基于禁忌搜索的多处理器任务调度算法

为合理利用多处理器资源,对任务调度算法进行研究,针对现有任务调度算法在任务规模较大的情况下全局寻优能力方面的不足,提出基于禁忌搜索的多处理器任务调度算法。对任务图不设任何约束条件,利用基于任务复制的TDS算法产生高质量的初始调度以降低算法复杂度,利用禁忌搜索算法全局寻优得到最优调度。实验结果表明,该算法可以有效降低任务调度长度,减少所需处理器数目。     

基于粒子群优化算法的异构多处理器任务调度

为提高异构多处理器任务调度的执行效率,充分发挥多处理器并行性能,提出一种基于粒子群优化的异构多处理器任务调度算法-PSOASA算法.PSOASA算法以求得任务最短完成时间为目标,首先通过建立新的编码方式和粒子更新公式实现粒子搜索空间到离散空间的映射,使连续的粒子群优化算法适用于离散的异构多处理器任务调度问题,同时通过引入模拟退火算法,克服粒子群算法的“早熟”收敛现象,避免求得的解陷入局部最优.实验结果表明,PSOASA算法的执行效率优于目前广泛采用的遗传算法,有效地降低任务的执行时间,减少了迭代次数,适用于异构多处理器环境大规模任务调度.     

基于粒子群优化的异构多处理器任务调度算法

为提高异构多处理器任务调度的执行效率,充分发挥多处理器并行性能,提出一种基于粒子群优化的异构多处理器任务调度算法——FPSOTTS算法。该算法以求得任务最短完成时间为目标,首先通过建立新的编码方式和粒子更新公式实现粒子搜索空间到离散空间的映射,使连续的粒子群优化算法适用于离散的异构多处理器任务调度问题;同时通过引入禁忌算法进行局部搜索,克服粒子群算法的早熟收敛现象,避免陷入局部最优。实验结果表明,FPSOTTS算法的执行效率优于Min-min算法和遗传算法,有效地降低任务的执行时间。FP-SOTTS算法很好地解决了异构多处理器任务调度问题,并且适合于大规模并行任务调度。     

一种粒子群优化的异构多处理器任务调度算法

为提高异构多处理器任务调度的执行效率,充分发挥多处理器并行性能,提出一种基于粒子群优化的异构多处理器任务调度算法-PSOASA算法.PSOASA算法以求得任务最短完成时间为目标,首先采用整数矩阵对粒子进行编码,并定义交换操作更新粒子状态,实现粒子搜索空间到离散空间的映射,使连续的粒子群优化算法适用于离散的异构多处理器任务调度问题,同时引入模拟退火算法,克服粒子群算法的“早熟”收敛现象,避免求得的解陷入局部最优.实验结果表明,PSOASA算法的执行效率优于目前广泛采用的遗传算法,有效地降低任务执行时间,减少了迭代次数,适用于异构多处理器环境大规模任务调度.     

基于自适应布谷鸟聚类搜索的推荐系统算法的研究

推荐系统本质是一种信息检索技术,能根据用户喜好在海量数据中检索出合适数据推荐给用户,传统推荐系统一般使用协同过滤推荐算法,协同过滤推荐算法主要通过挖掘用户的历史行为数据进行推荐,但传统推荐算法存在着稀疏矩阵、冷启动、实时性等问题困扰[1];因此,本文提出一种基于自适应布谷鸟聚类搜索的改进推荐系统算法,首先对推荐数据进行聚类处理,然后利用布谷鸟算法较强的全局搜索能力,提升推荐系统的准确度,实验结果表明,引入自适应布谷鸟聚类搜索能对传统协同过滤算法在推荐精度、召回率等方面指标方面有一定提高,计算效果优于传统推荐算法。     

基于逐维策略的布谷鸟搜索增强算法

布谷鸟搜索算法迭代运用Lévy Flights随机走动和Biased随机走动发现新个体的各维信息。当个体所有维信息生成后,算法将这些信息合成为个体并评价。在这种情况下,由于个体各维之间存在相互干扰,一些部分维进化的个体可能被放弃,从而影响算法的收敛速度以及求精能力。提出的布谷鸟搜索增强算法采用逐维评价策略接收一些部分进化的个体,可进一步增强算法的收敛速度和求精能力。在算法中,逐维评价策略作为局部搜索技术镶嵌在两个随机走动部件之后,并随机选择一些个体进行逐维更新后进行评价。实验结果说明逐维评价策略总体上能够有效且较好地改善算法的求解性能和收敛速度。     

基于改进布谷鸟搜索的k-means算法的离群点检测

为了解决k-means算法的离群点检测容易受到初始聚类中心的影响陷入局部最优的问题,本文提出一种基于改进布谷鸟搜索的k-means算法的离群点检测方法.首先,对原始布谷鸟搜索算法中的发现概率和莱维飞行步长做自适应策略改进并进行实验仿真;其次讨论改进后的布谷鸟搜索算法的收敛性问题;最后将改进后的布谷鸟搜索算法与k-mea...     

基于离散和声搜索的云计算任务调度研究

云计算任务调度是云计算最重要的问题之一。为解决云计算调度问题,提出一种基于改进和声搜索的调度算法。该算法采用离散形式编码,以总的任务完成时间为优化目标,并对标准和声搜索算法中新和声产生方式进行了改进。最后,在Cloud Sim平台上进行了仿真实验。实验结果表明,新提出的算法具有较好的调度性能。     

A Bipartite Genetic Algorithm for Multi-processor Task Scheduling

Until now, several methods have been presented to optimally solve the multiprocessor task scheduling problem that is an NP-hard one. In this paper, a genetic-based algorithm has been presented to solve this problem with better results in comparison with related methods. The proposed method is a bipartite algorithm in a way that each part is based on different genetic schemes, such as genome presentation and genetic operators. In the first part, it uses a genetic method to find an adequate sequence of tasks and in the second one, it finds the best match processors. To evaluate the proposed method, we applied it on several benchmarks and the results were compared with well known algorithms. The experimental results were satisfactory and in most cases the presented method had a better makespan with at least 10% less iterations compared to related works.     

异构计算系统的任务调度算法SMT=GA

给出一种对异构计算系统进行任务映射与调度的遗传算法－ＳＭＴ－ＧＡ算法。首先对ＨＣＳ任务调度问题作出形式描述,然后分别介绍ＳＭＴ－ＧＡ算法的总体框架,染色体设计,从染色体获得调度方案的方法,染色体适合度函数设计,交叉与变异遗传算子设计等。     

改进布谷鸟搜索算法在多机器人任务分配及路径规划中的应用

针对多机器人任务分配及路径规划问题,提出一种改进布谷鸟搜索算法求解多机器人任务分配及路径规划方法.根据任务点的环境信息和在其中寻找最佳机器人位置建立数学模型,并使用改进布谷鸟搜索算法求解任务分配及路径规划.改进的策略中融合了遗传算子、2-opt、模拟退火算法的Metropolis准则和插入、交换、逆序方法.不同规模的仿...     

一种基于PGRM结构的负载分布策略

本文首先介绍一种基于并行图重写模型的并行图重写机ＰＧＲＭ。在此基础上提出一种新的任务分布策略。目标是以基于多线程的负载分布策略，减少系统的通讯开销，缩短响应时间并且使派生的任务有合理的计算粒度。本文提出的策略是一种融合分布式与集中式两种控制机制的一种混合策略。实验测试结果表明这种分布策略在多ＴＲＡＮＳＰＵＴＥＲ构成的分布存储多机系统上具有高效性和实用性。     

基于捕食搜索策略的网格任务调度算法

为解决网格任务调度难题,设计了一种模仿动物捕食策略的网格任务调度算法SAPS(Scheduling Algorithm Based onPredatory Search)。该算法首先确定待搜索区域,将待搜索区域划分为若干子区域,然后对子区域依次进行区域搜索,在搜索过程中如在某个子区域发现较优解,则对此子区域进行精密搜索,如未发现较优解,则转到下一个子区域,直至遍历所有子区域。SAPS算法具有较好的全局搜索和局部搜索的能力,克服了Min-min算法单纯追求局部最优而缺少全局意识的缺点。试验结果表明,该算法能更有效地解决网格任务调度问题。     

Benchmark-problem instances for static scheduling of task graphs with communication delays on homogeneous multiprocessor systems

Scheduling program tasks on processors is at the core of the efficient use of multiprocessor systems. Most task-scheduling problems are known to be NP-Hard and, thus, heuristics are the method of choice in all but the simplest cases. The utilization of acknowledged sets of benchmark-problem instances is essential for the correct comparison and analysis of heuristics. Yet, such sets are not available for several important classes of scheduling problems, including multiprocessor scheduling problem with communication delays (MSPCD) where one is interested in scheduling dependent tasks onto homogeneous multiprocessor systems, with processors connected in an arbitrary way, while explicitly accounting for the time required to transfer data between tasks allocated to different processors. We propose test-problem instances for the MSPCD that are representative in terms of number of processors, type of multiprocessor architecture, number of tasks to be scheduled, and task graph characteristics (task execution times, communication costs, and density of dependencies between tasks). Moreover, we define our task-graph generators in a way appropriate to ensure that the corresponding problem instances obey the theoretical principles recently proposed in the literature.     

Contention-aware scheduling with task duplication

Finding an efficient schedule for a task graph on several processors is a trade-off between maximising concurrency and minimising interprocessor communication. Task duplication is a technique that has been employed to reduce or avoid interprocessor communication. Certain tasks are duplicated on several processors to produce the data locally and avoid the communication among processors. Most of the algorithms using task duplication have been proposed for the classic scheduling model, which allows concurrent communication and ignores contention for communication resources. It is increasingly recognised that this classic model is unrealistic and does not permit creating accurate and efficient schedules. The recently proposed contention model introduces contention awareness into task scheduling by assigning the edges of the task graph to the links of the communication network. It is intuitive that scheduling under such a model benefits even more from task duplication, yet no such algorithm has been proposed as it is not trivial to duplicate tasks under the contention model. This paper proposes a contention-aware task duplication scheduling algorithm. We investigate the fundamentals for task duplication in the contention model and propose an algorithm that is based on state-of-the-art techniques found in task duplication and contention-aware algorithms. An extensive experimental evaluation demonstrates the significant improvements to the speedup of the produced schedules.     

多机相关任务的相关矩阵调度算法

本文讨论了多机相关任务的调度问题,从时间和空间两方面考虑,提出了一种新的多机相关任务的调度算法－多机相关任务的相关矩阵调度算法（ＲＭＳＡ）。利用可变的相关矩阵Ｍｕ,表示任务的空间需求与处理机的局部存储空间的关系以及任务分配的状态。实验表明此算法具有较短的调度长度,并且具有较好的时间均衡性和空间协调性。     

CPU的多层次并行调度优化模型仿真

通过CPU多调度模式优化,提高CPU运行效率.由于海量数据进行运算的过程中,存在调试不均衡的问题,传统的CPU调度模型不能很好的均衡所有的调度任务关系,无法满足数据运算的实际需求,导致CPU负载不均衡,降低了调度效率.提出基于二叉树搜索算法的CPU多层次并行调度方法.针对CPU中不同层次的任务量进行预测,建立多层次并行调度模型,实现海量调度任务的多层次并行调度.在每个层次中,进行二叉树搜索,完成各个层次独立的CPU任务调度.将二叉树搜索方法运用到多层次并行调度模型中,完成CPU的多层次并行调度.实验结果表明,利用改进算法进行CPU多层次并行调度,能够提高调度效率,缩短调度时间,完成CPU合理调度,保证CPU的运算速率.     

Multi-skilled project scheduling with level-dependent rework risk; three multi-objective mechanisms based on cuckoo search

In many projects, multi-skilled workforces are able to perform different tasks with different quality levels. In this paper, a real-life version of the multi-skilled resource constrained project scheduling problem is investigated, in which the reworking risk of each activity depends on the assigned level of multi-skilled workforces. The problem is formulated mathematically as a bi-objective optimization model to minimize total costs of processing the activities and to minimize reworking risks of the activities, concurrently. In order to solve the resulting problem, three cuckoo-search-based multi-objective mechanisms are developed based on non-dominance sorting genetic algorithm, multi-objective particle swarm and multi-objective invasive weeds optimization algorithm. The parameters of the algorithms are tuned using the Taguchi method to improve the efficiency of the solution procedures. Furthermore, a competitive multi-objective invasive weeds optimization algorithm is used to evaluate the performance of the proposed methodologies. Finally, a priority based method is employed to compare the proposed algorithms in terms of different metrics.