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Description: 在操作系统中，由于进程总数多于处理机，它们必然竞争处理机。进程调度的功能就是按一定策略、动态地把处理机分配给处于就绪队列中的某一进程并使之执行。根据不同的系统设计目标，可有多种选择某一进程的策略。有两种基本的进程调度方式，即剥夺方式(preemptive mode)和非剥夺方式(non-preemptive mode)。前者指就绪队列中一旦有优先级高于现行进程优先级的进程出现时，系统便立即把处理机分配给高优先级的进程。当然，被剥夺了处理机的进程的有关状态和上下文都必须妥善保存以便今后恢复。后者是，一旦处理机分给了某进程，除非该进程的时间片已满或它主动放弃处理机引起进程调度的原因与操作系统的类型有关，大体可归结为以下几种：
(1) 进程运行完毕
(2) 进程提出I/O请求；
(3) 进程执行某种原语操作(如P操作)导致进程阻塞；
(4) 时间片已满；
(5) 可剥夺方式中，就绪队列中某进程的优先级变得高于现行进程；，系统不得以任何理由剥夺该现行进程的处理机。
-the operating system, due process than the total number of processor, and they will compete processor. Process scheduling function is to a certain strategy, Dynamic processor allocation of land to put in place a cohort of the implementation process of making it. According to various system design objectives, can have a wide choice of a process strategy. There are two basic process scheduling, that is deprived of means (preemptive mode) and non-denial mode (non-preempti 've mode). The former refers to ready queue once they have priority over the current process priority class process, the system will immediately processors allocated to high-priority process. Of course, the processors are deprived of the process of the state and context must be properly preserved for future restoration.
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Size: 235735 |
Author: 李明 |
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Description: 非常好用的ppt模板，主要可用于教育教学课件制作、公司简介演示等
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Size: 1038336 |
Author: 曾晖 |
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Description: 结合多智能体的学习、协调策略及粒子群算法，提出了一种基于多智能体粒子群优化的配电网络重构方法。该方法采用粒子群算法的拓扑结构来构建多智能体的体系结构，在多智能体系统中，每一个粒子作为一个智能体，通过与邻域的智能体竞争、合作。能够更快、更精确地收敛到全局最优解。粒子的更新规则减少了算法不可行解的产生，提高了算法效率。实验结果表明，该方法具有很高的搜索效率和寻优性能。-Combining the study of multi-agent technology，coordinating strategies with P$O，a Multi-Agent Particle Swarm Optimization(MA-PSO)algorithm is presented to handle distribution network reconfiguration problem．It applies Von Neuman architecture
of Particle Swarm Optimization algorithm to the composition of multi-agent system．An agent in MA-PSO
represents a particle to PSO and a candidate solution to the optimization problem．In order to decrease fitness value quickly，agents compete and cooper-ate with their agent of neighboring area．Making use of these agent—agent interactions，MA—PSO realizes the purpose of
minimizing
the value of objective function．The rules of particle renovating reduce unfeasible solution in the process
of particle renovating，which raises the algorithm efficiency satty．The experiment results indicate the
prominent efficiency and significant global optima searching performance of MS—PSO．
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Size: 515072 |
Author: yirufang |
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