Hybrid-EPC: an Emperor Penguins Colony algorithm with crossover and mutation operators and its application in community detection

Abstract

The idea of hybrid algorithms is formed due to the functional and structural differences in optimization algorithms. The goal is to create hybrid algorithms that can combine the strengths of the optimization algorithms to perform better in solving different problems. The Emperor Penguins Colony (EPC) algorithm is a population-based and nature-inspired optimization algorithm. This algorithm is powerful in finding global optima. In this paper, the standard EPC is improved by combining with genetic operators to finding better global optima. The genetic crossover and mutation operators have been used for modifying the decision vectors. These operators can cause a balance between exploration and exploitation. The balance between exploration and exploitation is effective in achieving a better optimal solution. The proposed algorithm called Hybrid-EPC is compared with GA, PSO, standard EPC, and Hybrid-PSO and tested on 20 various benchmark test functions. Also as an application, the proposed Hybrid-EPC algorithm is used for community detection in complex networks. For this purpose, the algorithm is tested on four social datasets and is compared with other community detection algorithms. The results show that this hybridization improves the standard EPC algorithm and has been successful in community detection.