MOPIO: A Multi-Objective Pigeon-Inspired Optimization Algorithm for Community Detection
Community detection is a hot research direction of network science, which is of great importance to complex system analysis. Therefore, many community detection methods have been developed. Among them, evolutionary computation based ones with a single-objective function are promising in either bench...
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doaj-99942bc635e24362a70164c5dced37242020-12-31T00:04:44ZengMDPI AGSymmetry2073-89942021-12-0113494910.3390/sym13010049MOPIO: A Multi-Objective Pigeon-Inspired Optimization Algorithm for Community DetectionJunliang Shang0Yiting Li1Yan Sun2Feng Li3Yuanyuan Zhang4Jin-Xing Liu5School of Computer Science, Qufu Normal University, Rizhao 276826, ChinaSchool of Computer Science, Qufu Normal University, Rizhao 276826, ChinaSchool of Computer Science, Qufu Normal University, Rizhao 276826, ChinaSchool of Computer Science, Qufu Normal University, Rizhao 276826, ChinaSchool of Information and Control Engineering, Qingdao University of Technology, Qingdao 266000, ChinaSchool of Computer Science, Qufu Normal University, Rizhao 276826, ChinaCommunity detection is a hot research direction of network science, which is of great importance to complex system analysis. Therefore, many community detection methods have been developed. Among them, evolutionary computation based ones with a single-objective function are promising in either benchmark or real data sets. However, they also encounter resolution limit problem in several scenarios. In this paper, a Multi-Objective Pigeon-Inspired Optimization (MOPIO) method is proposed for community detection with Negative Ratio Association (NRA) and Ratio Cut (RC) as its objective functions. In MOPIO, the genetic operator is used to redefine the representation and updating of pigeons. In each iteration, NRA and RC are calculated for each pigeon, and Pareto sorting scheme is utilized to judge non-dominated solutions for later crossover. A crossover strategy based on global and personal bests is designed, in which a compensation coefficient is developed to stably complete the work transition between the map and compass operator, and the landmark operator. When termination criteria were met, a leader selection strategy is employed to determine the final result from the optimal solution set. Comparison experiments of MOPIO, with MOPSO, MOGA-Net, Meme-Net and FN, are performed on real-world networks, and results indicate that MOPIO has better performance in terms of Normalized Mutual information and Adjusted Rand Index.https://www.mdpi.com/2073-8994/13/1/49community detectionPigeon-Inspired OptimizationPareto-optimal sortingmulti-objective optimizationcomplex network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Junliang Shang Yiting Li Yan Sun Feng Li Yuanyuan Zhang Jin-Xing Liu |
spellingShingle |
Junliang Shang Yiting Li Yan Sun Feng Li Yuanyuan Zhang Jin-Xing Liu MOPIO: A Multi-Objective Pigeon-Inspired Optimization Algorithm for Community Detection Symmetry community detection Pigeon-Inspired Optimization Pareto-optimal sorting multi-objective optimization complex network |
author_facet |
Junliang Shang Yiting Li Yan Sun Feng Li Yuanyuan Zhang Jin-Xing Liu |
author_sort |
Junliang Shang |
title |
MOPIO: A Multi-Objective Pigeon-Inspired Optimization Algorithm for Community Detection |
title_short |
MOPIO: A Multi-Objective Pigeon-Inspired Optimization Algorithm for Community Detection |
title_full |
MOPIO: A Multi-Objective Pigeon-Inspired Optimization Algorithm for Community Detection |
title_fullStr |
MOPIO: A Multi-Objective Pigeon-Inspired Optimization Algorithm for Community Detection |
title_full_unstemmed |
MOPIO: A Multi-Objective Pigeon-Inspired Optimization Algorithm for Community Detection |
title_sort |
mopio: a multi-objective pigeon-inspired optimization algorithm for community detection |
publisher |
MDPI AG |
series |
Symmetry |
issn |
2073-8994 |
publishDate |
2021-12-01 |
description |
Community detection is a hot research direction of network science, which is of great importance to complex system analysis. Therefore, many community detection methods have been developed. Among them, evolutionary computation based ones with a single-objective function are promising in either benchmark or real data sets. However, they also encounter resolution limit problem in several scenarios. In this paper, a Multi-Objective Pigeon-Inspired Optimization (MOPIO) method is proposed for community detection with Negative Ratio Association (NRA) and Ratio Cut (RC) as its objective functions. In MOPIO, the genetic operator is used to redefine the representation and updating of pigeons. In each iteration, NRA and RC are calculated for each pigeon, and Pareto sorting scheme is utilized to judge non-dominated solutions for later crossover. A crossover strategy based on global and personal bests is designed, in which a compensation coefficient is developed to stably complete the work transition between the map and compass operator, and the landmark operator. When termination criteria were met, a leader selection strategy is employed to determine the final result from the optimal solution set. Comparison experiments of MOPIO, with MOPSO, MOGA-Net, Meme-Net and FN, are performed on real-world networks, and results indicate that MOPIO has better performance in terms of Normalized Mutual information and Adjusted Rand Index. |
topic |
community detection Pigeon-Inspired Optimization Pareto-optimal sorting multi-objective optimization complex network |
url |
https://www.mdpi.com/2073-8994/13/1/49 |
work_keys_str_mv |
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1724365456910843904 |