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Recent Trends in Intelligent Computing, Communication and Devices pp 183–196Cite as

Community Detection Based on Improved Bee Evolutionary Genetic Algorithm

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1006))

Abstract

To solve the problem of unsatisfactory optimization ability and requiring prior knowledge in the community detection of the complex networks, an improved bee evolutionary genetic algorithm (IBEGA) was proposed by combining the bee evolutionary genetic algorithm. Firstly, the algorithm took modularity as the fitness function, combined the improved character encoding method with the corresponding genetic operators, and automatically acquired the optimal community number and the community detection solution without the prior knowledge. Then, by utilizing the local information of the network topology structure in initialization population, crossover operation and mutation operation, the search space was compressed, the optimization ability and convergence speed was improved and introducing the number of random population inversely proportional to the number of iterations to improve the exploration ability, robustness, and accuracy of the algorithm. Finally, the proposed IBEGA was simulated on real networks and synthetic networks. The results show that compared with other classical algorithms for community discovery and similar intelligent algorithms, the algorithm has the advantages of high accuracy, which shows that the algorithm is feasible and effective.

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Acknowledgments

This work is partially supported by the National Natural Science Foundation of China (61661037, 61162002), the Foundation of Jiangxi Provincial Department of Education (GJJ170575), and the Jiangxi province graduate innovation special foundation (YC2018-S370).

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Authors and Affiliations

  1. School of Information Engineering, Nanchang Hangkong University, Nanchang, 330063, China

    Shijin Zhang, Sheng Zhang, Jibiao Tian, Zhiqiang Wu & Weikai Dai

Authors
  1. Shijin Zhang
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  2. Sheng Zhang
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  3. Jibiao Tian
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  4. Zhiqiang Wu
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  5. Weikai Dai
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Corresponding author

Correspondence to Sheng Zhang .

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Editors and Affiliations

  1. Victoria Business School, Victoria University of Wellington, Wellington, New Zealand

    Vipul Jain

  2. Department of Computer Science and Engineering, Faculty of Engineering and Technology, SOA University, Bhubaneswar, Odisha, India

    Srikanta Patnaik

  3. University "Politehnica" of Bucharest, Bucharest, Romania

    Florin Popențiu Vlădicescu

  4. Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA

    Ishwar K. Sethi

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Zhang, S., Zhang, S., Tian, J., Wu, Z., Dai, W. (2020). Community Detection Based on Improved Bee Evolutionary Genetic Algorithm. In: Jain, V., Patnaik, S., Popențiu Vlădicescu, F., Sethi, I. (eds) Recent Trends in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-9406-5_23

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