations and the allowable stress constraints are considered in the formulation of the design problem. Furthermore, in obtaining the response of grillage systems, the effect of warping and shear is also taken into account. The algorithm starts with an initial population of designs and carries out basic genetic operations of selection, mating, crossover, and mutation that yield to a new generation. It continues the generation of populations until the same individual dominates the population. An improvement is also suggested to the general steps of the genetic algorithm to prevent the destruction of good individuals during the generation of new populations. The algorithm is applied to the optimum design of a 40-member grillage system to investigate the effect of warping.     

Genetic Algorithms in Optimal Detailed Design of Reinforced Concrete Members

Genetic algorithms emulate biologic evolutionary concepts to solve search and optimization problems. In this work, they are employed to perform the optimal detailed design of reinforced concrete members of multistory buildings. The objective is to convert the required reinforcement in square centimeters, given at a number of cross sections, into a set of reinforcing bars of specific diameter and length located at specific places along the member taking into account different criteria and rules of design practice. The anchorage lengths are taken into account, and the bars are cut at appropriate locations. For such problems, enumeration methods lead to expensive solutions, whereas genetic algorithms tend to provide near-optimal solutions in reasonable computing time. The genetic algorithms used in this work are based on a roulette wheel reproduction scheme; single, multiple-point, and uniform crossover; and constant or variable mutation schemes. A constant or variable elitist strategy is also used that passes the best designs of a generation to the next generation. The method decides the detailed design on the basis of a multicriterion objective that represents a compromise between a minimum weight design, a maximum uniformity, and the minimum number of bars for a group of members. By varying the weighting factors, designs with different characteristics result. Various parameters of the genetic algorithm are considered, and the corresponding results are presented.     

混合遗传算法在离散变量结构优化中的应用

针对遗传算法在离散变量结构优化设计中的缺陷,将进退搜索算法同遗传算法相结合,提出了一种混合遗传算法。建立了离散变量结构优化模型,并对一11杆桁架结构进行了优化设计。算例结果表明,混合遗传算法收敛快、精度高,应用于离散变量结构优化设计是有效的。     

Using GIS, Genetic Algorithms, and Visualization in Highway Development

A model for highway development is presented, which uses geographic information systems (GIS), genetic algorithms (GA), and computer visualization (CV). GIS serves as a repository of geographic information and enables spatial manipulations and database management. GAs are used to optimize highway alignments in a complex search space. CV is a technique used to convey the characteristics of alternative solutions, which can be the basis of decisions. The proposed model implements GIS and GA to find an optimized alignment based on the minimization of highway costs. CV is implemented to investigate the effects of intangible parameters, such as unusual land and environmental characteristics not considered in optimization. Constrained optimization using GAs may be performed at subsequent stages if necessary using feedback received from CVs. Implementation of the model in a real highway project from Maryland indicates that integration of GIS, GAs, and CV greatly enhances the highway development process.     

基于替代模型与遗传算法的混杂热塑性复合结构的优化

采用综合处理方法,如用织物结构或单向纤维丝,能够对注塑或模压热塑结构进行局部加强。网状结构可以通过周期性典型的注塑或模压方式生产,具有较好的力学性能。由于局部增强在数量和位置方面都增加了设计过程的复杂性,数学优化技术与有限元模拟的结合能够设计重量和成本均较优的结构。这项工作评价了与遗传算法结合的近似模型,案例采用普通的而不是工业化的梁结构。有限元模型基于层单元,分4种不同材料,用9个设计变量控制不同区域材料的厚度。材料的非线性性质包含了超塑聚合物和一种特殊织物材料的压缩响应。对有和没有定期选择、径向基函数及Kriging的4种不同近似方法的精度进行了评估。不是孤立地检查平均近似误差,而是提出了一个如何处理单一近似误差的方法。结果表明,在连续的、连续与离散混合的64个设计变量约束条件下,多峰遗传算法可以成功地用于优化结构重量。     

给水管网遗传优化设计中惩罚系数的确定

针对应用遗传算法进行给水管网优化设计中惩罚系数难以确定的问题,提出了一种应用给水管网技术经济参数来计算惩罚系数的新方法。数值模拟结果表明,采用该方法计算惩罚系数是可行的,能以较高的概率搜索到最优解,减小了确定惩罚系数的随意性和盲目性,提高了遗传算法的进化效率和收敛性,对于工程优化设计具有一定的参考价值。     

A Fuzzy Expert System for Damage Assessment Using Genetic Algorithms and Neural Networks

Abstract: An efficient knowledge-acquisition support method is required for improvement and maintenance of the knowledge base in durability evaluation of an RC bridge deck. Such a method is proposed in this paper to automatically acquire fuzzy production rules. This method makes joint use of genetic algorithms and a neural network. Using a neural network as a subsystem, the evaluation function of genetic algorithms can be provided with the weights of the neural network. Introducing a neural network into genetic algorithms, it is possible to acquire new knowledge so that the method is useful when it is difficult to acquire knowledge in the field.     

利用模糊逻辑和遗传法对高层建筑结构进行主动控制

近年来土木工程结构上采用的主动调谐质量阻尼(ATMD)控制系统开始引起研究者们极大的关注。强调利用遗传法和模糊逻辑(GFLC)的组合作用,在ATMD控制系统的设计和优化措施中选择合适的参数,以使得在地震激励作用下建筑的反应最小化。因此,提出的方法,在处理不可预知的非线性现象方面,具有模糊逻辑控制(FLC)和遗传法(GAs)的双重优势。模型采用剪切型框架,并且在状态空间下解决问题。这种方法被应用在位于伊朗共和国的雷什特城的一个11层高的实际建筑上。从这种控制系统中得到的结果,与采用TMD方案和线性二次调节(LQR)控制方法中得出的结果相对比发现,GAs和FLC的综合使用对减少建筑在地震作用下的反应非常有效。     

单向碳纤维增强环氧基复合物中纤维破坏的荷载传递微观机制：第三部分复合性能的多尺度再现

第三部分介绍了多尺度再现方法。该方法考虑了与复合物退化相关的非常重要的微观现象,包括纤维的断裂、界面剥离、纤维之间的超负荷以及模具的黏弹性性能。分析结果用以精确预测单向碳纤维增强环氧基复合物中纤维的宏观破坏以及量化相同材料压力容器中的损伤累积。该方法还介绍了由于纤维的破坏形成的声发散行为,以及如何预测压力容器的残余寿命。     

Algorithms for Optimal Design of Smart Structural Systems

Simultaneous optimal design of smart structural systems is considered in this article. A major augmentation to this multidisciplinary optimization problem is optimal structural layout design in terms of shape and topology. Four algorithms, namely, guided random search techniques, sequential mathematical programming, and their mixtures, are presented and used for the problem solving. A method to prevent singularities when updating structural layout is also suggested. Design variable linking schemes are used to reduce the number of design variables and give reasonable optimal results. Two examples are solved. Comparison of the algorithms on the basis of achieved improvements in system energy, control effort, robustness, and controllability is made.     

Hybrid Genetic Algorithms for Optimization of Truss Structures

The genetic algorithm (GA) is a new optimization paradigm that models a natural evolution mechanism. The framework of the GA naturally corresponds to a discrete optimization problem. Although the GA is very robust, it is also very computationally intensive and hence slower than other methods. To speed up the convergence, this article proposes a hybrid GA that combines the concept of survival of the fittest with the concept of adaptation. The fully stressed design optimality criterion is employed to play the role of adaptation. Numerical examples show that even though the displacement constraints are active, (1) both average weight and minimum weight obtained by a hybrid GA are less than those obtained by a pure GA, (2) a hybrid GA is more stable than a pure GA, and (3) the speed of convergence of a hybrid GA is superior to that of a pure GA.     

Genetic Algorithms in Topologic Design of Grillage Structures

The paper describes the use of genetic algorithms in determining the optimal layout and sizing of two-dimensional (2D) and three-dimensional (3D) grillage structures for stress, displacement, and element buckling constraints. The design space for this problem is highly nonconvex and not readily amenable to traditional methods of nonlinear programming. The approach develops an optimal topology from a set of predefined structural universe so as to satisfy kinematic stability requirements and other constraints on structural response. A two-level genetic algorithm–based search is used, wherein the kinematic stability constraints are imposed at one level, followed by the treatment of stress and displacement constraints at a second level of optimization. Since genetic algorithms search for an optimal design from a discrete set of alternatives in the design space, their adaptation in the topologic design problem is natural and is governed only by issues related to computational efficiency. Strategies designed to alleviate the computational requirements of a genetic algorithm–based search are discussed in the paper.     

一种改进调度算法的电梯节能新技术

针对现有电梯调度算法存在的重服务效率轻节能的不足,提出了一种基于电梯等待时间的电梯节能调度方法。设计了基于等待时间的两种电梯节能调度算法,并利用这两种算法开发了群控电梯模拟运行测试软件平台,并在该模拟软件平台上,分别对加与不加等待时间的电梯运行能耗进行了模拟测试及结果分析。结果表明,基于等待时间的电梯节能调度技术应用于群控电梯的运行节能,一般能实现节能3％～15％。     

keySection建筑生成方法研究

详细介绍东南大学建筑学院建筑设计生成方法研究组的keySection研究过程及其成果.该生成工具以建筑庭院采光设计为出发点,运用简易进化算法及程序编写探索了建筑设计生成技巧的新方法.     

Improving Structural Design by Genetic Search

Design optimization has long been a goal eagerly sought by engineers. The established classic methods of optimization demanded large-scale computing facilities met only by the mainframe computers of the 1960s through 1980s. Two important developments of the last decade have changed the course of events. First, the microcomputer has arrived as a large-capacity, rapid-processing computing capability well up to the needs of optimization. Second, the methods of optimization have been joined by new approaches based on concepts of evolutionary or "genetic" progression in which an initial population (collection) of individuals (designs) is changed progressively in the direction of improved "fitness." The concept of searching a large design space, notionally created by combining all possible values of the design variables and tracing a path through increasingly "better" designs, has become a realistic prospect. The purpose of this paper is to describe the ideas behind the concept of genetic search, to outline the basic principles of the genetic algorithm, to illustrate the genetic algorithm with a simple example of structural optimization, and to consider further developments in this area.