耦合优化蚁群算法与P-Median model的选址模型设计

为节省城建部门对于公共体育设施的投入成本以及提高城市人民生活质量,以运动场所优化选址为例,提出一种新型设施选址模型。该模型主要基于P-Median model(最小化阻抗模型)根据需求点数量从全部候选设施选址中选择设施空间位置,让用户达到离自己最近设施距离成本总和最小的目的,对选址的基本原则和实际情况提出要求,构造目标函数用于优化后蚁群算法求解进行选址工作。优化蚁群算法实现基于Python语言模块,通过改进蚁群原始信息素,提升原有算法的收敛速度,求出目标函数最优解,可以很好地模拟对于运动场所的选址。用二者耦合进行优势互补所设计的选址模型来搜寻研究区蚁群信息素浓度残留最大的栅格像元,从而确定未被已有设施点服务半径覆盖的最佳设施点建立位置。实验结果表明,该新型选址模型相较于最小化阻抗模型与最大化覆盖模型,新增优化设施点使整体服务半径覆盖率分别高出10.42%和6.95%,适合求解较为精确且小规模空间下的选址问题。

Evaluation of murine models of permanent focal cerebral ischemia

Background To date murine models of permanent focal cerebral ischemia have not been well characterized. The purposes of this paper were to compare three different permanent middle cerebral artery occlusion (MCAo) models with or without cra niectomy, and to identify an ideal mouse model of permanent focal cerebral ischemia. Methods Experiments were performed on 45 healthy adult male Kunming mice, weighing 28 to 42 g. The animals were randomly assigned to three groups (n=15 in every group) based on surgical procedure: MCAo via the external carotid artery (ECA), MCAo via the common carotid artery (CCA), and direct ligation of the middle cerebral artery (MCA). Each day post-ischemia, the animals were scored using an eight-grade n eurological function scale, and mortality was also recorded. Seven days post-ischemia, the brains were removed for lesion size determination using triphenyltetrazoli um chloride staining. Correlation analysis of lesion volume and neurological score was carried out. Results Mortality in the group receiving direct MCA ligation was lowest among the three groups, and there was a significant difference between the direct MCA ligation group and the two intraluminal occlusion groups (P<0.05). In all groups, neurological scores gradually increased with prolongation of ischemic duration, peaking after two days, then gradually decreasing. In the direct MCA ligation group, however, neurological scores were relatively stable. There was a significant correlation between infarct volume and neurological score 7 days after MCAo in every group (all r>0.7, P<0.05), suggesting good reproducibility of lesion volume in the three groups, but the infarct volume was more constant in the direct MCA ligation group. Conclusion The direct ligation model of MCAo provides an optimal means of studying permanent focal cerebral ischemia, and is preferable to the models using intraluminal sutures.

Research on Data Routing Model Based on Ant Colony Algorithms

Improved traditional ant colony algorithms,a data routing model used to the data remote exchange on WAN was presented.In the model,random heuristic factors were introduced to realize multi-path search.The updating model of pheromone could adjust the pheromone concentration on the optimal path according to path load dynamically to make the system keep load balance.The simulation results show that the improved model has a higher performance on convergence and load balance.

Improved Ant Colony Algorithm for Vehicle Scheduling Problem in Airport Ground Service Support

Support vehicles are part of the main body of airport ground operations,and their scheduling efficiency directly impacts flight delays.A mathematical model is constructed and the responsiveness of support vehicles for current operational demands is proposed to study optimization algorithms for vehicle scheduling.The model is based on the constraint relationship of the initial operation time,time window,and gate position distribution,which gives an improvement to the ant colony algorithm(ACO).The impacts of the improved ACO as used for support vehicle optimization are compared and analyzed.The results show that the scheduling scheme of refueling trucks based on the improved ACO can reduce flight delays caused by refueling operations by 56.87%,indicating the improved ACO can improve support vehicle scheduling.Besides,the improved ACO can jump out of local optima,which can balance the working time of refueling trucks.This research optimizes the scheduling scheme of support vehicles under the existing conditions of airports,which has practical significance to fully utilize ground service resources,improve the efficiency of airport ground operations,and effectively reduce flight delays caused by ground service support.

Monitoring the little fire ant,Wasmannia auropunctata(Roger 1863),in the early stage of its invasion in China:Predicting its geographical distribution pattern under climate change

Invasive alien ants(IAAs)are among the most aggressive,competitive,and widespread invasive alien species(IAS)worldwide.Wasmannia auropunctata,the greatest IAAs threat in the Pacific region and listed in“100 of the world’s worst IAS”,has established itself in many countries and on islands worldwide.Wild populations of W.auropunctata were recently reported in southeastern China,representing a tremendous potential threat to China’s agricultural,economic,environmental,public health,and social well-being.Estimating the potential geographical distribution(PGD)of W.auropunctata in China can illustrate areas that may potentially face invasion risk.Therefore,based on the global distribution records of W.auropunctata and bioclimatic variables,we predicted the geographical distribution pattern of W.auropunctata in China under the effects of climate change using an ensemble model(EM).Our findings showed that artificial neural network(ANN),flexible discriminant analysis(FDA),gradient boosting model(GBM),Random Forest(RF)were more accurate than categorical regression tree analysis(CTA),generalized linear model(GLM),maximum entropy model(MaxEnt)and surface distance envelope(SRE).The mean TSS values of ANN,FDA,GBM,and RF were 0.820,0.810,0.843,and 0.857,respectively,and the mean AUC values were 0.946,0.954,0.968,and 0.979,respectively.The mean TSS and AUC values of EM were 0.882 and 0.972,respectively,indicating that the prediction results with EM were more reliable than those with the single model.The PGD of W.auropunctata in China is mainly located in southern China under current and future climate change.Under climate change,the PGD of W.auropunctata in China will expand to higher-latitude areas.The annual temperature range(bio7)and mean temperature of the warmest quarter(bio10)were the most significant variables affecting the PGD of W.auropunctata in China.The PGD of W.auropunctata in China was mainly attributed to temperature variables,such as the annual temperature range(bio7)and the mean temperature of the warmest quarter(bio10).The populations of W.auropunctata in southern China have broad potential invasion areas.Developing strategies for the early warning,monitoring,prevention,and control of W.auropunctata in southern China requires more attention.

Optimization of Fairhurst-Cook Model for 2-D Wing Cracks Using Ant Colony Optimization (ACO), Particle Swarm Intelligence (PSO), and Genetic Algorithm (GA)

The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the sliding crack or so called, “wing crack” model. Fairhurst-Cook model explains this specific type of failure which starts by a pre-crack and finally breaks the rock by propagating 2-D cracks under uniaxial compression. In this paper, optimization of this model has been considered and the process has been done by a complete sensitivity analysis on the main parameters of the model and excluding the trends of their changes and also their limits and “peak points”. Later on this paper, three artificial intelligence algorithms including Particle Swarm Intelligence (PSO), Ant Colony Optimization (ACO) and genetic algorithm (GA) has been used and compared in order to achieve optimized sets of parameters resulting in near-maximum or near-minimum amounts of wedging forces creating a wing crack.

Design of Cybersecurity Threat Warning Model Based on Ant Colony Algorithm

In this paper,a cybersecurity threat warning model based on ant colony algorithm is designed to strengthen the accuracy of the cybersecurity threat warning model in the warning process and optimize its algorithm structure.Through the ant colony algorithm structure,the local global optimal solution is obtained;and the cybersecurity threat warning index system is established.Next,the above two steps are integrated to build the cybersecurity threat warning model based on ant colony algorithm,and comparative experiment is also designed.The experimental results show that,compared with the traditional qualitative differential game-based cybersecurity threat warning model,the cybersecurity threat warning model based on ant colony algorithm has a higher correct rate in the warning process,and the algorithm program is simpler with higher use value.

机器人人工拣选环境下混流装配线齐套物料配送优化

为了提高汽车制造企业配送的智能化水平、解决混流装配线齐套物料人工拣选效率低、成本高的问题,引入基于齐套物料配送策略的机器人人工拣选模式,通过优化自动拣选机器人、工人的配置数量及配送周期,使包含机器人使用成本、劳动力成本和在制品库存成本在内的总成本最小。为求解该配送优化问题,提出了改进量子蚁群算法,利用量子比特的叠加性增加种群多样性,避免算法陷入局部最优,同时设计了改进量子旋转门更新机制和基于差分进化操作的非最优个体优化策略,提高了算法收敛速度和寻优质量。最后,通过算例分析验证了模型的正确性与算法的有效性,并分析了拣选批量对总成本的影响。

基于改进蚁群算法的机器人全局路径规划

针对传统蚁群算法存在初始信息素缺乏、收敛速度慢以及无法有效躲避障碍物等问题,文中提出了一种基于改进蚁群算法的全局路径规划。引入正态分布函数改进传统启发函数,提高了算法效率,缩短了算法收敛所需时间。自适应调整信息素挥发系数,限定信息素范围,避免过早收敛。对算法路径平滑处理,缩短路径长度,从而实现机器人的全局路径规划。仿真结果表明,在20×20环境下,文中算法平均迭代次数比传统蚁群算法减少了28代,收敛速度更快。平均拐点减少了33.3%,使路径更为平滑,克服了初始信息素缺乏,加快了收敛速度,减少了拐点数量,能够有效躲避环境中的障碍物,证明了该算法的可行性。

基于自动分层分区的通信网络拓扑建模方法

通信系统抗震韧性评估对提升系统整体抗震能力和减轻灾害损失具有重要意义,基于通信系统组网规则快速建立合理的通信网络拓扑能够为抗震韧性评估提供网络拓扑数据,同时为建设经济且稳定的城市通信网络提供指导。针对这一问题提出了一种建立合理通信网络拓扑的方法:在获得通信节点基础信息的前提下,基于通信网络组网规则采用优化算法自动建立给出通信网络拓扑。该方法首先采用聚类算法实现通信节点的自动分区;然后,基于通信线路的拓扑规则,以线路长度最短为优化目标,采用蚁群算法和Dijkstra算法优化建立同一分区内节点间线路拓扑。为了验证该方法的可用性,以两座不同规模城市为例,建立了两座城市通信网络拓扑模型。建模结果显示:针对不同规模城市该方法能够结合具体情况快速建立合理的城市通信网络拓扑模型。

中央空调负荷精细化调控双层优化技术研究

针对当前中央空调系统粗放式调节不能准确释放调控潜力的问题,深入分析中央空调各子系统工作原理与调控方式,提出基于双层优化的中央空调负荷精细化调控技术方案。首先,面向实际工程实践需求建立建筑湿热标度模型;然后,建立以舒适度和经济性优化目标函数为主的上层调控模型,采用Shapley值法对温湿设定值的进行分配;接着,建立以各子系统优化组合能耗最低为优化目标函数的下层调控模型,采用蚁群算法对优化目标函数进行求解;之后,通过求解双层模型,制定包括末端风机、冷冻水泵以及冷水机组等组件的精细化调控策略;最后,通过多个算例验证所提中央空调负荷精细化调控技术方案的正确性与有效性。

基于BIM的PC构件排样优化设计

针对PC构件排样与上游装配式建筑设计模型的协同效用问题,进行了基于BIM的PC构件排样优化设计研究。分析了装配式BIM中PC构件的组成元素,完成PC构件信息的提取;结合改进蚁群算法与动态剩余矩形算法对PC构件排样进行优化,采用遗传变异策略与路径选择机制改进算法后期易陷入局部最优的不足,经过多次优化迭代得出最优排样。某PC构件企业的生产排样实例验证了基于BIM的PC构件排样优化方案可实现上游装配式建筑设计模型与PC构件排样的协同效用,提高了排样效率。

面向人员岸滩行进的三维路径规划算法研究

针对当前无法为人员岸滩行进提供科学合理的路径规划这一问题,论文基于蚁群算法提出了面向岸滩行进的最优路径规划算法。首先对基本的蚁群算法进行了改良,包括路径搜索方式、信息素更新策略和启发函数的合理设计等,改善了算法的收敛效率;然后定量结合多类岸滩场路径规划影响因子,构建了满足岸滩行进的代价函数;最终实现了面向岸滩行进的算法构建。该算法可为实现复杂地形条件下岸滩行进的最优路径解算和基于蚁群算法的相关三维路径规划分析研究提供参考借鉴。

基于改进蚁群算法的邮船舱室模块移运路径规划

针对大型邮船舱室模块运输过程中存在的移运路线长、路线混乱、舱室模块易与障碍物发生碰撞等问题,提出应用加入动态搜索模型的蚁群算法对邮船舱室模块进行路线规划,为运输舱室模块提供清晰、便捷的移运路线。对主竖区的障碍物进行分析,建立模拟实际工况的栅格地图,采用改进蚁群算法寻找移运路径。对不同位置所经过的栅格地图和蚁群数量进行动态调整。采用模拟退火算法寻找蚁群算法的参数。采用离散点分析确定移运路径的主、支通道。仿真试验结果表明,应用改进蚁群算法建立主、支通道进行舱室模块移运可有效提高舱室模块的运输效率。

基于智能预警技术的输电线路在线巡视方法

针对常规技术对输电线路在线巡视效率低下、误差率高的问题,提出了基于智能预警技术的输电线路在线巡视方法,设计一套基于5G通信和无线数据通信技术的输电线路在线智能预警系统,通过ARM+DSP双核处理器实现输电线路故障识别。采用无人机技术实现高空输电线路巡检,通过改进型蚁群算法模型实现输电线路故障检测,并设计了故障检测定位技术,利用融入行波定位技术的蚁群算法模型提高了输电线路在线巡视能力。实验表明,方法定位精确、误检率低,具有一定的实际应用价值。

基于蚁群算法的纯电动汽车路径规划研究

为了缓解纯电动汽车用户出行焦虑,提出一种考虑交通动态性及速度时变性的路径规划方法。根据道路节点位置、海拔高度、充电桩位置等信息建立沈阳市20 km×20 km区域道路拓扑结构,基于车辆充电需求、行驶距离、行驶时间、行驶能耗、附件能耗建立纯电动汽车多目标路径函数,采用蚁群算法开展路径规划。仿真结果表明,本文提出的规划方法能够找到切合实际的目标路径。

基于改进蚁群算法的低碳冷链配送路径优化

针对目前冷链配送路径优化中存在的综合配送成本考虑不全面的问题,根据生鲜配送特点,综合考虑基本运输成本、碳排放、制冷、货损以及时间窗约束,建立以综合配送成本最小化为目标的路径优化模型。提出改进蚁群算法对该优化模型进行求解,在初始阶段使用遗传算法生成初期信息素分布,而后使用蚁群算法进行后续寻优搜索,再引入模拟退火算法的Metropolis准则筛选优质解。通过仿真实验验证了优化模型和改进算法的有效性,对低碳可持续发展理念下生鲜品冷链配送路径优化问题研究具有一定意义,助力冷链运输行业向低碳经济转型。

四川盆地南缘昭通示范区罗布向斜志留系龙马溪组页岩储层裂缝建模

基于岩心分析、成像测井分析和三维地震资料,对四川盆地南缘昭通示范区罗布向斜龙一1亚段和五峰组的多尺度离散裂缝网络建模方法进行了研究。研究结果表明:(1)将蚂蚁体属性中的强响应部分确定性提取成面,可得到大尺度离散裂缝网络模型;用蚂蚁体沿层切片显示的带走向趋势的裂缝参与小尺度离散裂缝网络建模,降低了小尺度裂缝走向模拟的不确定性。(2)罗布向斜龙一1亚段和五峰组裂缝以高角度剪切缝为主,多被方解石充填,裂缝走向为北东—南西向,成像测井解释的裂缝走向与附近地震解释的断层走向一致。(3)大尺度离散裂缝网络模型与钻井的断层位置相符,小尺度裂缝发育部位与钻井液漏失部位吻合,多尺度离散裂缝网络模型可较好地表征罗布向斜龙一1亚段和五峰组的裂缝发育特征。(4)罗布向斜Y12H井应用多尺度离散裂缝网络模型优化压裂方案,压裂缝网复杂度高,试气产量较邻井提高了近41%,优化小尺度裂缝走向后的多尺度离散裂缝网络模型实际应用效果较好。

基于SysML的空间有效载荷测试路径自动生成方法

为简化对空间有效载荷这一复杂系统的集成测试工作,引入基于模型的系统工程(model-based sytems engineering,MBSE)思想,提出一种基于系统建模语言(system modeling language,SysML)的测试路径自动生成方法。所提方法所需的信息全部来源于载荷设备在数字设计阶段所构建的SysML数字模型。首先,对载荷的SysML活动图进行预处理;之后,根据载荷运行特性与活动图特性构建测试路径搜索模型,并以此提出改进蚁群算法以搜索全部测试路径;最后,基于SysML用例图在全部测试路径中进一步进行搜索,从而获取指定功能的测试路径。以空间燃烧科学实验载荷为例展示所提方法的详细过程,并对算法性能进行分析。在200次重复实验中,所提方法所得测试路径的覆盖率达到100%,最大迭代次数为27。实验结果表明,所提方法不会产生大量的无效测试路径,大大提高测试路径规划工作的效率。

基于改进蚁群算法的微气泡减阻气流量智能调控技术研究

[目的]为了提高微气泡减阻技术的实际效能,基于改进蚁群算法开展微气泡减阻气流量智能调控技术研究。[方法]首先,基于微气泡减阻机理,利用自主研发船模样机开展微气泡减阻试验,获得不同航速下的理想最佳气流量;随后,采用改进蚁群算法,开发气流量智能调控技术的软件系统;最后,将智能调控硬件系统应用于船模样机,开展自航模试验以验证该技术的有效性。[结果]所研技术不仅可以有效地调控气流量达到最佳微气泡减阻效果,还可以监控航速变化对气流量进行自适应调控,使船舶在各种航速下均能保持最大减阻。[结论]该技术可以提高微气泡减阻技术的自动化与智能程度,增强微气泡减阻技术的实际效能。