共查询到20条相似文献,搜索用时 250 毫秒
1.
首先将基于排序的路径选择方法引入基本蚁群算法 ,并用之于连续变量的优化问题和边坡的最小安全系数搜索 ,结果发现对于设计变量较少的数值优化问题和简单边坡的最小安全系数搜索问题 ,该蚁群算法可以找到全局最优解或比较接近全局最优解。但对于复杂边坡的最小安全系数搜索问题 ,该蚁群算法很容易陷入局部最优。另外复合形法对于不同的初始复合形也会得到不同的最小安全系数 ,利用本文提出的基于最小海明距离的替换准则将蚁群算法得到的局部最优解替换掉初始复形中的一个顶点 ,则复合形法容易找到全局最优 ,成为一种全局搜索能力很强的优化算法。 相似文献
2.
针对大型公共建筑存在的结构复杂、消防疏散困难等问题,提出了用于优化疏散路径的改进蚁群算法。首先,针对基本蚁群算法(ACO)引入Dijkstra 算法,并利用Dijkstra 算法计算出全局性较好的次优路径进而对蚁群算法初始信息素分布情况进行了加强。其次,根据火灾的实时情况改进了蚁群算法的转移概率、更新规则、信息素挥发系数、启发函数等。最后,对改进的蚁群算法进行对比仿真实验。实验结果表明该算法具有较强的全局搜索能力以及较高的搜索效率,能够避免算法进入局部最优陷阱,有效提高消防疏散路径规划效率。 相似文献
3.
《Planning》2019,(13)
本文设计了测试数据自动生成模型,提出一种基于改进的蚁群算法的测试数据自动生成方法。该方法在传统蚁群算法的概率算子中引入相似度影响因子,增加了算法的全局搜索能力。通过三角形判别问题,对改进的算法与传统蚁群算法对比分析。实验结果表明,该算法相较传统蚁群算法具有搜索效率高、全局搜索能力强的特点,在测试数据自动生成问题中具有较强的可行性。 相似文献
4.
混沌扰动启发式蚁群算法及其在边坡非圆弧临界滑动面搜索中的应用 总被引:4,自引:0,他引:4
通过引入混沌扰动算子增加解的多样性和提高全局寻优能力,另外通过构造蚂蚁的启发式搜索方式提高对局部最优解的搜索能力,从而有效地克服了基本蚁群算法容易出现停滞和搜索效率低的缺陷。还利用Spencer法和Janbu法,探讨了所提出的具有混沌扰动算子启发式蚁群算法在边坡稳定性分析中的应用。实例计算和对比分析结果表明,该法有效而又可靠。 相似文献
5.
6.
7.
基于蚁群算法的土石坝土体参数反演 总被引:5,自引:0,他引:5
介绍了新近为求解复杂组合优化问题而提出的蚁群算法。将蚁群算法运用于土石坝土体参数反演问题的求解:先对反演参数的搜索空间进行离散,将参数反演问题转化成一个组合优化问题;再针对土体参数反演问题的特点,改进蚁群算法,并将其用于土体参数的反演计算。算例表明,改进蚁群算法可有效求解土石坝土体参数反演问题。 相似文献
8.
9.
工程结构优化设计是把力学和优化技术有机地结合,根据设计要求,使部分参与计算的量以变量出现,建立结构设计参数与结构重量、最大允许应力等的非线性关系,获得连续域蚁群算法求解结构优化问题所需的目标函数,用连续域蚁群算法进行寻优搜索运算,从而求出所需最优解。算例表明,连续域蚁群算法可求解多维连续优化问题,收敛速度快,且计算精度高,可用于工程结构优化设计。 相似文献
10.
11.
12.
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。 相似文献
13.
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。 相似文献
14.
《Urban Water Journal》2013,10(3):154-173
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. 相似文献
15.
Mohammad Sadegh ES-HAGHI Aydin SHISHEGARAN Timon RABCZUK 《Frontiers of Structural and Civil Engineering》2020,14(5):1110
We propose a new algorithm, named Asymmetric Genetic Algorithm (AGA), for solving optimization problems of steel frames. The AGA consists of a developed penalty function, which helps to find the best generation of the population. The objective function is to minimize the weight of the whole steel structure under the constraint of ultimate loads defined for structural steel buildings by the American Institute of Steel Construction (AISC). Design variables are the cross-sectional areas of elements (beams and columns) that are selected from the sets of side-flange shape steel sections provided by the AISC. The finite element method (FEM) is utilized for analyzing the behavior of steel frames. A 15-storey three-bay steel planar frame is optimized by AGA in this study, which was previously optimized by algorithms such as Particle Swarm Optimization (PSO), Particle Swarm Optimizer with Passive Congregation (PSOPC), Particle Swarm Ant Colony Optimization (HPSACO), Imperialist Competitive Algorithm (ICA), and Charged System Search (CSS). The results of AGA such as total weight of the structure and number of analyses are compared with the results of these algorithms. AGA performs better in comparison to these algorithms with respect to total weight and number of analyses. In addition, five numerical examples are optimized by AGA, Genetic Algorithm (GA), and optimization modules of SAP2000, and the results of them are compared. The results show that AGA can decrease the time of analyses, the number of analyses, and the total weight of the structure. AGA decreases the total weight of regular and irregular steel frame about 11.1% and 26.4% in comparing with the optimized results of SAP2000, respectively. 相似文献
16.
Symeon Christodoulou 《Automation in Construction》2009,18(3):285-293
The paper presents a methodology to arrive at critical path calculations in construction networks by imitating the natural selection processes utilized by real-life ants in search of shortest paths to a food source, and by using Ant Colony Optimization (ACO) algorithms. Ant Colony Optimization is a population-based, artificial multi-agent, general-search technique for the solution of difficult combinatorial problems with its theoretical roots based on the behavior of real ant colonies. The fundamental mathematical background of the ACO method is outlined and a suggested possible implementation strategy is described for solving for longest (critical) paths in construction schedule networks. The ACO methodology should be of interest to both researchers and practitioners since it provides an alternative method to critical path calculations, with a wide range of applications. The described ACO virtual multi-agent approach is supplemented by a sample case study as well as algorithms for the solution of resource-unconstrained construction schedules. 相似文献
17.
18.
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. 相似文献
19.
Eric Forcael Vicente González Francisco Orozco Sergio Vargas Alejandro Pantoja Pablo Moscoso 《Computer-Aided Civil and Infrastructure Engineering》2014,29(10):723-737
Natural disasters such as earthquakes and tsunamis foster the creation of effective evacuation strategies to prevent the loss of human lives. This article proposes a simulation model to find out optimum evacuation routes, during a tsunami using Ant Colony Optimization (ACO) algorithms. ACO is a discrete optimization algorithm inspired by the ability of ants to establish the shortest path from their nest to a food source, and vice versa, using pheromones. The validation of the model was carried out through two drills, which were conducted in the coastal town of Penco, Chile. This town was strongly affected by an 8.8 Mw earthquake and tsunami over February 2010. The first drill was held with minimum information, leaving the population to act randomly and intuitively. The second drill was carried out with information provided by the model, inducing people to use the optimized routes generated by the ACO algorithm. The results showed that, in case of an emergency, conventional evacuation routes showed longer escape times compared to those produced by the model developed in this research. 相似文献
20.
Rahul Putha Luca Quadrifoglio Emily Zechman 《Computer-Aided Civil and Infrastructure Engineering》2012,27(1):14-28
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. 相似文献