TY - GEN
T1 - Multi-objective Discrete Moth-Flame Optimization for Complex Network Clustering
AU - Liu, Xingjian
AU - Zhang, Fan
AU - Li, Xianghua
AU - Gao, Chao
AU - LIU, Jiming
N1 - Funding Information:
Acknowledgment. This work was supported by the National Natural Science Foundation of China (Nos. 61976181, 11931015) and Natural Science Foundation of Chongqing (Nos. cstc2018jcyjAX0274, cstc2019jcyj-zdxmX0025).
PY - 2020/9/17
Y1 - 2020/9/17
N2 - Complex network clustering has been extensively studied in recent years, mostly through optimization approaches. In such approaches, the multi-objective optimization methods have been shown to be capable of overcoming the limitations (e.g., instability) of the single-objective methods. Nevertheless, such methods suffer from the shortcoming of incapability of maintaining a good tradeoff between exploration and exploitation, that is, to find better solutions based on the good ones obtained so far. In this paper, we present a new nature-inspired heuristic optimization method, called multi-objective discrete moth-flame optimization (DMFO) method, which achieves such a tradeoff. We describe the detailed algorithm of DMFO that utilizes the Tchebycheff decomposition approach with an norm constraint on the direction vector (2-Tch). Furthermore, we show the experimental results on synthetic and several real-world networks that verify that the proposed DMFO and the algorithm are both effective and promising for tackling the task of complex network clustering.
AB - Complex network clustering has been extensively studied in recent years, mostly through optimization approaches. In such approaches, the multi-objective optimization methods have been shown to be capable of overcoming the limitations (e.g., instability) of the single-objective methods. Nevertheless, such methods suffer from the shortcoming of incapability of maintaining a good tradeoff between exploration and exploitation, that is, to find better solutions based on the good ones obtained so far. In this paper, we present a new nature-inspired heuristic optimization method, called multi-objective discrete moth-flame optimization (DMFO) method, which achieves such a tradeoff. We describe the detailed algorithm of DMFO that utilizes the Tchebycheff decomposition approach with an norm constraint on the direction vector (2-Tch). Furthermore, we show the experimental results on synthetic and several real-world networks that verify that the proposed DMFO and the algorithm are both effective and promising for tackling the task of complex network clustering.
KW - Complex network clustering
KW - Decomposition
KW - Discrete moth-flame optimization
KW - Multi-objective optimization
UR - http://www.scopus.com/inward/record.url?scp=85092084866&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59491-6_35
DO - 10.1007/978-3-030-59491-6_35
M3 - Conference proceeding
AN - SCOPUS:85092084866
SN - 9783030594909
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 372
EP - 382
BT - Foundations of Intelligent Systems - 25th International Symposium, ISMIS 2020, Proceedings
A2 - Helic, Denis
A2 - Stettinger, Martin
A2 - Felfernig, Alexander
A2 - Leitner, Gerhard
A2 - Ras, Zbigniew W.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Symposium on Methodologies for Intelligent Systems, ISMIS 2020
Y2 - 23 September 2020 through 25 September 2020
ER -