  Examining the community structures within intricate networks is crucial for
comprehending their intrinsic dynamics and functionality. The paper presents
the Fast Local Move Iterated Greedy (FLMIG) algorithm, a novel method designed
to effectively identify community structures in intricate networks. The FLMIG
algorithm improves the modularity optimization process by including a rapid
local move heuristic and an iterated greedy mechanism that switches between
destructive and constructive phases to strengthen the community partitions. The
main innovation is the integration of random neighbor moves with an enhanced
Prune Louvain algorithm, which guarantees fast convergence while maintaining
the connection of the identified communities. The results of our comprehensive
studies, conducted on both synthetic and and real-world networks, clearly show
that FLMIG surpasses existing cutting-edge techniques in terms of both accuracy
and computing efficiency. This algorithm not only provides a strong tool for
identifying communities, but also makes a valuable contribution to the broader
field of network analysis by offering a method that can effectively handle
large-scale and dynamically evolving networks.
