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An efficient hybrid approach based on PSO, ABC and k-means for cluster analysis

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Abstract

Particle Swarm Optimization (PSO) algorithm is one of the typical example of Swarm Intelligence (SI) algorithm. This article addresses such problems of PSO algorithm as random initial position of each particle, unsmooth speed weight change, and poor search ability, and proposes an optimization algorithm—hybrid PSO (HPSO) algorithm to solve these problems. This algorithm makes comprehensive improvements to the PSO clustering algorithm by using the K-means clustering algorithm to generate initial clustering centers, adopting a negative exponential function model to update the weight of velocity when constructing the “position-velocity” model, and introducing the “search restriction” mechanism, and the “fly-back” mechanism and auxiliary search methods such as the single point crossover operator in the Artificial Bee Colony (ABC) algorithm. Furthermore, experimental results were analyzed and verified. The experiment compares HPSO algorithm with K-Means algorithm, PSO algorithm, and other two typical improved algorithms from the literature on six of the UCI standard clustering test data sets. The results indicate that HPSO algorithm has good performance in stability, clustering effectiveness, robustness and global search ability.

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References

  1. Ab Razak MF, Anuar NB, Othman F et al (2018) Bio-inspired for features optimization and malware detection[J]. Arabian Journal for ence and Engineering 43(12):6963–6979

    Google Scholar 

  2. Abualigah L, Diabat A (2020) A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments[J]. Clust Comput, (1)

  3. Abualigah L, Diabat A, Geem ZW (2020) A comprehensive survey of the harmony search algorithm in clustering applications[J]. Applied ences 10(11):3827

    Google Scholar 

  4. Abuligah L (2020) Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications[J]. Neural Comput & Applic

  5. Castro LND, Zuben FJV (2002) An evolutionary immune network for data clustering[C]// Brazilian symposium on neural networks. IEEE

  6. Chang-Sheng Z, Ji-Gui S, Yan C et al (2008) PSO based partitional clustering algorithm[J]. Journal of Jilin University(Engineering and Technology Edition) 38(6):1371–1377

    Google Scholar 

  7. Dong-Qiang W, Xiao-Xia W (2017) Large data optimization particle swarm clustering algorithm based on cloud storage[J]. Electronic Design Engineering

  8. El-Gallas AI, El-Hawary M, Sallam AA, et al. (2001) Swarm-intelligently trained neural network for power transformer protection[C]// conference on electrical & Computer engineering. IEEE

  9. Falco ID, Cioppa AD, Tarantino E (2007) Facing classification problems with particle swarm optimization[J]. Appl Soft Comput 7(3):652–658

    Article  Google Scholar 

  10. Ganesan T, Elamvazuthi I, Shaari KZK, et al. (2013) Swarm intelligence and gravitational search algorithm for multi-objective optimization of synthesis gas production[J].Applied Energy, 103(MAR.):368---374

  11. Harrington P (n.d.) Machine Learning in Action[M]// Machine learning in action

  12. Honggui H, Wei L U , Junfei Q (2017) Design and application of particle swarm optimization algorithm based on population diversity[J]. Inf Control

  13. http://archive.ics.uci.edu/ml/ (21 April 2019)

  14. Jain AK (2010) Data clustering: 50 years beyond K-means[J]. Pattern Recogn Lett 31(8):651–666

    Article  Google Scholar 

  15. Jain AK, Dubes RC (1988) Algorithms for clustering data[M]. Prentice Hall

  16. Jinping YU, Jie Z, Hongbiao M (2014) K-means clustering algorithm based on improved artificial bee colony algorithm[J]. Journal of Computer Applications 556-562(1):3852–3855

    Google Scholar 

  17. Kader A (2010) Genetically Improved PSO Algorithm for Efficient Data Clustering[C]// Second International Conference on Machine Learning & Computing. IEEE

  18. Kamel N, Ouchen I, Baali K (2014) A sampling-PSO-K-means algorithm for document clustering[J]. Advances in Intelligent Systems and Computing 238:45–54

    Article  Google Scholar 

  19. Kennedy J, Eberhart R (2002) Particle swarm optimization[C]// Icnn95-international conference on neural networks. IEEE

  20. Krishna K, Narasimha MM (1999) Genetic K-means algorithm[J]. IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society 29(3):433–439

    Article  Google Scholar 

  21. Lei W, Huan J I, Qing-Zheng X U (2008) A dynamic clustering analysis based on artificial immune particle swarm optimization algorithm[J]. Journal of Xi'an University of Technology

  22. Liu JM, Han LC, Hou LW (2005) Cluster analysis based on particle swarm optimization algorithm[J]. Systems Engineering-theory & Practice

  23. Lu B, Ju F (2012) An optimized genetic K-means clustering algorithm[C]// international conference on Computer Science & Information Processing. IEEE

  24. Lu H , Li Y , Uemura T , et al. (2018) Low illumination underwater light field images reconstruction using deep convolutional neural networks[J]. Future Generation Computer Systems, 82(MAY):142–148

  25. Lu H, Liu Q, Tian D, Li Y, Kim H, Serikawa S (2019) The cognitive internet of vehicles for autonomous driving[J]. IEEE Netw 33(3):65–73

    Article  Google Scholar 

  26. Lu H, Wang D, Li Y, Li J, Li X, Kim H, Serikawa S, Humar I (2019) CONet: a Cognitive Ocean network[J]. IEEE Wirel Commun 26(3):90–96

    Article  Google Scholar 

  27. Lukasik S , Kowalski PA , Charytanowicz M, et al. (2016) Clustering using flower pollination algorithm and Calinski-Harabasz index[C]// 2016 IEEE Congress on Evolutionary Computation, Vancouver, 24–29 July 2016, pp. 2724–2728. IEEE

  28. Michalewicz Z (1994) Genetic algorithms+data structures[J]. Evolution Programs Second Extended Edition

  29. Naik A , Satapathy S C , Parvathi K (2013) A comparative analysis of results of data clustering with variants of particle swarm optimization[C]// international conference on swarm, evolutionary, and Memetic computing. Springer International Publishing

  30. Omran M, Salman APEA (2005) Particle swarm optimization method for image clustering[J]. International Journal of Pattern Recognition & Artificial Intelligence 19(3):297–321

    Article  Google Scholar 

  31. Prajapati A, Chhabra JK (2018) A particle swarm optimization-based heuristic for software module clustering problem[J]. Arabian Journal for Science & Engineering 43(12):7083–7094

    Article  Google Scholar 

  32. Sakai Y, Lu H, Tan J K , et al. (2019) Recognition of surrounding environment from electric wheelchair videos based on modified YOLOv2[J]. Future Generation Computer Systems, 92(MAR.):157–161

  33. Settles M , Nathan P , Soule T (2005) Breeding swarms[J]

  34. Sriadhi S (2018) K-means method with linear search algorithm to reduce Means Square error (MSE) within data clustering[C]// 3rd annual applied science and engineering conference

  35. Tao FU, Wen-Jing S, Computer DO , et al. (2013) PSO-based K-means algorithm and its application in network intrusion detection[J]. Computer Science

  36. Xiao-Xue L, Mao-Xian Z (2015) A K-means algorithm based on the improved particle swarm optimization algorithm[J]. Journal of Shandong University of Technology(Natural ence Edition)

  37. Yan X, Zhu Y, Zou W, Wang L (2012) A new approach for data clustering using hybrid artificial bee colony algorithm[J]. Neurocomputing 97:241–250

    Article  Google Scholar 

  38. Yongchun C, Zhengqi C, Yabin S (2014) Improved artificial bee colony clustering algorithm based on K-means[J]. Journal of Computer Applications 034(001):204–207,217

    Google Scholar 

  39. Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization[J]. Applied Mathematics & Computation 217(7):3166–3173

    Article  MathSciNet  Google Scholar 

Download references

Funding

This research was supported in part by the Ministry of Education Humanities and Social Sciences Project (No.18YJAZH087), in part by the National Nature Science Foundation of China (No.61672553), in part by the National Social Science Fund of China (No. 20BGL251). The authors sincerely thank for the kind reviewers for their wise comments that helped us to improve the quality of the paper.

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Authors and Affiliations

  1. School of Information Engineering, Minzu University of China, Beijing, 100081, China

    Qiumei Pu, Jingkai Gan, Lirong Qiu, Jiaxin Duan & Hui Wang

Authors
  1. Qiumei Pu
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  2. Jingkai Gan
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  3. Lirong Qiu
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  4. Jiaxin Duan
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  5. Hui Wang
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Contributions

Qiumei Pu designed experiments; Qiumei Pu and Jiaxin Duan carried out experiments; Lirong Qiu and Jingkai Gan analyzed experimental results; Jingkai Gan wrote and edited the manuscript; Hui Wang managed the research activity planning and execution.

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Correspondence to Qiumei Pu.

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Pu, Q., Gan, J., Qiu, L. et al. An efficient hybrid approach based on PSO, ABC and k-means for cluster analysis. Multimed Tools Appl 81, 19321–19339 (2022). https://doi.org/10.1007/s11042-021-11016-6

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  • DOI: https://doi.org/10.1007/s11042-021-11016-6

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