Social network analysis is an important research trend which helps in presenting valuable insights from social networks. Community detection is an analysising method which divides the complex network in a set of dynamic clusters with high connective edges. Meta Heuristic algorithms are algorithms, which are inspired from nature, based on the behavior of herds or swarms called ”swarm optimization algorithms”. Swarm Algorithms presents a better community detection in complex networks analysis. 

This chapter presents a comparative analysis of seven swarm algorithms such as krill herd, BAT swarm, Artificial fish swarm, Cuckoo search, chicken swarm, ant loin and loin algorithm for solving the community detection problem in complex networks. Experiments are executed over four benchmark data sets such as Zachary Karate Club, Bottlenose Dolphin, American College football and Facebook. 

Experimental results are recorded using seven quality measures such as normalized  mutual information (NMI), Modularity, Ground truth, recall, precision, F-measure and Accuracy.