基于Q学习蚁群算法的热网优化方法

以热网最小年费用作为目标函数,引入基于Q学习规则的蚁群算法,建立了热网优化算法。结合算例,比较了比摩阻算法、模拟退火算法、基于Q学习规则蚁群算法,基于Q学习规则蚁群算法的热网最小年费用最低。     

基于改进的蚁群算法的污水管网优化设计

以城市污水管网为研究对象,在布局一定的前提下建立污水管道优化设计模型,针对以往蚁群算法应用时存在的一些缺点,分别在信息素更新、能见度方面做出改进,并且在蚁群转移概率公式中加入权重机制,以提高算法搜索效率和防止局部最优现象发生.将改进的蚁群算法运用到JZ市老城区局部污水管道优化设计的实例中,并与应用基本蚁群算法的优化设计方案进行比较,结果无论在收敛上或者解的质量上都优于基本蚁群算法,验证了改进的蚁群算法是可行和高效的.     

基于改进蚁群算法在集装箱码头泊位优化中的应用

探讨了蚁群算法在码头泊位优化中的应用问题。以一定时间内所有船舶在港总时间最短作为优化目标,建立泊位优化配置模型。在求解中,利用改进蚁群算法确定了停靠在每个泊位上船舶的作业顺序。然后,在实例场景与最初的先到先服务算法相比,改进的多态蚁群算法在停港时间上有较大的提高。     

用复合蚁群算法确定边坡临界滑动面

阐述了蚁群算法基本原理，针对蚁群算法局部搜索能力不强等问题进行探讨，把单纯形加速度法引入蚁群算法中构成复合蚁群算法，可加强局域空间的探索，通过工程实例进行了验证。计算表明，在边坡临界滑动面确定中，复合蚁群算法对改善搜索效率、确保计算精确度具有明显效果。     

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。     

基于改进的蚁群算法的测试数据自动生成方法

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。     

基于改进蚁群算法的地铁疏散路径优化

为提高地铁应急疏散效率,在基本蚁群算法的基础上对其路径选择策略、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。以西安某地铁站为例,在突发事件情况下,站内人员的数量、密度、事件发生位置等基本参数不变,分别运用基本蚁群算法和改进蚁群算法对其进行验证。研究证实,在解决地铁站乘客疏散路径问题方面,改进蚁群算法较基本蚁群算法不论是在乘客整体疏散效率还是疏散成功率上都有很大提升。     

基于改进ACO算法的建筑疏散路径优化研究

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。     

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。     

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。     

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.     

基于单亲遗传算法的钢筋混凝土梁优化设计

本文首先给出钢筋混凝土矩形截面简支梁在全约束下的优化模型 ,进而给出了简化的数学模型 ,并设计了相应的适应度函数 ,用单亲遗传算法来寻求钢筋混凝土连续梁的满足约束、造价最低的设计方案。算例表明这种方法有很强的全局搜索能力 ,收敛速度快 ,并取得了较好的效果。     

工程实用的遗传算法结构优化设计

针对工程结构设计的特点以及目前遗传算法在大规模设计变量设计中的计算量大、优化结果较差的特点,提出了相应的改进措施,算例表明改进方法是有效的。     

Artificial intelligence algorithms for predicting peak shear strength of clayey soil-geomembrane interfaces and experimental validation

The peak shear strength of clayey soil-geomembrane interfaces is a vital parameter for the design of relevant engineering infrastructure. However, due to the large number of influence factors and the complex action mechanism, accurate prediction of the peak shear strength for clayey soil-geomembrane interfaces is always a challenge. In this paper, a machine learning model was established by combining Mind Evolutionary Algorithm (MEA) and the ensemble algorithm of Adaptive Boosting Algorithm (ADA)-Back Propagation Artificial Neural Network (BPANN) to predict the peak shear strength of clayey soil-geomembrane interfaces based on the results of 623 laboratory interface direct shear experiments. By comparing with the conventional machine learning algorithms, including Particle Swarm Optimisation Algorithm (PSO) and Genetic Algorithm (GA) tuned ADA-BPANN, MEA tuned Support Vector Machine (SVM) and Random Forest (RF), the superior performance of MEA tuned ADA-BPANN has been validated, with higher predicting precision, shorter training time, and the avoidance of local optimum and overfitting. By adopting the proposed novel model, sensitivity analysis was carried out, which indicates that normal pressure has the largest influence on the peak shear strength, followed by geomembrane roughness. Furthermore, an analytical equation was proposed to assess the peak shear strength that allows the usage of machine learning skills for the practitioners with limited machine learning knowledge. The present research highlights the potential of the MEA tuned ADA-BPANN model as a useful tool to assist in preciously estimating the peak shear strength of clayey soil-geomembrane interfaces, which can provide benefits for the design of relevant engineering applications.     

基于遗传算法的冷水机组运行优化策略研究

本文通过采用回归方法获得冷水机组性能系数(COP)与部分负荷率(PLR)的关系,并建立各台冷水机组能耗方程,利用遗传算法求解其最小值,从而获得各冷水机组所承担的最优负荷比率,同时根据某一实际建筑计算了其全年的冷负荷率时间分布情况,在此基础对系统冷水机组全年的运行情况进行了优化,从而达到5.7%的节能效果。     

面向故障诊断的供水管网水压监测点优化布置方法

为解决供水管网故障诊断中准确率不高、经济性不佳的问题,设计了一个基于改进的果蝇优化算法核极限学习机的供水管网故障诊断模型。经验证,该模型具有学习速度快、故障识别率高等优点。以该模型为核心提出了基于果蝇优化算法的供水管网水压监测点优化布置方法。该方法首先利用果蝇优化算法形成多组水压监测点方案,然后用供水管网故障诊断模型计算每种方案的诊断准确率,选择其中诊断准确率最高、经济性最好的方案作为候选最优方案,并以此方案为基础,使用果蝇优化算法不断循环迭代,最终找到故障诊断准确率高且经济性最好的水压监测点布置方案。利用Matlab语言代码对提出供水管网水压监测点优化布置方法进行了编码实现,使用实际管网数据进行了实验,结果表明,所提出的供水管网水压监测点优化布置方法是一个有效的水压监测点优化布置方法。     

基于活动分割的资源优化研究

在假定活动可分割的基础上,针对资源波动问题,提出了一个基于遗传算法的资源优化方法。首先在已知活动工期的基础上,确定项目工期,继而确定关键工序和非关键工序,并利用基于二元表示的遗传算法,对每个可分的非关键活动在其ES（最早开始时间）到LF（最迟结束时间）的范围内进行调整,以优化资源分配。最后引用一个示例,表明了提出的方法在资源优化方面的优越性。     

Optimized flowshop scheduling of multiple production lines for precast production

The current approach in practice to produce the flowshop schedules of precast production is the dispatching rule technique, which does not guarantee optimal schedules. Some researchers have developed models for a single production line to optimize the scheduling, in which Genetic Algorithm (GA) is used to obtain the solution. However the models cannot be used for multiple production lines. Moreover, some optimization constraints and objectives were missing in the models, such as avoiding frequent type change of precast components during production. Both issues hinder their work to be applied to real precast plants. To overcome the problem, this paper proposes a Flowshop Scheduling Model of Multiple production lines for Precast production (MP-FSM) and develops a corresponding optimization approach to facilitate optimized scheduling by using GA. The approach was validated preliminarily by comparing with traditional scheduling approaches. The results demonstrated that optimized schedules can be obtained by using the proposed approach.     

基于Neuman理论获取潜水含水层系统参数的简化方法

本文通过对考虑流速垂直分量和弹性释水的Neuman潜水完整井流模型解析解的数值分析,给出了模型的数值计算过程,并结合加速遗传算法(AGA)获取了潜水含水层系统参数,通过应用实例表明,可以取得很好的参数估算效果,并且对比传统配线法可知:所得结果与配线法结果近似一致;利用参数计算的降深与实测降深的拟合优于配线法;不必分抽水初期、末期分别拟合,避免了配线法初期和末期导水系数不一致的问题;实现了利用Neuman潜水流模型获得参数的自动优选,简化了参数估计过程。