社团数有限的任务网络划分方法

Liang Guo, Yunjun Lu, Qian Liu, Keren Zhu, Lv Zhao
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引用次数: 0

摘要

为了解决当前任务网络划分方法中无法确定社区数量的问题,本文提出了任务支持度的概念和测量方法,并以社区具有较大的内部关联和较小的外部社区关联作为任务网络划分的优化目标。在NSGA-II方法的基础上,引入了任务划分粒度的概念,提出了一种基于NSGA-II的有限社团数任务网络划分方法。最后通过仿真实验验证了该算法的可行性以及与传统方法相比在时间消耗方面的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A method for task network partition with limited community number
In order to solve the problem that the number of communities in the current task network partition method cannot be determined, this paper proposes the concept and measurement method of task support degree, and takes the community to have a large internal relevance and a small external-community relevance as the optimization objective of task network partition. Based on the NSGA-II method, the concept of task partition granularity is introduced, and a task network partition method based on NSGA-II with limited community number is proposed. Finally, simulation experiments verify the feasibility of the algorithm and its advantages in terms of time consumption compared with traditional methods.
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