TY - JOUR
T1 - Dmfo-cd
T2 - A discrete moth-flame optimization algorithm for community detection
AU - Nadimi-Shahraki, Mohammad H.
AU - Moeini, Ebrahim
AU - Taghian, Shokooh
AU - Mirjalili, Seyedali
N1 - Funding Information:
Finantial support by Comision Interministerial de Ciencia y Tecnologia of Spain (CICYT, PB95-0428-CO2 and PB94-0577) is gratefully acknowledged, one of us (L. S.) is grateful for a research fellowship.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/11
Y1 - 2021/11
N2 - In this paper, a discrete moth–flame optimization algorithm for community detection (DMFO-CD) is proposed. The representation of solution vectors, initialization, and movement strategy of the continuous moth–flame optimization are purposely adapted in DMFO-CD such that it can solve the discrete community detection. In this adaptation, locus-based adjacency representation is used to represent the position of moths and flames, and the initialization process is performed by considering the community structure and the relation between nodes without the need of any knowledge about the number of communities. Solution vectors are updated by the adapted movement strategy using a single-point crossover to distance imitating, a two-point crossover to calculate the movement, and a single-point neighbor-based mutation that can enhance the exploration and balance exploration and exploitation. The fitness function is also defined based on modularity. The performance of DMFO-CD was evaluated on eleven real-world networks, and the obtained results were compared with five well-known algorithms in community detection, including GA-Net, DPSO-PDM, GACD, EGACD, and DECS in terms of modularity, NMI, and the number of detected communities. Additionally, the obtained results were statistically analyzed by the Wilcoxon signed-rank and Friedman tests. In the comparison with other comparative algorithms, the results show that the proposed DMFO-CD is competitive to detect the correct number of communities with high modularity.
AB - In this paper, a discrete moth–flame optimization algorithm for community detection (DMFO-CD) is proposed. The representation of solution vectors, initialization, and movement strategy of the continuous moth–flame optimization are purposely adapted in DMFO-CD such that it can solve the discrete community detection. In this adaptation, locus-based adjacency representation is used to represent the position of moths and flames, and the initialization process is performed by considering the community structure and the relation between nodes without the need of any knowledge about the number of communities. Solution vectors are updated by the adapted movement strategy using a single-point crossover to distance imitating, a two-point crossover to calculate the movement, and a single-point neighbor-based mutation that can enhance the exploration and balance exploration and exploitation. The fitness function is also defined based on modularity. The performance of DMFO-CD was evaluated on eleven real-world networks, and the obtained results were compared with five well-known algorithms in community detection, including GA-Net, DPSO-PDM, GACD, EGACD, and DECS in terms of modularity, NMI, and the number of detected communities. Additionally, the obtained results were statistically analyzed by the Wilcoxon signed-rank and Friedman tests. In the comparison with other comparative algorithms, the results show that the proposed DMFO-CD is competitive to detect the correct number of communities with high modularity.
KW - Community detection
KW - Complex network
KW - Metaheuristic algorithms
KW - Moth–flame optimization algorithm
KW - Optimization
KW - Swarm intelligence algorithms
UR - http://www.scopus.com/inward/record.url?scp=85118889863&partnerID=8YFLogxK
U2 - 10.3390/a14110314
DO - 10.3390/a14110314
M3 - Article
AN - SCOPUS:85118889863
VL - 14
JO - Algorithms
JF - Algorithms
SN - 1999-4893
IS - 11
M1 - 314
ER -