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A Novel Ant Colony Optimization Algorithm with Dynamic Control Population for Community Detecting

  • Jianjun Chen
  • Shupeng Gao
  • Zhen Su
  • Siqi Chen
  • Xianghua LiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)

Abstract

Nowadays, complex networks have driven great interests of scholars. As a special characteristic of a network, the community structure has wide research prospects. Many current algorithms are adopted for detecting the potential community structure, in which the ant colony algorithm is a typical one. However, the computational cost of the ant colony is too high which limits its performance. In this paper, we propose a novel ant colony optimization algorithm with dynamic control population. In the proposed algorithm, when a certain condition is reached, the number of ants starts to decrease based on the proposed rules. The efficiency of the proposed algorithm is estimated through comparing with the classical ant colony algorithm in real-world networks. Experiments show that the proposed algorithm has apparently lower computational cost, while the quality of the division is reserved relatively.

Keywords

Community mining Ant colony optimization Dynamic control population 

Notes

Acknowledgment

This work was supported by National Natural Science Foundation of China (No. 61602391), Natural Science Foundation of Chongqing (No. cstc2018jcyjAX0274), and in part by the National Training Programs of Innovation and Entrepreneurship for Undergraduates.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Jianjun Chen
    • 1
  • Shupeng Gao
    • 2
  • Zhen Su
    • 1
  • Siqi Chen
    • 3
  • Xianghua Li
    • 1
    Email author
  1. 1.College of Computer and Information ScienceSouthwest UniversityChongqingChina
  2. 2.School of Mechanical EngineeringNorthwestern Polytechnical UniversityShanxiChina
  3. 3.College of Intelligence and ComputingTianjin UniversityTianjinChina

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