DMFO-CD: A Discrete Moth-Flame Optimization Algorithm for Community Detection

In this paper, a discrete moth–flame optimization algorithm for community detection (DMFO-CD) is proposed. The representation of solution vectors, initialization, and movement strategy of the continuous moth–flame optimization are purposely adapted in DMFO-CD such that it can solve the discrete comm...

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Autores principales:	Mohammad H. Nadimi-Shahraki, Ebrahim Moeini, Shokooh Taghian, Seyedali Mirjalili
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	community detection complex network optimization metaheuristic algorithms swarm intelligence algorithms moth–flame optimization algorithm Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/a59b7ef55a4b4be3b12f7767e62c2959
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spelling	oai:doaj.org-article:a59b7ef55a4b4be3b12f7767e62c29592021-11-25T16:13:06ZDMFO-CD: A Discrete Moth-Flame Optimization Algorithm for Community Detection10.3390/a141103141999-4893https://doaj.org/article/a59b7ef55a4b4be3b12f7767e62c29592021-10-01T00:00:00Zhttps://www.mdpi.com/1999-4893/14/11/314https://doaj.org/toc/1999-4893In this paper, a discrete moth–flame optimization algorithm for community detection (DMFO-CD) is proposed. The representation of solution vectors, initialization, and movement strategy of the continuous moth–flame optimization are purposely adapted in DMFO-CD such that it can solve the discrete community detection. In this adaptation, locus-based adjacency representation is used to represent the position of moths and flames, and the initialization process is performed by considering the community structure and the relation between nodes without the need of any knowledge about the number of communities. Solution vectors are updated by the adapted movement strategy using a single-point crossover to distance imitating, a two-point crossover to calculate the movement, and a single-point neighbor-based mutation that can enhance the exploration and balance exploration and exploitation. The fitness function is also defined based on modularity. The performance of DMFO-CD was evaluated on eleven real-world networks, and the obtained results were compared with five well-known algorithms in community detection, including GA-Net, DPSO-PDM, GACD, EGACD, and DECS in terms of modularity, NMI, and the number of detected communities. Additionally, the obtained results were statistically analyzed by the Wilcoxon signed-rank and Friedman tests. In the comparison with other comparative algorithms, the results show that the proposed DMFO-CD is competitive to detect the correct number of communities with high modularity.Mohammad H. Nadimi-ShahrakiEbrahim MoeiniShokooh TaghianSeyedali MirjaliliMDPI AGarticlecommunity detectioncomplex networkoptimizationmetaheuristic algorithmsswarm intelligence algorithmsmoth–flame optimization algorithmIndustrial engineering. Management engineeringT55.4-60.8Electronic computers. Computer scienceQA75.5-76.95ENAlgorithms, Vol 14, Iss 314, p 314 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	community detection complex network optimization metaheuristic algorithms swarm intelligence algorithms moth–flame optimization algorithm Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
spellingShingle	community detection complex network optimization metaheuristic algorithms swarm intelligence algorithms moth–flame optimization algorithm Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95 Mohammad H. Nadimi-Shahraki Ebrahim Moeini Shokooh Taghian Seyedali Mirjalili DMFO-CD: A Discrete Moth-Flame Optimization Algorithm for Community Detection
description	In this paper, a discrete moth–flame optimization algorithm for community detection (DMFO-CD) is proposed. The representation of solution vectors, initialization, and movement strategy of the continuous moth–flame optimization are purposely adapted in DMFO-CD such that it can solve the discrete community detection. In this adaptation, locus-based adjacency representation is used to represent the position of moths and flames, and the initialization process is performed by considering the community structure and the relation between nodes without the need of any knowledge about the number of communities. Solution vectors are updated by the adapted movement strategy using a single-point crossover to distance imitating, a two-point crossover to calculate the movement, and a single-point neighbor-based mutation that can enhance the exploration and balance exploration and exploitation. The fitness function is also defined based on modularity. The performance of DMFO-CD was evaluated on eleven real-world networks, and the obtained results were compared with five well-known algorithms in community detection, including GA-Net, DPSO-PDM, GACD, EGACD, and DECS in terms of modularity, NMI, and the number of detected communities. Additionally, the obtained results were statistically analyzed by the Wilcoxon signed-rank and Friedman tests. In the comparison with other comparative algorithms, the results show that the proposed DMFO-CD is competitive to detect the correct number of communities with high modularity.
format	article
author	Mohammad H. Nadimi-Shahraki Ebrahim Moeini Shokooh Taghian Seyedali Mirjalili
author_facet	Mohammad H. Nadimi-Shahraki Ebrahim Moeini Shokooh Taghian Seyedali Mirjalili
author_sort	Mohammad H. Nadimi-Shahraki
title	DMFO-CD: A Discrete Moth-Flame Optimization Algorithm for Community Detection
title_short	DMFO-CD: A Discrete Moth-Flame Optimization Algorithm for Community Detection
title_full	DMFO-CD: A Discrete Moth-Flame Optimization Algorithm for Community Detection
title_fullStr	DMFO-CD: A Discrete Moth-Flame Optimization Algorithm for Community Detection
title_full_unstemmed	DMFO-CD: A Discrete Moth-Flame Optimization Algorithm for Community Detection
title_sort	dmfo-cd: a discrete moth-flame optimization algorithm for community detection
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a59b7ef55a4b4be3b12f7767e62c2959
work_keys_str_mv	AT mohammadhnadimishahraki dmfocdadiscretemothflameoptimizationalgorithmforcommunitydetection AT ebrahimmoeini dmfocdadiscretemothflameoptimizationalgorithmforcommunitydetection AT shokoohtaghian dmfocdadiscretemothflameoptimizationalgorithmforcommunitydetection AT seyedalimirjalili dmfocdadiscretemothflameoptimizationalgorithmforcommunitydetection
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DMFO-CD: A Discrete Moth-Flame Optimization Algorithm for Community Detection

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