多Agent系统任务分配的研究

针对多Agent系统中存在的任务分配问题,提出一种任务与Agent之间多对多的分配模式,建立相应的任务分配模型,定义任务分配中的性能影响因子,并推导出进行任务分配优化的目标函数,通过一个实际案例,验证该方法的有效性。     

基于DFS的多Agent动态任务分配算法

针对任务分配算法应用于不确定动态环境时存在的不足,研究具有动态模糊特性的任务环境,借助动态模糊集理论,给出相关的多Agent动态任务分配算法并进行实例测试。测试结果表明,该算法模型可以合理地模拟MAS系统中任务分配的运行过程,并获得最优的任务分配策略与良好的任务实现效果。     

多Agent系统中基于改进合同网模型的任务分配研究

任务分配是多Agent系统中研究的热.奴,合同网模型是关于多Agent系统中任务分配的经典策略,但传统的合同网模型存在很多不足。在引入基于信任度的招标策略和基于自适应的投标策略的基础上,主要针对传统合同网模型中标阶段存在的问题,从投标Agent的负载、能力和信任度3个方面进行综合考虑和权衡,提出了一种基于多属性评价中标策略的动态任务分配算法,从而有效地提高了任务分配和执行的效率。最后通过仿真实验验证了基于多属性评价中标策略的正确性和合理性。     

基于混合遗传蚁群算法的多Agent动态任务分配研究

在多Agent系统中,由于任务的复杂性和Agent之间的异构,Agent的动态任务分配问题实际上是一个NP难优化问题。针对MAS的任务分配问题的动态特性,首先建立任务分配数学模型,建立任务分配优化的目标函数;其次提出了一种混合遗传蚁群算法。利用遗传算法快速迭代和蚁群算法正反馈信息、分布式求解的特点实现任务分配的组合优化。实验仿真的结果分析表明,该算法具备较好的全局收敛效率和求解精度,可明显提升多Agent系统的性能。     

网络结构化多Agent系统的任务分配

网络结构化多Agent系统既包括系统运行的底层物理网络,还包括Agent之间的交互网络。传统的任务分配方式并没有深入考虑到网络结构化的特点。文中首先论述网络结构化多Agent系统中任务分配的特点,介绍和分析基于底层网络拓扑与资源分布的任务分配方式、基于Agent交互网络与资源分布的任务分配方式和基于综合网络情境资源的任务分配方式。然后对相关工作进行综述,并与网络结构化多Agent系统任务分配模型进行比较分析。最后论述该方向的难点和未来要解决的问题。     

一种基于多Agent系统的饲料配方优化算法

针对饲料配方问题的特殊性和复杂性,结合多Agent系统的特点,提出了基于多Agent系统的饲料配方优化算法,算法采用多Agent协商的方式对原料变量的取值进行多阶段调整.对于有最优解的问题,算法能够逼近最优解;对于没有最优解的问题,算法能够发现并忽略不可满足或相互冲突的约束,一方面避免了人为设定的不足,另一方面所得到的解优于传统目标规划方法得到的解.     

多Agent层次任务分配方法

提出了一种层次任务分配方法,用于解决动态环境中的任务分配问题.利用全局分配方法为Agent分配合适的任务,当环境发生变换时,通过局部调整来解决任务和Agent之间的匹配问题,使得每个Agent能够根据局部信息选择理想的任务来执行,提高了分配算法的鲁棒性和多Agent整体效用.仿真实验结果表明,该方法是可行且有效的,能够解决动态环境中的任务分配问题.     

基于能力及任务需求的多Agent任务分配方法

针对动态环境中的任务和Agent的动态变化问题,提出一种基于能力及任务需求的层次任务分配方法.利用全局分配方法为Agent分配合适的任务,当环境发生变换时,通过局部调整来解决任务和Agent之间的不适应问题,每个Agent能够根据局部信息选择理想的任务执行,提高了分配算法的鲁棒性.仿真实验表明该方法是可行及有效的,能够解决动态环境中的任务分配问题.     

基于多Agent的运输任务系统的研究

运输任务分配是供应链中一个十分重要的问题。将多Agent技术应用于运输任务分配系统，通过多Agent之间的协商完成运输任务的分配，并利用JavaBean技术为Java提供了基于组件的软件开发体系，开发了一个运输任务分配的原型系统。     

一种优化的多Agent相关任务并行调度算法

讨论了在多Agent系统中多组作业的并行调度问题，提出了一个描述多组作业推进速度的指标——调度效率和一种优化的多Agent相关任务并行调度算法——多Agent相关任务均衡 压缩调度算法(MADTBCSA)。以调度效率作为调度的标准，通过追求多组作业的均衡推进，来达到有效利用Agent时间的目的，同时利用静态压缩算法，进一步压缩调度长度，提高了Agent的利用率。     

An effective iterated greedy algorithm for reliability-oriented task allocation in distributed computing systems

This paper investigates the problem of allocating parallel application tasks to processors in heterogeneous distributed computing systems with the goal of maximizing the system reliability. The problem of finding an optimal task allocation for more than three processors is known to be NP-hard in the strong sense. To deal with this challenging problem, we propose a simple and effective iterative greedy algorithm to find the best possible solution within a reasonable amount of computation time. The algorithm first uses a constructive heuristic to obtain an initial assignment and iteratively improves it in a greedy way. We study the performance of the proposed algorithm over a wide range of parameters including problem size, the ratio of average communication time to average computation time, and task interaction density. The viability and effectiveness of our algorithm is demonstrated by comparing it with recently proposed task allocation algorithms for maximizing system reliability available in the literature.     

基于社区发现的多主体信任评估

为了解决多主体系统(MAS)的开放性、动态性和不确定性所带来的主体信任问题,提出一种基于社区发现的信任评估方法。首先使用G-N算法(GIRVAN M, NEWMAN M E J. Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America, 2002, 99(12):7821-7826)发现系统中的社区结构;然后根据推荐主体的推荐信任分别计算被评估主体的社区内部、外部声誉,进而结合直接信任形成主体的综合信任度;最后根据协作反馈实现主体信任度的动态调整。仿真实验结果表明,基于社区发现的信任评估方法能有效评估主体信任度,通过引入反馈机制能进一步提高交互成功率。     

Optimal task allocation in hypercube multiprocessor ensembles

Criteria are established to determine the optimal policy for allocating a set of uniform tasks onto a multiprocessor hypercube ensemble. It is shown that the optimal policy depends on the ratio of computation to intertask communication required by the distributed program, and that based on this ratio, tasks should be placed either all on one processor or uniformly distributed over the largest possible hypercube.     

基于二层邻居信息的多智能体系统编队控制

为了加快多智能体编队控制过程中的状态收敛,提出基于多跳式网络技术的编队控制方法。首先将多智能体系统（MAS）中的每个智能体之间的相对速度偏移引入到控制协议中,然后引入每个智能体与标准位移之间的位置偏移,最后将多跳式网络技术应用在已定的通信拓扑之中,更多的机体信息被传递,每一个智能体能够联系上的邻居数量更多。利用一个六智能体系统的编队仿真进行验证,仿真结果表明所提出的编队控制方法可以使得多智能体系统形成指定队形,并且与没有引入多跳式网络技术的控制方法相比,其状态收敛所需时间减少了约10 s,收敛效率更高。     

Using task migration to improve non-contiguous processor allocation in NoC-based CMPs

In this paper, a processor allocation mechanism for NoC-based chip multiprocessors is presented. Processor allocation is a well-known problem in parallel computer systems and aims to allocate the processing nodes of a multiprocessor to different tasks of an input application at run time. The proposed mechanism targets optimizing the on-chip communication power/latency and relies on two procedures: processor allocation and task migration. Allocation is done by a fast heuristic algorithm to allocate the free processors to the tasks of an incoming application when a new application begins execution. The task-migration algorithm is activated when some application completes execution and frees up the allocated resources. Task migration uses the recently deallocated processors and tries to rearrange the current tasks in order to find a better mapping for them. The proposed method can also capture the dynamic traffic pattern of the network and perform task migration based on the current communication demands of the tasks. Consequently, task migration adapts the task mapping to the current network status. We adopt a non-contiguous processor allocation strategy in which the tasks of the input application are allowed to be mapped onto disjoint regions (groups of processors) of the network. We then use virtual point-to-point circuits, a state-of-the-art fast on-chip connection designed for network-on-chips, to virtually connect the disjoint regions and make the communication latency/power closer to the values offered by contiguous allocation schemes. The experimental results show considerable improvement over existing allocation mechanisms.     

Decentralized task allocation for surveillance systems with critical tasks

This paper considers the problem of assigning a set of tasks to a set of heterogeneous agents under the additional assumptions that some tasks must be necessarily allocated and therefore are critical for the assignment problem, and that each agent can execute a limited number of tasks. In order to solve this problem in a decentralized way (i.e., without any form of central supervision), we develop an extension of an algorithm proposed in the recent literature. After analyzing convergence and communication requirement of the algorithm, a set of numerical simulations is provided to confirm the effectiveness of the proposed approach.     

改进的蚁群-遗传算法在优化航线中的应用

航线优化是一个重要问题。提出的改进算法是先通过限制、选择和更新信息素、控制周游次数,找出航线的满意解,大大缩短了搜索时间;再用所得较好的航线表示作为初始种群,指定为父体,直接进行分组定界操作,将已得航线进行优化改良,求得最佳航线。实验结果表明,该算法应用于求解航线优化问题行之有效。     

Multi-objective optimization for dynamic task allocation in a multi-robot system

In this paper, we propose a solution to the Multi-Robot Dynamic Task Allocation problem. We use Multi-Objective optimization in order to estimate, and subsequently, make an offer for its assignment. The motivation is to provide a generic solution, independent of the domain, with an aim to better utilize resources such as time or energy. The algorithm provides a significant degree of flexibility, and can be implemented in a number of diverse domains, provided the modeling of the parameters follows the convention presented. For this, we take into account – besides the distance traveled – the efficiency of a robot in a specific task type. The system has been shown to demonstrate scalability, as the experimental results indicate. It is also capable of responding to changes in the environment.     

Decentral task allocation for industrial AGV-systems with resource constraints

Automated Guided Vehicles (AGVs) form a large and important part of the logistics transportation systems in today's industry and are widely used, especially in Europe. Today's AGV-systems offered by global manufacturers almost all operate under some form of centralized control where a single central controller coordinates the entire fleet of AGVs. There is a trend towards decentralized control of these systems where AGVs make individual decisions that promote the flexibility, robustness and scalability of transport. However, its practical implementation seems to be in its infancy. In addition to the lack of practical implementation of decentralized control in industrial AGV-systems, we have observed a research gap in intelligent resource management of AGV-systems, which we have tried to address in previous work by proposing a more intelligent resource management approach. In this paper, we have addressed both the perceived lack of practical decentralized AGV control and the lack of intelligent resource management by proposing a decentralized task allocation algorithm based on sequential single-item auctions, taking into account resource constraints, and in which our intelligent resource management approach from previous work is introduced. We have benchmarked our new approach to a genetic algorithm-based task-allocation solver that uses “threshold-100”-charging as a resource management strategy. The result of the proposal is a decentralized task-allocation architecture under resource constraints that could be used in current AGV-systems to add more decentralized features to the fleet.     

多智能体系统分散式通信决策研究

通信是多智能体系统(MAS)之间协调与协作的最有效和最直接的方法,然而通信的代价却限制了该方法的使用。为了减少MAS协调过程中的通信量,提出一种启发式算法,使Agent仅选择能够改善团队期望回报的观察信息进行通信。实验结果证明,对通信信息的选择能够高效的利用通信带宽,有助于提高系统的性能。