改进的蚁群算法及其在桁架结构优化中的应用

蚁群算法是优化领域中的一种新型模拟进化算法,具有很强的搜索较优解的能力,其缺点是搜索时间长、容易出现停滞现象。引用局部搜索能力较强的模拟退火算法对其改进,使其跳出局部最优,发现更高质量解。并将其成功应用在25杆桁架中,结果表明,基于模拟退火的改进蚁群算法是有效可行的,是解决组合优化问题的有效方法。     

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。     

改进的蚁群算法在TSP问题中的应用

应用基本蚁群算法来求解TSP问题,在算法中引入去交叉策略,降低其计算复杂度,仿真算例表明,该方法能有效加快局部搜索能力,加快了大规模TSP问题的求解。     

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。     

基于蚁群算法优化工程项目中的工期——成本

当前我国建筑业的总体水平较发达国家相对落后,存在较大差距,如施工技术落后、施工规范不合理、建筑市场过于饱和等。尤其是在施工阶段经常面临工期延长、总造价过高、质量差等问题,这些问题严重影响到企业经济效益的提高,因此如何有效地进行工程施工组织设计,从而控制施工进度和降低成本,成为摆在众多建筑施工单位面前的一个重要问题。针对上述问题,文章提出一种适用于施工工程的多目标工期-成本的蚁群算法。以工程实例对该算法进行验证,表明蚁群算法在工期成本优化方面具有显著效果,为施工管理者进行最优方案选择提供了强有力的手段。     

桁架结构优化设计的改进蚁群算法

通过对已有的优化方法进行分析,针对蚁群算法容易收敛到局部最优解的缺陷,通过遗传算法和禁忌算法来提高增加蚁群算法的全局优化能力,并改进了算法的灵活性和扩展性;将改进的蚁群算法应用到桁架结构优化设计中,提出了桁架结构优化设计的改进蚁群算法,并建立了相应的优化模型;最后,对10杆平面桁架的优化进行了研究和分析,结果表明,提出的改进蚁群算法是科学可行的。     

改进的蚁群算法

蚁群算法依靠其良好的正反馈机制,在对称旅行商问题上取得了成功,使得算法引起了广泛的关注。其通过信息素的聚集来达到局部搜索,但随着搜索的进行某条路径上的信息素越来越多,将会造成搜索停止,陷入停滞。本文通过将信息素的高低进行限制以及遗传算法的加入增强了全局搜索功能,同时增加了最优路径临域内的搜索,使得搜索加快,便于最优路径的寻找。最终,通过十杆件进行验证,证明算法能在较短时间内取得较好的效果。     

基于改进蚁群算法的钢管混凝土构件的优化

钢管混凝土结构综合了钢筋混凝土和钢结构的优点,近年来成为我国重要的建筑结构形式之一,在未来也具有广阔的推广前景。本文以纯弯、轴压构件为例,应用改进蚁群算法MATLAB程序建立构件的优化模型。充分利用蚁群算法较强的分步并行和自适应搜索能力,从而跳过了钢管与套箍混凝土之间复杂作用机理的分析。通过对函数的选取和参数的控制,在设计要求范围内对钢管混凝土构件进行了优化,为以后的设计提供了一定的参考。     

基于改进蚁群算法的室内疏散路径优化

针对传统蚁群算法在解决室内疏散问题时存在收敛速度慢、容易陷入局部最优的缺陷问题,将火场的动态参数引入到蚁群算法中,对其路径选择策略、启发函数和信息素更新策略进行改进,为整个疏散群体求解更优的疏散路径.运用改进的蚁群算法对室内人员的疏散路径进行动态规划,考虑了路径的实时拥挤度,避免了疏散人员局部实现路径优化的瓶颈效应.将...     

基于蚁群算法的水资源优化配置

利用蚁群算法的全局搜索最优特性,提出了求解水资源优化配置问题的方法,并详细论述了水资源优化配置中蚁群算法的实现过程,最后结合实际水源和用户情况,计算出在不同保证率下的用水优化分配方案,以供参考。     

Optimal transient network rehabilitation using multi-objective ant colony optimization algorithm

ABSTRACT

In this paper, the problem of optimal rehabilitation of water distribution networks (WDNs) for both steady and transient state is investigated. Two objectives are considered in this study: minimizing rehabilitation cost by considering pipe size diameters as decision variables and minimizing the transient impacts by minimizing a surge damage potential factor (SDPF). A multi-objective ant colony optimization (MOACO) model is developed to solve this problem. The developed model is verified using the well-known New York City tunnel network. The application is carried out to modify an existing real WDN in New Cairo district designed in steady-state conditions to cope with the transient condition. The advantage of this study is to produce a set of solutions that help decision makers to identify the suitable approach, while evaluating and aggregating the preferences over multiple objective functions.     

蚁群算法在雨水管道系统优化设计中的新尝试

引入一种新的优化设计方法——蚁群算法,并探讨将该法应用于雨水管道系统的优化设计.通过实例说明蚁群算法的过程,并与遗传算法进行比较,表明蚁群算法在给排水管网设计中有较为广阔的应用前景.     

Design of tall bridge piers by ant colony optimization

This paper describes a methodology for the analysis and design of Reinforced Concrete (RC) tall bridge piers with hollow rectangular sections, which are typically used in deep valley bridge viaducts. Piers are usually considered tall when the shaft has a height of 50 m or more. Three different types of rectangular hollow tall piers have been studied for road piers of 90.00 m in height: RTRA90, RLON90 and RLT90. RTRA90 has the two side walls inclined, RLON90 has the two frontal walls inclined and RLT90 has all four walls inclined. The procedure used in the present study to solve the combinatorial problem is a variant of the ant colony optimization. RTRA90 leads to the most economical pier, both in column and foundation cost, since it is the most efficient set up for horizontal loads. Regarding the cost of the vertical column only, i.e. excluding the foundation, the cost of RTRA90 and RLON90 are similar, but the cost of the column RLT90 is higher due to its larger unit cost of interior formwork.     

A particle swarm ant colony optimization for truss structures with discrete variables

In this paper, a particle swarm optimizer with passive congregation (PSOPC), ant colony optimization (ACO) and harmony search scheme (HS) are combined to reach to an efficient algorithm, called discrete heuristic particle swarm ant colony optimization (DHPSACO). This method is then employed to optimize truss structures with discrete variables. The DHPSACO applies a PSOPC for global optimization and the ant colony approach for local search, similar to its continuous version. The problem-specific constraints are handled using a modified feasible-based mechanism, and the harmony search scheme is employed to deal with variable constraints. Some design examples are tested using the new method and their results are compared to those of PSO, PSOPC and HPSO algorithms to demonstrate the effectiveness of the present method.     

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.     

基于蚁群算法的混凝土运输车辆调度方案

提出了基于蚁群算法的混凝土运输车辆调度方案,以司机利益均等和运输花费最低为目标进行建模并求解.车辆调度模型包括任务选择模型和车辆选择模型两部分.基于蚁群算法的任务选择模型,充分运用了信息素的积累来表示当前任务的紧急状况及完成情况,找到了任务派遣的智能解决方案.同时,给出实际的案例证明该方案的可行性.最后,提出了混凝土运输车辆调度智能算法的下一步发展方向.     

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.     

非线性最小二乘估计的蚁群遗传融合算法

基于对遗传算法和蚁群算法的分析,提出把蚁群算法和遗传算法相结合的混合算法,有效避免了两种算法的不足之处,并将该混合遗传算法用于非线性最小二乘参数估计中,算例验证了该算法的可行性和有效,性。     

粒子群-鱼群混合算法在桁架结构优化设计中的应用

群智能算法由于其优异的搜索性能被广泛应用于结构优化设计,人工鱼群算法和粒子群算法都是基于动物群体行为的智能优化随机算法.本文介绍了人工鱼群算法和粒子群算法的基本原理,并提出了粒子群和鱼群杂交混合的一种新方法:粒子群-鱼群混合算法,将粒子群-鱼群混合算法应用到四个桁架结构的重量优化设计,包括平面桁架结构和空间桁架结构,通过比较粒子群-鱼群混合算法、人工鱼群算法、粒子群算法的优化结果,发现改进的粒子群-鱼群混合算法具有收敛精度高、收敛速度快等特点,同时具有较好的稳定性,可用于结构优化设计.