Improving Pareto Optimal Designs Using Genetic Algorithms

Abstract: Pareto optimal designs are the best designs that can be produced for a given problem formulation for a given set of criteria when the criteria are not combined in any way. If the goal is to improve the performance in those criteria, then it is possible to manipulate the problem formulation to achieve an improvement. The approach adopted is to encode the formulation in a genetic algorithm and to allow the formulation to evolve in the direction of improving Pareto optimal designs. A set of rules (in the form of a shape grammar), the execution of which produces a design, is encoded as the genes in a genetic algorithm. However, the rule set is allowed to evolve, not just the order of execution of rules. We present an example demonstrating both the approach and its utility in improving Pareto optimal designs.     

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。     

Genetic Algorithms for Optimal Urban Transit Network Design

This article attempts to highlight the effectiveness of genetic algorithm (GA)–based procedures in solving the urban transit network design problem (UTNDP). The article analyzes why traditional methods have problems in solving the UTNDP. The article also suggests procedures to alleviate these problems using GA–based optimization technique. The thrust of the article is three–fold: (1) to show the effectiveness of GAs in solving the UTNDP, (2) to identify features of the UTNDP that make it a difficult problem for traditional techniques, and (3) to suggest directions, through the presentation of GA–based methodologies for the UTNDP, for the development of GA–based procedures for solving other optimization problems having features similar to the UTNDP.     

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.     

某斜拉桥桥塔锚固区预应力筋优化研究

对某斜拉桥桥塔锚固区布置的双层环向预应力筋进行优化研究。将单位环向预应力筋对锚固区的预应力效应和正常工作拉索荷载对锚固区的索力效应分别单独进行考虑,然后采用线性叠加,得出了两者在锚固区的效应。通过考虑各种约束函数,并结合预应力筋数量最小这一目标函数,对某斜拉桥桥塔锚固区现有的环向预应力筋布置方式进行优化研究。优化研究表明:桥塔锚固区仅仅采用环向预应力筋布置时,长短边的应力和抗裂安全系数都不可能均匀;另外,预应力筋布置在锚固区偏外侧的位置时,所需预应力筋数量最少。     

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.     

Heuristic Analysis of Bridge Trusses Under AASHTO Live Loads

In a recent article, Adeli and Balasubramanyam [2] presented a heuristic approach for analysis of bridge trusses subjected to American Association of State Highway and Transportation Officials (AASHTO) moving loads. This procedure is based on the recognition of patterns of influence line diagrams (ILDs) for various members of a bridge truss. The procedure was applied to Pratt trusses. This arricle extends and generalizes the previous work of the authors by applying it to other classes of trusses, i.e., the statically determinate K-trusses and the statically indeterminate Parker trusses. This approach is significantly more efficient than the bruteforce method of generating the nodal load vectors due to the moving loads positioned at numerous locations along the span and performing numerous structural analyses. Thus, it is particularly suitable for microcomputers. The heuristic approach presented in this article can be easily extended to other classes of bridge trusses.     

Reliability-Based Optimal Design of Electrical Transmission Towers Using Multi-Objective Genetic Algorithms

Abstract:   A hybrid methodology for performing reliability-based structural optimization of three-dimensional trusses is presented. This hybrid methodology links the search and optimization capabilities of multi-objective genetic algorithms (MOGA) with structural performance information provided by finite element reliability analysis. To highlight the strengths of the proposed methodology, a practical example is presented that concerns optimizing the topology, geometry, and member sizes of electrical transmission towers. The weight and reliability index of a tower are defined as the two objectives used by MOGA to perform Pareto ranking of tower designs. The truss deformation and the member stresses are compared to threshold values to assess the reliability of each tower under wind loading. Importance sampling is used for the reliability analysis. Both the wind pressure and the wind direction are considered as random variables in the analysis. The research results presented demonstrate the benefit of implementing MOGA optimization as an integral part of a reliability-based optimization procedure for three-dimensional trusses.     

Dynamic Optimal Traffic Assignment and Signal Time Optimization Using Genetic Algorithms

Abstract:   In this article a dynamic system-optimal traffic assignment model is formulated for a congested urban road network with a number of signalized intersections. A simulation-based approach is employed for the case of multiple-origin-multiple-destination traffic flows. The artificial intelligence technique of genetic algorithms (GAs) is used to minimize the overall travel cost in the network with fixed signal timings and optimization of signal timings. The proposed method is applied to the example network and results are discussed. It is concluded that GAs allow the relaxation of many of the assumptions that may be needed to solve the problem analytically by traditional methods.     

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．     

双层桥面三桁刚性悬索加劲钢桁梁桥全桥试验模型

为了研究中国首座双层桥面三桁刚性悬索加劲钢桁梁桥--东江大桥的空间受力行为,并检验施工方案,针对其结构独特、受力复杂、施工难度大的特点,进行了全桥缩尺模型试验.详细介绍了东江大桥模型试验的目的、模型总体构造、截面设计原则、连接构造、施工过程模拟、支座设计以及试验加栽等.从模型设计可以看出:该模型制造难度较大;研制的模型能够满足利用试验模拟施工全过程以及对结构局部进行测试的要求.     

A Heuristic Approach for Interactive Analysis of Bridge Trusses under Moving Loads

A heuristic approach is presented for interactive analysis of statically determinate and indeterminate bridge trusses subjected to moving loads such as the American Association of State Highway and Transportation Officials (AASHTO) specifications live loads. These loads consist of two-axle truck loading, two-axle truck plus one-axle semitrailer loading, and uniform lane loading. The procedure is based on using the information about the shape of the influence line diagrams (ILDs) for members of a truss. This information may be obtained through numerical experimentation for any given type of truss. A methodology is presented for classifying the ILDs for member axial forces of a bridge truss. The procedure is applied to Pratt trusses. Heuristic rules are developed for finding the maximum compression and tensile forces in the members of a Pratt truss based on its characteristic influence line diagrams and the type of the AASHTO live load. The procedure presented in this article results in substantial savings in structural analysis computations, and is specially suitable on microcomputers in an interactive environment.     

三主桁连续板桁组合桥空间计算方法研究

针对三主桁连续板桁组合桥的构造特点,通过构造结合梁单元的位移模式,利用势能原理推导结合梁单元的刚度矩阵,提出2种结合梁法,结合梁法一的特点是把桥面板作为主桁架弦杆的上翼缘并与主桁架弦杆形成钢-混结合梁,结合梁法二的特点是把桥面板作为纵、横梁的上翼缘并与纵、横梁形成钢-混结合梁;而常规的板梁组合法的特点是桥面板为连续各向...     

基于遗传算法的枝状燃气管网布局优化

以图论和遗传算法为理论基础,以管网造价最小为优化目标,提出枝状燃气管网遗传布局优化的方法,并针对枝状管网的特点,对基本遗传算法进行改进。改进后的遗传算法能够获得造价最小的布置方案,算法寻优效率较高。     

最优解算法综述

本文研究了钢筋下料优化的离散组合最优化问题,并对组合优化中常用的遗传算法、模拟退火算法、启发式算法进行了综述,探讨了各自的特点并进行比较。最后根据近几年国内外的文献发现,单独的某个算法找到最优解存在随机性,且得到最优解会花费大量时间。解决这一问题的方法是将几种算法根据具体工况进行组合,组合后的算法的效率更高。     

基于神经网络和遗传算法的大跨度PC斜拉桥参数识别研究

在大跨度PC斜拉桥施工控制中,主梁挠度实测值和理论计算值存在偏差的主要原因之一是计算模型参数取值有偏差。本文分析和确定了主要影响参数,建立了反应主要影响参数和挠度变化之间非线性映射关系的BP神经网络模型,结合遗传算法并利用其强大的全局搜索能力求解出满足运算终止条件的最优解,即待识别参数值。同时,将此优化方法成功运用于梅溪河斜拉桥参数识别中,取得了满意的效果。     

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。     

双层桥面三桁刚性悬索加劲钢桁梁桥全桥静动力模型设计

介绍了东江大桥双层桥面三桁刚性悬索加劲钢桁梁全桥静动力模型的设计方法.在刚度相似法的基础上进行改进,确定合适的相似比,有效地解决了模型与实桥各部分结构弹性模量相似比不一致的模型设计问题;并通过增加附加质量的方法,使得模型桥各部分的角频率与实桥的角频率具有一致的相似比.最后介绍了模型试验自重荷载及活载加栽的有限元计算方法.该方法解决了加载点多且分布不均匀的难题.     

钱塘江第四大桥拱肋钢锚箱受力分析

吊杆和拱肋之间的锚固区是把桥面荷栽传给拱肋的关键部位，由于锚固区构造复杂，承受较大的局部荷栽，故建立锚固区的实体模型进行有限元分析，得到了锚固区局部的应力，并对钢锚箱与钢管拱肋的连接部位的焊缝进行了疲劳强度的验算。