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首页> 外文期刊>International Journal of Computer Networks & Communications >Hybrid Algorithm Based on Ant and Genetic Algorithms for Task Allocation on a Network of Homogeneous Processors
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Hybrid Algorithm Based on Ant and Genetic Algorithms for Task Allocation on a Network of Homogeneous Processors

机译：基于蚂蚁和遗传算法的混合算法在异构处理器网络中的任务分配

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In the field of parallel computing, there is an essential problem which is called Task AllocationProblem(TAP). The task allocation problem (TAP) is a problem where many of tasks require to beallocated to a set of processors.The number of m tasks that is needed to be allocated with number of nprocessors where (m>n) so that the time needed to process all the tasks is minimized. This paper presentsan efficient algorithm (TAP_ACO_GA) to solve the task allocation problem. The proposed algorithm isbased on the idea of Ant Colony Optimization Algorithm(ACO) and the idea of Genetic Algorithm (GA).The proposed algorithm is tested in different dataset and its results are compared with the results in[2]and the results show that TAP_ACO_GA is better than the other algorithm.
机译：在并行计算领域，存在一个基本问题，称为任务分配问题（TAP）。任务分配问题（TAP）是许多任务需要重新分配给一组处理器的问题。需要分配的m个任务的数量与n个处理器的数量（m> n）分配在一起，以便处理所有任务被最小化。本文提出了一种有效的算法（TAP_ACO_GA）来解决任务分配问题。该算法基于蚁群优化算法（ACO）和遗传算法（GA）的思想。该算法在不同的数据集中进行了测试，并与[2]中的结果进行了比较，结果表明： TAP_ACO_GA比其他算法更好。

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