基于蚁群算法的刚架结构优化设计

通过对蚁群算法原理分析及对3跨24层168根杆件的钢框架采用蚁群算法的结构质量进行优化计算,并对此结构采用美国钢结构规范(AISC)、英国钢结构规范(BS 5990)、中国<钢结构设计规范>(GB 50017-2003)3种规范体系对比分析,显示出了蚁群算法对基于TSP模型的此类结构优化设计具有很好的实用价值,为土木工程结构优化与分析计算提供了有效可行的方法.     

基于蚁群算法的刚架结构优化设计

通过对蚁群算法原理分析及对3跨24层168根杆件的钢框架采用蚁群算法的结构质量进行优化计算,并对此结构采用美国钢结构规范(AISC)、英国钢结构规范(BS5990)、中国《钢结构设计规范》(GB50017-2003)3种规范体系对比分析,显示出了蚁群算法对基于TSP模型的此类结构优化设计具有很好的实用价值,为土木工程结构优化与分析计算提供了有效可行的方法。     

基于改进蚁群算法的无线传感器网络优化

本文通过对无线传感器技术的阐述,提出了一种最大流量算法,用于研究静态无线传感器网络中K势垒的高覆盖率问题。     

浅谈结构优化设计中的蚁群算法

结构优化设计是设计理论的重要方面,在建筑界得到广泛的重视,随着社会经济的发展,结构设计也得到了长足的发展,对于结构优化设计理念和算法,人们也提出了越来越高的要求。随之蚁群算法逐步得到了发展,蚁群算发是一种新型的、新生的仿生进化算法。有着很强的适应性。也比较容易和其他的方法结合来运作,所以,研究蚁群算的优化方法,有着十分重要的意义,本文将会总结蚁群算法的原理,以便能够在现实中能够广泛的运用。     

基于改进蚁群算法的冰鲜水产品配送路径优化方法研究

为研究冰鲜水产品最优配送路径的优化方法,在传统蚁群算法基础上提出一种改进的蚁群算法,先后分别采用局部最优和全局最优两种方式对传统蚁群算法的信息素更新方式加以扩大至最优解寻觅范围,并对启发因子的函数定义范围加以扩展至初始节点,利用2-opt算法进行局部优化。实例仿真结果表明,在相同配送条件下,改进后的蚁群算法与避圈法、传统蚁群算法相比较,其配送时间分别缩短31.64%和8.15%,其配送路径长度分别缩短21.89%和16.94%。研究表明,改进的蚁群算法可用于冰鲜水产品最优配送路径的计算,该方法可在实际应用中有效提高冰鲜水产品的物流运输效率。     

基于改进蚁群算法的冰鲜水产品配送路径优化方法研究

为研究冰鲜水产品最优配送路径的优化方法,在传统蚁群算法基础上提出一种改进的蚁群算法,先后分别采用局部最优和全局最优两种方式对传统蚁群算法的信息素更新方式加以扩大至最优解寻觅范围,并对启发因子的函数定义范围加以扩展至初始节点,利用2-opt算法进行局部优化。实例仿真结果表明,在相同配送条件下,改进后的蚁群算法与避圈法、传统蚁群算法相比较,其配送时间分别缩短31.64%和8.15%,其配送路径长度分别缩短21.89%和16.94%。研究表明,改进的蚁群算法可用于冰鲜水产品最优配送路径的计算,该方法可在实际应用中有效提高冰鲜水产品的物流运输效率。     

蚁群算法及其在硐群施工优化中的应用

为解决复杂的组合优化问题，近来提出了一种新的模拟进化算法－－蚁群算法。从原理，算法实现等方面详细介绍了该算法，并针对有序组合优化问题，改进了原算法。把改进算法应用于地下工程中的一类组合优化问题－硐群施工顺序优化。一个大型地下硐室群工程的施工顺序优化结果表明，蚁群算法的应用效果良好，是解决岩土工程中的组合优化问题的一种好方法。     

基于修正萤火虫算法的多模函数优化

萤火虫优化(glowworm swarm optimization,GSO)算法是一种计算多模函数优化问题的新型算法,该算法和蚁群优化、粒子群优化一样,都是一种群智能算法。针对GSO算法在优化多模函数时收敛速度慢、求解精度不高和发现峰值率低的缺点,首先在算法中采用变步长的运动策略,使得步长随着迭代时间自适应地逐渐减小;其次采用较小的初始决策范围值;最后添加了萤火虫的自探索机制。改进后的学习行为更符合自然界生物的学习规律,更有利于萤火虫发现问题的所有局部最优解。利用标准测试函数对修正后的萤火虫算法进行测试,仿真结果表明,修正的萤火虫算法具有良好的收敛性和计算精度,在寻找多模函数的峰值个数时显示出很大的优势。     

网络资源的优化算法浅论

在分析了施工组织设计中的几种传统优化模式的基础上,对资源均衡优化算法进行了研究,探讨了其智能实现的可行性。     

基于增强型蚁群算法的压路机工作路径规划方法

在复杂工作空间中为压路机规划合理的施工路径,使所规划的路径与障碍物无触碰且长度最短,是有效指导交叉工序并行运转的基础。本文对蚁群算法进行改进,对信息素和启发信息差异性进行扩展,对搜索区间上下界进行迭代阈值限制,进而提出了一种增强型蚁群算法。利用增强型蚁群算法,结合工程实例,验证了压路机路径规划的效果。选择燃油量消耗指标进行结果对比,发现按照所规划的路径运动可以节省燃油,减少资源投入,进而提升工程项目建设的综合效益。     

Performance-based seismic design of steel frames using ant colony optimization

In this paper, a performance-based optimal seismic design of frame structures is presented using the ant colony optimization (ACO) method. This discrete metaheuristic algorithm leads to a significant improvement in consistency and computational efficiency compared to other evolutionary methods. A nonlinear analysis is utilized to arrive at the structural response at various seismic performance levels, employing a simple computer-based method for push-over analysis which accounts for first-order elastic and second-order geometric stiffness properties. Two examples are presented to illustrate the capabilities of ACO in designing lightweight frames, satisfying multiple performance levels of seismic design constraints for steel moment frame buildings, and a comparison is made with a standard genetic algorithm (GA) implementation to show the superiority of ACO for the discussed optimization problem.     

Constrained Ant Colony Optimisation Algorithm for the layout and size optimisation of sanitary sewer networks

The incremental solution building capability of Ant Colony Optimisation Algorithm (ACOA) is used in this paper for the efficient layout and pipe size optimisation of sanitary sewer network. Layout and pipe size optimisation of sanitary sewer networks requires optimal determination of pipe locations, pipe diameters and pipe slopes leading to a highly constrained mixed-integer nonlinear programming (MINLP) problem presenting a challenge even to the modern heuristic search methods. A constrained version of ACOA equipped with a Tree Growing Algorithm (TGA) is proposed in this paper for the simultaneous layout and pipe size determination of sewer networks. The method is based on the assumption that a base layout including all possible links of the network is available. The TGA algorithm is used in an incremental manner to construct feasible tree-like layouts out of the base layout, while the constrained ACOA is used to optimally determine the cover depths of the constructed layout. Proposed formulation is used to solve three hypothetical test examples of different scales and the results are presented and compared with those produced by a conventional application of ACOA in which an ad-hoc engineering concept is used for layout determination. The results indicate the effectiveness and efficiency of the proposed method to optimally solve the problem of layout and size determination of sewer networks.     

基于改进蚁群算法的钢结构优化设计

在介绍蚁群优化算法的原理、基本框架的基础上,提出了一种改进的蚁群算法——分层蚁群算法,并将该算法应用到钢结构优化设计中,最后通过一个算例验证了该方法的效率和有效性,结果表明该方法科学可行,具有很好的应用前景。     

蚁群算法在GIS最短路径求解中应用的初步研究

最短路径的求解是GIS应用中的主要问题之一。在传统的最短路径求解算法中,Dijkstra算法和启发式搜索算法-A＊算法具有较好的效果,得到了广泛的应用。蚁群算法是由意大利学者Dorigo等人于20世纪90年代初期通过模拟自然界中蚂蚁集体寻径的行为而提出的一种基于种群的启发式仿生进化系统。蚁群算法最早成功应用于解决著名的旅行商问题,该算法采用了分布式正反馈并行计算机制,易于与其他方法结合,而且具有较强的鲁棒性,是一种很有前途的仿生优化算法。本文将对该算法应用于GIS中最短路径的求解方面的问题进行初步的研究。     

运用蚁群优化法对钢结构进行基于性能的抗震设计

运用蚁群优化法(ACO)对钢结构进行了基于性能的抗震设计。这个离散的数学运算法比其他算法更为有效并精确。采用了非线性分析以得到结构在各种地震性能水平下的结构响应,采用一个简单的计算机程序,对包含一阶弹性和二阶几何刚度的特性进行推覆分析。采用两个实例说明了ACO在轻钢结构中的应用,证明其可满足抗弯钢结构在多种地震性能水平下的要求,同时也与标准遗传算法的结果进行对比,表明ACO更适合解决此类优化问题。     

Ant colony optimization-based multi-mode scheduling under renewable and nonrenewable resource constraints

An ant colony optimization (ACO)-based methodology for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) considering both renewable and nonrenewable resources is presented. With regard to the MRCPSP solution consisting of activity sequencing and mode selection, two levels of pheromones are proposed to guide search in the ACO algorithm. Correspondingly, two types of heuristic information and probabilities as well as related calculation algorithms are introduced. Nonrenewable resource-constraint and elitist-rank strategy are taken into account in updating the pheromones. The flowchart of the proposed ACO algorithm is described, where a serial schedule generation scheme is incorporated to transform an ACO solution into a feasible schedule. The parameter-selection and the resultant performance of the proposed ACO methodology are investigated through a series of computational experiments. It is expected to provide an effective alternative methodology for solving the MRCPSP by utilizing the ACO theory.     

Comparing Ant Colony Optimization and Genetic Algorithm Approaches for Solving Traffic Signal Coordination under Oversaturation Conditions

Abstract: This article proposes to solve the oversaturated network traffic signal coordination problem using the Ant Colony Optimization (ACO) algorithm. The traffic networks used are discrete time models which use green times at all the intersections throughout the considered period of oversaturation as the decision variables. The ACO algorithm finds intelligent timing plans which take care of dissipation of queues and removal of blockages as opposed to the sole cost minimization usually performed for undersaturation conditions. Two scenarios are considered and results are rigorously compared with solutions obtained using the genetic algorithm (GA), traditionally employed to solve oversaturated conditions. ACO is shown to be consistently more effective for a larger number of trials and to provide more reliable solutions. Further, as a master‐slave parallelism is possible for the nature of ACO algorithm, its implementation is suggested to reduce the overall execution time allowing the opportunity to solve real‐time signal control systems.     

A unified approach to parameter selection in meta-heuristic algorithms for layout optimization

Meta-heuristic optimization algorithms have attracted many researchers in the last decade. Adjustment of different parameters of these algorithms is usually a time consuming task which is mostly done by a trial and error approach. In this study an index, namely convergence factor (CF), is introduced that can show the performance of these algorithms. CF of an algorithm provides an estimate of the suitability of the parameters being set and can also enforce the algorithm to adjust its parameters automatically according to a pre-defined CF.In this study GA, ACO, PSO and BB-BC algorithms are used for layout (topology plus sizing) optimization of steel braced frames. Numerical examples show these algorithms have some similarities in common that should be taken into account in solving optimization problems.     

Optimal Design of Scissor-Link Foldable Structures Using Ant Colony Optimization Algorithm

Abstract:   In this article, the Ant Colony Optimization (ACO) algorithm is employed to size optimization of scissor-link foldable structures. The advantage of using ACO lies in the fact that the discrete spaces can be optimized with no complexity. The algorithm selects the optimum cross-sections from the available sections list. Elastic behavior is assumed for the formulation of the problem. In addition to strength constraints, the displacement constraints are considered for design. Here, the displacement method is used for analysis employing a special 3-node beam known as a uniplet. Two design examples are presented to demonstrate the performance of the algorithm.     

Improved ant colony optimization algorithms for determining project critical paths

In project management, a project can be represented as a network in two ways; namely, activity-on-arc (AoA) and activity-on-node (AoN). Two recent papers have shown that ant colony optimization (ACO) could find critical path(s) in projects represented as AoA networks. This paper points out that the number and placement of logical dummy activities associated with AoA-based networks can pose serious problems. To get around the problems, an ACO technique based on AoN networks is then proposed. For comparison, the two existing AoA-based ACO algorithms were reproduced and modified into AoN-based algorithms. Moreover, the proposed ACO algorithm was applied to AoA networks as well. All six algorithms were tested with several benchmark problems. The test results strongly indicate that AoN-based ACO algorithms are more effective and efficient in finding critical paths than AoA-based algorithms.