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Progress in Artificial Intelligence

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16-02-2021 | Regular Paper

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

Authors: Sasan Harifi, Javad Mohammadzadeh, Madjid Khalilian, Sadoullah Ebrahimnejad

Published in: Progress in Artificial Intelligence | Issue 2/2021

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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.

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Title: Hybrid-EPC: an Emperor Penguins Colony algorithm with crossover and mutation operators and its application in community detection
Authors: Sasan Harifi
Javad Mohammadzadeh
Madjid Khalilian
Sadoullah Ebrahimnejad
Publication date: 16-02-2021
Publisher: Springer Berlin Heidelberg
Published in: Progress in Artificial Intelligence / Issue 2/2021
Print ISSN: 2192-6352
Electronic ISSN: 2192-6360
DOI: https://doi.org/10.1007/s13748-021-00231-9

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