TY - JOUR
T1 - SiFSO
T2 - fish swarm optimization-based technique for efficient community detection in complex networks
AU - Ahmad, Yasir
AU - Ullah, Mohib
AU - Khan, Rafiullah
AU - Shafi, Bushra
AU - Khan, Atif
AU - Zareei, Mahdi
AU - Aldosary, Abdallah
AU - Mohamed, Ehab Mahmoud
N1 - Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2020
Y1 - 2020
N2 - Efficient community detection in a complex network is considered an interesting issue due to its vast applications in many prevailing areas such as biology, chemistry, linguistics, social sciences, and others. There are several algorithms available for network community detection. This studyproposed the Sigmoid Fish Swarm Optimization (SiFSO) algorithm to discover efficient network communities. Our proposed algorithm uses the sigmoid function for various fish moves in a swarm, including Prey, Follow, Swarm, and Free Move, for better movement and community detection. The proposed SiFSO algorithm's performance is tested against state-of-the-art particle swarm optimization (PSO) algorithms in Q-modularity and normalized mutual information (NMI). The results showed that the proposed SiFSO algorithm is 0.0014% better in terms of Q-modularity and 0.1187% better in terms of NMI than the other selected algorithms.
AB - Efficient community detection in a complex network is considered an interesting issue due to its vast applications in many prevailing areas such as biology, chemistry, linguistics, social sciences, and others. There are several algorithms available for network community detection. This studyproposed the Sigmoid Fish Swarm Optimization (SiFSO) algorithm to discover efficient network communities. Our proposed algorithm uses the sigmoid function for various fish moves in a swarm, including Prey, Follow, Swarm, and Free Move, for better movement and community detection. The proposed SiFSO algorithm's performance is tested against state-of-the-art particle swarm optimization (PSO) algorithms in Q-modularity and normalized mutual information (NMI). The results showed that the proposed SiFSO algorithm is 0.0014% better in terms of Q-modularity and 0.1187% better in terms of NMI than the other selected algorithms.
UR - http://www.scopus.com/inward/record.url?scp=85098186708&partnerID=8YFLogxK
U2 - 10.1155/2020/6695032
DO - 10.1155/2020/6695032
M3 - Article
SN - 1076-2787
VL - 2020
SP - 1
EP - 9
JO - Complexity
JF - Complexity
IS - 1
M1 - 6695032
ER -