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Based on Hybrid Particle Swarm Optimization Algorithm Respectively Research on Multiprocessor Task Scheduling

机译:基于混合粒子群优化算法分别研究多处理器任务调度

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Multiprocessor system plays an important role in the computer, in order to improve the parallel computing performance of the system, its essence is to solve a multiprocessor system task scheduling algorithm of NP problem, and the TSP (traveling salesman problem) is a typical NP-complete problem. This article will be attributed to solve multiprocessor task scheduling multiprocessor task scheduling of TSP combination optimization problems. In this article, through the experiment to verify the hybrid particle swarm optimization algorithm and genetic algorithm in solving TSP multiprocessor task scheduling optimization problems, the experimental results show that the hybrid particle swarm optimization algorithm in solving the questions of different size of task scheduling is not only to solve the high quality, and solving the faster, the perform better than genetic algorithm.
机译:多处理器系统在计算机中发挥着重要作用,为了提高系统的并行计算性能,其本质是解决了NP问题的多处理器系统任务调度算法,而TSP(旅行推销员问题)是典型的np-完整的问题。本文将归因于解决TSP组合优化问题的多处理器任务调度多处理器任务调度。在本文中,通过实验来验证混合粒子群优化算法和遗传算法在解决TSP多处理器任务调度优化问题中,实验结果表明,混合粒子群优化算法解决了不同大小的任务调度的问题不是只能解决高质量,并解决比遗传算法更好。

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