Abstract
Community detection decomposes large-scale, complex networks ‘optimally’
into sets of smaller sub-networks. It finds sub-networks that have the
least inter-connections and the most intra-connections. This article
presents an efficient community detection algorithm that detects
community structures in a weighted network by solving a multi-objective
optimization problem. The whale optimization algorithm is extended to
enabe it to handle multi-objective optimization problems with discrete
variables and to solve the problems on parallel processors. To this end,
the population’s positions are discretized using a transfer function
that maps real variables to discrete variables, the initialization steps
for the algorithm are modified to prevent generating unrealistic
connections between variables, and the updating step of the algorithm is
redefined to produce integer numbers. To identify the community
configurations that are Pareto optimal, the non-dominated sorting
concept is adopted. The proposed algorithm is tested on the Tennessee
Eastman process to show its application and performance.