TY - JOUR
T1 - A Discrete Moth-Flame Optimization with an l2-norm Constraint for Network Clustering
AU - Li, Xianghua
AU - Qi, Xin
AU - Liu, Xingjiang
AU - Gao, Chao
AU - Wang, Zhen
AU - Zhang, Fan
AU - Liu, Jiming
N1 - Publisher Copyright:
IEEE
PY - 2022/5
Y1 - 2022/5
N2 - 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.
AB - 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.
KW - decomposition
KW - discrete moth-flame optimization
KW - multi-objective optimization
KW - Network clustering
UR - http://www.scopus.com/inward/record.url?scp=85125357766&partnerID=8YFLogxK
U2 - 10.1109/TNSE.2022.3153095
DO - 10.1109/TNSE.2022.3153095
M3 - Journal article
AN - SCOPUS:85125357766
SN - 2327-4697
VL - 9
SP - 1776
EP - 1788
JO - IEEE Transactions on Network Science and Engineering
JF - IEEE Transactions on Network Science and Engineering
IS - 3
ER -