复杂网络中群体检测的甲虫天线搜索算法

Liefa Liao, Fan Zhang
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引用次数: 3

摘要

社区检测可以自然地将网络分割成更小的部分,简化网络分析。本文提出了一种基于甲虫天线搜索算法(BAS)的群落检测算法,该算法采用了一种新的优化算法,计算过程更简单,参数更少。甲虫触角搜索(BAS)是一种受甲虫觅食行为启发的高效元启发式算法。其优点是计算量小,优化速度快,易于实现。甲虫通过头上的左右触角来感知食物气味的强度,从而找到食物的位置。针对BAS算法,重新设计了社团检测的编码策略。我们以模块密度测度作为优化目标,提高了分辨率极限。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Beetle Antennae Search Algorithm for Community Detection in Complex Network
Community detection can naturally divide the network into smaller parts to simplify network analysis. This paper proposes a new algorithm, community detection based on Beetle Antennae Search Algorithm (BAS), which employs a novel optimization algorithm with a simpler calculation process and fewer parameters. Beetle antennae search (BAS) is an efficient metaheuristic algorithm inspired by the foraging behaviors of beetles. The advantages include a small amount of calculation, fast optimization speed, and easy to implement. The beetle gets food location by using the left and right antennae on its head to sense the intensity of food odor. The encoding strategy for community detection has been redesign for the BAS algorithm. We take the module density measure as an optimization goal, which improves the resolution limit.
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