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。     

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。     

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...     

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.     

蚁群算法在地铁车站内应急疏散的应用

基于寻找优化路径的蚁群算法,选择地铁站样本,研究地铁爆炸事故发生时人的群体性行为、疏散路径及疏散时间等问题,并在此基础之上,计算得出地铁站样本客流量上限.分析结果显示,发生爆炸事故时,采用蚁群算法进行疏散能够在较短时间内使乘客选择优化的疏散路径,迅速撤离危险区域,到达安全地带.     

The application of the ant colony optimization algorithm to the construction site layout planning problem

A good site layout is vital to ensure the safety of the working environment, and for effective and efficient operations. Moreover, it minimizes travel distance, decreases materials handling, and avoids the obstruction of materials and plant movement. Based on studies in the manufacturing industry, the cost of materials handling could be reduced by 20–60% if an appropriate facility layout is adopted. In designing a site layout, a planner will first position the key facilities that influence the method and sequence of construction, and then assign the remaining facilities in the available space that is left over. This process is similar to the positioning of facilities in the ant colony optimization (ACO) algorithm. The general principle of the ACO algorithm is to assign facilities to a location one by one, and the occupied locations are deleted from the location scope in the next assignment. In the study, ACO algorithm is employed to resolve the construction site layout planning problem in a hypothetical medium‐sized construction project. By applying fuzzy reasoning and the entropy technique, the study calculates the closeness relationship between facilities, in which the optimal site layout is affected by the mutual interaction of facilities.     

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.     

基于扎根理论的生鲜企业O2O平台化影响因素研究

近年来,生鲜电商市场飞速发展,然而受目前生鲜电商所具有的产品时效性强、物流及履约成本高、顾客消费习惯尚未养成等特征影响,加之线上行业整体交易规模仍然偏小,大量生鲜电商平台处于亏损状态。而采用线上线下融合的"新零售"模式,加快生鲜企业O2O平台化进程,有助于降低生鲜企业运营成本,提高企业运营绩效。鉴于此,通过对生鲜企业和消费者进行深度访谈,结合相关统计报告与政策法规,采用扎根理论研究法探究影响生鲜企业O2O平台化的关键因素。研究发现,环境因素、产品因素、平台因素、用户因素是影响生鲜企业平台化的重要因素,分别在其中起驱动、保障、支持和主导作用。只有综合考虑以上四个因素的影响,全面掌握生鲜企业O2O平台化所处的环境,解决好产品质量标准和货源问题,快速增加平台流量,保障消费者权益,才能保证平台持续稳定运营。因此,为推动生鲜企业O2O平台化进程,可从平台服务模式、营销策略、管理方法、信息共享四个方面入手,通过提供增值服务来增强竞争力,通过完善营销策略来提高用户黏性,通过优化管理方法来加强质量监督,通过建立完整的信息流来实现信息共享。     

Stochastic time-cost optimization using non-dominated archiving ant colony approach

This article employs a non-dominated archiving ant colony approach to solve the stochastic time-cost trade-off optimization problem. The model searches for non-dominated solutions considering total duration and total cost of the project as two objectives. In order to expect more realistic outcomes for the time-cost trade-off problem, uncertainties in time and cost of the project should be taken into account. Fuzzy sets theory is used to answer for uncertainties in time and cost of the project. The model embeds the α-cut approach to account for accepted risk level of the project manager. Left and right dominance ranking method is used for finding non-dominated solutions. The ranking method employs decision maker's optimism using β concept. The performance of the model is tested according to performance metrics for multi-objective evolutionary algorithms proposed in the literature. The results show that the algorithm is adequately reliable. A case study is solved to show the application of the proposed model for the uncertain time-cost trade-off problem.     

基于蚁群算法的多机器人环境探索

针对多机器人的环境探索问题,采用了蚁群算法,解决了多机器人的目标分配与环境区域覆盖。通过对蚁群算法和等待拍卖算法在两种不同环境模型的覆盖率的比较,表明了蚁群算法在相同覆盖率的情况下运行次数较少。     

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.