北斗/INS组合导航中人工鱼群粒子滤波的应用

为解决粒子滤波中的粒子退化和枯竭问题,提出一种动态人工鱼群粒子滤波算法,该算法在粒子滤波重采样过程中引入人工鱼群算法的觅食和聚群行为,并依据概率密度的动态比值动态调整人工鱼的移动步长,此算法提升了粒子的多样性,克服了粒子退化及枯竭问题;推动粒子向优选区域逼近,并提高了粒子的全局搜索能力,避免粒子陷入局部最优。将改进的动态人工鱼群粒子滤波在北斗/INS紧组合的模型上进行应用,并通过仿真与人工鱼群粒子滤波及标准粒子滤波算法PF相比较。仿真结果表明,动态人工鱼群粒子滤波可显著提高估算精度,从而为在利用北斗和INS在紧组合导航时提供了新的方法。     

求解0/1背包问题的人工鱼群算法

本文将人工鱼群算法应用于解决0/1背包问题,利用人工鱼群算法的求解思想,对0-1背包问题进行了详细的分析,通过仿真实验与文献中的算法进行了比较,结果表明,本文的人工鱼群算法得到的最优解和平均解都要优于文献中的算法。     

动态自适应人工鱼群算法研究

针对标准人工鱼群算法解精度不高的缺点,文章提出了动态自适应人工鱼群算法,使人工鱼群算法的两个重要参数—步长和视野实现动态自适应调整,使鱼群中的个体在早期具有较大的步长和较宽阔的视野,以保证算法具有较快的收敛速度和较广阔的搜索范围,而在后期逐渐转入精细的局部搜索,使算法的解精度得以提高。并通过实验对比证明动态自适应人工鱼群算法的解精度优于标准人工鱼群算法。     

混合粒子群算法在动态车间调度中的应用

车间调度问题是广泛存在于现实生活中的经典算法规划问题。好的生产调度系统有利于提高企业工作效率及降低企业成本,是工业生产的核心竞争力。粒子群算法因为强大的智能规划能力而被广泛用于车间调度问题当中。文章在原有标准粒子群算法基础上,引入模拟退火机制及遗传算法中交叉变异策略形成的混合粒子群优化算法,并在更具有实际生产环境的动态车间调度中模拟应用,与遗传算法、离散粒子群算法进行比较,具有较强优势。     

改进捕鱼算法求解柔性作业车间调度问题

传统基于精确算法求解柔性作业车间调度问题时,仅能对小量柔性作业车间调度问题实施求解,具有一定的局限性。针对该问题,采用改进捕鱼算法求解柔性作业车间调度问题,在分析经典捕鱼算法存在弊端的基础上,提出改进捕鱼算法,融入渔夫的自身感知性能以及捕鱼经验,分析鱼浓度高的区域,并不断趋向该区域区间,通过概率分布原理对渔夫撒网方案实施优化。分析求解柔性作业车间调度问题的描述以及性能指标,将性能指标作为改进捕鱼算法的输入,通过运算获取最佳的调度结果。实验结果说明,所提算法具有较高的调度效率和精度,并且确保作业车间能耗的最小化。     

求解Logit随机用户均衡问题的改进人工鱼群算法

针对人工鱼群算法求解大型优化问题时存在探索能力差以及搜索盲目性大的缺点,设计一种定向搜索变异的改进人工鱼群算法,该算法在迭代过程中不仅保证鱼群在当前状态下能够自适应变异,并且还可以使其向当前最优位置移动。随后将这种改进人工鱼群算法应用于求解Logit随机用户均衡问题,构建了随机用户均衡交通分配问题新的模型和求解方法。仿真结果表明,该方法具有较好的稳定性和收敛速度,具有在大型城市交通分配问题中应用的潜力。     

基于二维熵的人工鱼群材料图像分割方法

阈值法是图像分割中的重要方法,并在图像处理中得到了广泛的应用。针对电子扫描显微镜(SEM)摄取的纤维材料图像的自身特性,在预处理的基础上,提出了一种基于二维灰度直方图的人工鱼群图像分割方法。二维直方图的阈值的选取,是一个求全局最优的优化问题,本文将人工鱼群的算法应用于图像分割中,利用人工鱼群算法寻求二维熵的最优值,在实验中,人工鱼群算法收敛速度快,结果稳定,取得了理想的效果。     

基于粒子群算法的车间作业调度问题

通过对车间调度问题的描述,针对传统算法寻优效率低的弱点,提出了一种基于粒子群算法的车间作业调度问题的解决方案.对粒子群算法的基本原理进行了阐述,并对粒子群算法的编码、参数的选择以及解码进行了研究,以最小化最大流程时间作为评价算法的性能指标,将其用于编程求解典型调度问题.仿真结果表明,粒子群算法在求解车间作业调度的应用上是十分有效的.     

浅谈企业车间MES生产调度的设计分析

企业车间采用MES(制造执行系统)进行生产调度,能够缩短产品生产的周期,降低企业生产的成本。MES生产调度的设计,要符合企业车间的基本需求,完善企业车间的运营环境,改善企业生产的状态,进而体现出MES生产调度设计的作用和重要性。本文主要探讨企业车间MES生产调度设计的相关内容。     

基于动态参数调整人工鱼群算法的电力系统无功优化

目前现行的电力系统无功优化所用的人工智能算法都会存在易局部收敛和收敛速度慢等问题。为了克服上述问题,文中选取人工鱼群算法并对算法中一些参数进行调整,在步长选择上采取变尺度,并引入评价函数,对于视野范围和拥挤度因子上采用动态调整。文中将系统有功网损作为目标函数,利用改进的人工鱼群算法实现电力系统无功优化。通过IEEE-30节点仿真系统计算,证明改进的人工鱼群算法在电力系统无功优化上的可行性和有效性。     

基于直觉模糊集决策的多Agent敏捷车间调度

由于静态调度忽略了实际车间调度的不确定性,而动态调度则不一定能达到车间调度的敏捷性要求,为此,研究了基于直觉模糊集决策的多Agent敏捷车间调度方法。文中提出了具有自治性和全局优化性的多Agent敏捷调度生产控制结构。研究了基于粒子群算法的静态调度方案和基于直觉模糊集决策的合同网方法,并将两者进行结合,形成了新的敏捷车间调度策略。最后,进行了实例分析,结果表明该调度策略在敏捷车间调度问题的应用上是有效的。     

基于风险估计模型的网格任务动态级调度算法

网格中的各种资源的失效是不可避免的,为尽可能减少由于资源失效导致任务执行失败带来的影响,网格的任务调度算法的目标不仅要最小化任务执行的时间,还要兼顾考虑任务在资源上执行失败的风险。提出了Risk—DLS（Dynamic Level Scheduling）算法是将资源的风险估计模型与DLS算法相结合,通过仿真试验与DLS算法相比较,新算法在选择适当的参数的情况下,不仅能够最小化DAG型应用的完成时间,还能提高任务执行的成功率,有效的减少网格环境的不确定性对任务执行的影响。     

Network real-time scheduling algorithm based on multi-feature dynamic priority

Real-time task scheduling system structure and task model were proposed aiming at the network real-time scheduling problem.The task degree of urgency was defined by considering the deadline of task,execution time and interval time between works.The task degree of tightness was proposed based on service-level assurance,according to functional importance of different tasks in the real-time task scheduling system.The thrashing limit for avoiding task switching frequently was acquired through dynamic regulation to task priorities by degree of urgency and degree of tightness,which guaranteed the success rate of tasks execution and utilization ratio of client execution.Test simulation results suggest that the multi-feature dynamic priority scheduling strategy improves the success rate of task scheduling and shorten the average response time,which suggests it has obvious superiority compared with BE and EDF scheduling algorithm.     

Energy-efficient scheduling for multiprocessors

An energy-efficient scheduling algorithm is proposed for parallel tasks in a multiprocessor system. The proposed algorithm utilises the dynamic voltage scaling (DVS) method for low energy consumption and executes tasks in parallel to compensate for the execution delay induced by the DVS method     

Resource scheduling in mobile edge computing using improved ant colony algorithm for space information network

With the development of space information network (SIN), new network applications are emerging. Satellites are not only used for storage and transmission but also gradually used for calculation and analysis, so the demand for resources is increasing. But satellite resources are still limited. Mobile edge computing (MEC) is considered an effective technique to reduce the pressure on satellite resources. To solve the problem of task execution delay caused by limited satellite resources, we designed Space Mobile Edge Computing Network (SMECN) architecture. According to this architecture, we propose a resource scheduling method. First, we decompose the user tasks in SMECN, so that the tasks can be assigned to different servers. An improved ant colony resource scheduling algorithm for SMECN is proposed. The heuristic factors and pheromones of the ant colony algorithm are improved through time and resource constraints, and the roulette algorithm is applied to route selection to avoid falling into the local optimum. We propose a dynamic scheduling algorithm to improve the contract network protocol to cope with the dynamic changes of the SIN and dynamically adjust the task execution to improve the service capability of the SIN. The simulation results show that when the number of tasks reaches 200, the algorithm proposed in this paper takes 17.52% less execution time than the Min-Min algorithm, uses 9.58% less resources than the PSO algorithm, and achieves a resource allocation rate of 91.65%. Finally, introducing dynamic scheduling algorithms can effectively reduce task execution time and improve task availability.     

Operations Management in Automated Semiconductor Manufacturing With Integrated Targeting, Near Real-Time Scheduling, and Dispatching

To help address the need for manufacturing cycle time reduction, Intel has adopted an integrated operations management approach that consists of three key components: targeting, near real-time scheduling, and dispatching. These components work in conjunction to maximize fab efficiency. Combined with fully automated execution, this approach allows Intel to implement a coordinated operational management philosophy at its fabrication facilities. OPSched was developed at Intel to fulfill the requirements of the integrated approach. We look at why this approach is suitable for automated semiconductor manufacturing based on the implementation of the OPSched system at Intel's high-volume manufacturing facilities and the results achieved.      

网格调度的算法研究及改进

优化网格资源调度算法可以提高网格系统执行效率,给任务安排合理的执行顺序和合适的处理器是优化网格资源调度算法需突破的关键技术.文中研究并实现了（Heterogeneous-Earliest-Finish） HEFT[1]算法和新的（Hierarchical Reliability-Driven Scheduling）HRDS算法.采用DAG[2]任务图生成函数,通过对已有HEFT算法进行研究,采用SimGrid为在分布计算环境下进行分布并行应用调度研究提供一个仿真环境,对HRDS算法进行了改进和验证.验证过程中在HRDS算法中加入了可靠性开销作为调度依据,并把算法分为两层调度,本地可靠性驱动调度和全局可靠性驱动调度.两算法的调度结果在SimGrid网格模拟器中仿真调度,仿真成功并且调度结果在可靠性和性能方面HRDS都比HEFT算法要好.     

1553B网络BC的SPN建模与分析

针对1553B网络中BC节点采用不同的实时任务调度算法将影响轮询总线表实时任务实施次数,进而影响到总线网络的吞吐量和数据传输时延,为研究实时任务调度算法对1553B总线网络通信的影响,建立了BC节点采用静态调度算法下的任务集SPN模型,模型分析结果表明固定优先级高的任务使轮询总线表任务不能实时执行,容易被阻塞,影响了总线网络的通信效果,因此BC节点适宜采用动态调度算法。