An Improved Gray Wolf Optimization Algorithm with a Novel Initialization Method for Community Detection

Yan Kang, Zhongming Xu, Haining Wang (), Yanchong Yuan, Xuekun Yang and Kang Pu
Additional contact information
Yan Kang: Software College, Yunnan University, Kunming 650504, China
Zhongming Xu: Software College, Yunnan University, Kunming 650504, China
Haining Wang: Software College, Yunnan University, Kunming 650504, China
Yanchong Yuan: Software College, Yunnan University, Kunming 650504, China
Xuekun Yang: Software College, Yunnan University, Kunming 650504, China
Kang Pu: School of Artificial Intelligence, Hohai University, Nanjing 211100, China

Mathematics, 2022, vol. 10, issue 20, 1-21

Abstract: Community discovery (CD) under complex networks is a hot discussion issue in network science research. Recently, many evolutionary methods have been introduced to detect communities of networks. However, evolutionary optimization-based community discovery still suffers from two problems. First, the initialization population quality of the current evolutionary algorithm is not good, resulting in slow convergence speed, and the final performance needs to be further improved. Another important issue is that current methods of CD have inconsistent network detection performance at different scales, showing a dramatic drop as the network scale increases. To address such issues, this paper proposes an algorithm based on the novel initial method and improved gray wolf optimization (NIGWO) to tackle the above two problems at the same time. In this paper, a novel initialization strategy is proposed to generate a high-quality initial population and greatly accelerate the convergence speed of population evolution. The strategy effectively fused the elite substructure of the community and different features based on the dependency and other features among nodes. Moreover, an improved GWO is presented with two new search strategies. An improved hunting prey stage is proposed to retain the excellent substructures of populations and quickly improve the community structure. Furthermore, new mutation strategies from node level to community level are designed in an improved encircling prey stage. Specifically, boundary nodes are mutated according to a proposed function to improve the search efficiency and save the computation assumption. Numerous experiments have proven our method obtains more excellent performance in most networks compared with 11 state-of-the-art algorithms.

Keywords: swarm intelligence algorithm; community detection; node importance; initial population enhancement (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/20/3805/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/20/3805/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:20:p:3805-:d:943113

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().