A Discrete Moth-Flame Optimization with an l2-norm Constraint for Network Clustering

Xianghua Li, Xin Qi, Xingjiang Liu, Chao Gao*, Zhen Wang*, Fan Zhang, Jiming Liu

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

3 Citations (Scopus)


Complex network clustering problems have been gained great popularity and widespread researches recently, and plentiful optimization algorithms are aimed at this problem. Among these methods, the optimizating methods aiming at multiple objectives can break the limitations (e.g., instability) of those optimizing single objective. However, one shortcoming stands out that these methods cannot balance the exploration and exploitation well. In another sentence, it fails to optimize solutions on the basis of the good solutions obtained so far. Inspired by nature, a new optimized method, named multi-objective discrete moth-flame optimization (DMFO) method is proposed to achieve such a tradeoff. Specifically, we redefine the simple flame generation (SFG) and the spiral flight search (SFS) processes with network topology structure to balance exploration and exploitation. Moreover, we present the DMFO in detail utilizing a Tchebycheff decomposition method with an l2-norm constraint on the direction vector (2-Tch). Besides that, experiments are taken on both synthetic and real-world networks and the results demonstrate the high efficiency and promises of our DMFO when tackling dividing complex networks.

Original languageEnglish
Pages (from-to)1776-1788
Number of pages13
JournalIEEE Transactions on Network Science and Engineering
Issue number3
Early online date23 Feb 2022
Publication statusPublished - May 2022

Scopus Subject Areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Computer Networks and Communications

User-Defined Keywords

  • decomposition
  • discrete moth-flame optimization
  • multi-objective optimization
  • Network clustering


Dive into the research topics of 'A Discrete Moth-Flame Optimization with an l2-norm Constraint for Network Clustering'. Together they form a unique fingerprint.

Cite this