Saeed NasehiMoghaddam - Faculty of Engineering,University of Zanjan, Zanjan, Iran
Mehdi Ghazanfari - Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Babak Teimourpour - Faculty of Systems and Industrial Engineering, Tarbiat Modares University, Tehran, Iran

Block modeling as a social structure discovery process needs to find and adopt a partitioning of actors to equivalent classes or positions. The best partitioning, naturally, must provide the closest estimation of network ties and show the most agreement with original network data. This interpretation of the best, leads to the structure with the most fitness to original network data. Finding this best partition vector can be formulated as an optimization problem and can be solved by Meta heuristic algorithms. In this paper, we use cuckoo search and genetic algorithm as a basis for comparison with cuckoo search. In addition to simple cuckoo search, we apply a hybrid cuckoo search algorithm to find the solution. The results of experiments through multiple samples reveals that while genetic algorithm shows the better performance in terms of convergence time and small iteration, the hybrid cuckoo search finds the better solutions than genetic algorithm in large iteration in terms of quality of solutions measured by fitness function. Furthermore, the hybrid cuckoo search shows no significant superiority over the simple cuckoo search, unless the large iteration numbered is used. In addition to block model problem, the proposed hybrid cuckoo search shows clear superiority over the greedy discrete PSO for community detection problem.

Social Network Analysis (SNA): blockmodeling: Genetic Algorithm: Cuckoo Search: likelihood ratio statistics G2