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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >Community Detection Utilizing a Novel Multi-swarm Fruit Fly Optimization Algorithm with Hill-Climbing Strategy
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Community Detection Utilizing a Novel Multi-swarm Fruit Fly Optimization Algorithm with Hill-Climbing Strategy

机译：利用具有山攀岩策略的新型多群果蝇优化算法的社区检测

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摘要

The community detection methods based on evolutionary algorithm have become a hot research topic in recent years. However, most contemporary evolution-based community detection algorithms need many parameters in the initialization process and are characterized by complicated computational processes, which are puzzled for users to have a better understanding of these parameters on the performance of corresponding algorithm. In this paper, we first propose a new community detection method utilizing multi-swarm fruit fly optimization algorithm (CDMFOA), which needs only a few parameters and has a simple computational process. Moreover, we adopt the multi-swarm fruit fly strategy and hill-climbing method in community detection algorithm in order to resolve the premature convergence and improve the local search ability of CDMFOA. Meanwhile, we separately utilize modularity and modularity density as objective function in the framework of theCDMFOA, named CDMFOA_Q and CDMFOA_D, so as to check their detection abilities and accuracies in partitioning communities of complex networks. The experimental results on synthetic and real-world networks show that CDMFOA can effectively detect community structure in complex networks. Besides, we also demonstrate that the CDMFOA_D performs better thanCDMFOA_Q and other traditional modularity-based methods.
机译：近年来，基于进化算法的社区检测方法已成为一个热门研究课题。然而，大多数当代基于演化的社区检测算法需要初始化过程中的许多参数，并且具有复杂的计算过程，这对于用户来说是更好地理解这些参数的对应算法的性能。在本文中，我们首先提出了一种利用多群果蝇优化算法（CDMFOA）的新社区检测方法，只需要几个参数并具有简单的计算过程。此外，我们采用了群体检测算法中的多群果蝇策略和爬山方法，以解决早产和提高CDMFOA的本地搜索能力。同时，我们将模块化和模块化密度分别用于Thecdmfoa的框架内的目标函数，命名为CDMFOA_Q和CDMFOA_D，以检查复杂网络的分区社区的检测能力和精度。综合和真实网络的实验结果表明，CDMFOA可以有效地检测复杂网络中的社区结构。此外，我们还证明了CDMFOA_D执行更好的基于传统模块化的方法。

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