Skip to main content
SpringerLink
Log in

Variants of bat algorithm for solving partitional clustering problems

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

Clustering is an exploratory data analysis technique that organize the data objects into clusters with optimal distance efficacy. In this work, a bat algorithm is considered to obtain optimal set of clusters. The bat algorithm is based on the echolocation feature of micro bats. Moreover, some improvements are proposed to overcome the shortcoming associated with bat algorithm like local optima, slow convergence, initial seed points and trade-off between local and global search mechanisms etc. An enhanced cooperative co-evolution method is proposed for addressing the initial seed points selection issue. The local optima issue is handled through neighbourhood search-based mechanism. The trade-off issue among local and global searches of bat algorithm is addressed through a modified elitist strategy. On the basis of aforementioned improvements, three variants (BA-C, BA-CN and BA-CNE) of bat algorithm is developed and efficacy of these variants is tested over twelve benchmark clustering datasets suing intra-cluster distance, accuracy and rand index parameters. Simulation results showed that BA-CNE variant achieves more effective clustering results as compared to BA-C, BA-CN and BA. The simulation results of BA-CNE are also compared with several existing clustering algorithms and two statistical tests are also applied to investigate the statistical difference among BA-CNE and other clustering algorithms. The simulation and statistical results confirmed that BA-CNE is an effective and robust algorithm for handling partitional clustering problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Aggarwal CC, Chandan KR (eds) (2013) Data clustering: algorithms and applications. CRC Press, Hoboken

    Google Scholar 

  2. Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678

    Article  Google Scholar 

  3. Kant S, Ansari IA (2016) An improved K means clustering with Atkinson index to classify liver patient dataset. Int J Syst Assur Eng Manag 7(1):222–228

    Article  Google Scholar 

  4. Chang DX, Zhang XD, Zheng CW (2009) A genetic algorithm with gene rearrangement for K-means clustering. Pattern Recogn 42(7):1210–1222

    Article  Google Scholar 

  5. Scheunders P (1997) A genetic c-means clustering algorithm applied to color image quantization. Pattern Recogn 30(6):859–866

    Article  Google Scholar 

  6. Mitra S, Banka H (2006) Multi-objective evolutionary biclustering of gene expression data. Pattern Recogn 39(12):2464–2477

    Article  MATH  Google Scholar 

  7. Gomez-Muñoz VM, Porta-Gándara MA (2002) Local wind patterns for modeling renewable energy systems by means of cluster analysis techniques. Renew Energy 25(2):171–182

    Article  Google Scholar 

  8. Nanda SJ, Panda G (2014) A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol Comput 16:1–18

    Article  Google Scholar 

  9. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697

    Article  Google Scholar 

  10. Cura T (2012) A particle swarm optimization approach to clustering. Expert Syst Appl 39(1):1582–1588

    Article  Google Scholar 

  11. Kumar Y, Sahoo G (2014) A charged system search approach for data clustering. Progress Artif Intell 2(2–3):153–166

    Article  Google Scholar 

  12. Jordehi AR (2015) Enhanced leader PSO (ELPSO): a new PSO variant for solving global optimisation problems. Appl Soft Comput 26:401–417

    Article  Google Scholar 

  13. Kushwaha N, Pant M, Kant S, Jain VK (2018) Magnetic optimization algorithm for data clustering. Pattern Recogn Lett 115:59–65

    Article  Google Scholar 

  14. Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175–184

    Article  MathSciNet  Google Scholar 

  15. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132

    MathSciNet  MATH  Google Scholar 

  16. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern 26(1):29–41

    Article  Google Scholar 

  17. Jordehi AR (2014) A chaotic-based big bang–big crunch algorithm for solving global optimisation problems. Neural Comput Appl 25(6):1329–1335

    Article  Google Scholar 

  18. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010) (pp 65–74). Springer, Berlin, Heidelberg

  19. Ashish T, Kapil S, Manju B (2018) Parallel bat algorithm-based clustering using MAPreduce. Networking communication and data knowledge engineering. Springer, Singapore, pp 73–82

    Book  Google Scholar 

  20. Yilmaz S, Kucuksille EU (2013) Improved bat algorithm (IBA) on continuous optimization problems. Lecture Notes Softw Eng 1(3):279

    Article  Google Scholar 

  21. Fister Jr I, Fister D, Yang XS (2013) A hybrid bat algorithm. ArXiv: 1303.6310

  22. Meng XB, Gao XZ, Liu Y, Zhang H (2015) A novel bat algorithm with habitat selection and Doppler effect in echoes for optimization. Expert Syst Appl 42(17–18):6350–6364

    Article  Google Scholar 

  23. Senthilnath J, Kulkarni S, Benediktsson JA, Yang XS (2016) A novel approach for multispectral satellite image classification based on the bat algorithm. IEEE Geosci Remote Sens Lett 13(4):599–603

    Article  Google Scholar 

  24. Tang H, Sun W, Yu H, Lin A, Xue M (2020) A multirobot target searching method based on bat algorithm in unknown environments. Expert Syst Appl 141:112945

    Article  Google Scholar 

  25. Gan C, Cao W, Wu M, Chen X (2018) A new bat algorithm based on iterative local search and stochastic inertia weight. Expert Syst Appl 104:202–212

    Article  Google Scholar 

  26. Liu Q, Li J, Wu L, Wang F, Xiao W (2020) A novel bat algorithm with double mutation operators and its application to low-velocity impact localization problem. Eng Appl Artif Intell 90:103505

    Article  Google Scholar 

  27. Liu Q, Wu L, Xiao W, Wang F, Zhang L (2018) A novel hybrid bat algorithm for solving continuous optimization problems. Appl Soft Comput 73:67–82

    Article  Google Scholar 

  28. Eskandari S, Javidi MM (2020) A novel hybrid bat algorithm with a fast clustering-based hybridization. Evol Intel 13(3):427–442

    Article  Google Scholar 

  29. Yildizdan G, Baykan ÖK (2020) A novel modified bat algorithm hybridizing by differential evolution algorithm. Expert Syst Appl 141:112949

    Article  Google Scholar 

  30. Ghanem WA, Jantan A (2019) An enhanced Bat algorithm with mutation operator for numerical optimization problems. Neural Comput Appl 31(1):617–651

    Article  Google Scholar 

  31. Cui Z, Li F, Zhang W (2019) Bat algorithm with principal component analysis. Int J Mach Learn Cybern 10(3):603–622

    Article  Google Scholar 

  32. Cai X, Wang H, Cui Z, Cai J, Xue Y, Wang L (2018) Bat algorithm with triangle-flipping strategy for numerical optimization. Int J Mach Learn Cybern 9(2):199–215

    Article  Google Scholar 

  33. Zhu LF, Wang JS (2019) Data clustering method based on bat algorithm and parameters optimization. Eng Lett 27(1):241–250

    Google Scholar 

  34. Shehab M, Khader AT, Laouchedi M, Alomari OA (2019) Hybridizing cuckoo search algorithm with bat algorithm for global numerical optimization. J Supercomput 75(5):2395–2422

    Article  Google Scholar 

  35. Al-Betar MA, Awadallah MA (2018) Island bat algorithm for optimization. Expert Syst Appl 107:126–145

    Article  Google Scholar 

  36. Liu L, Luo S, Guo F, Tan S (2020) Multi-point shortest path planning based on an Improved Discrete Bat Algorithm. Appl Soft Comput 95:106498

    Article  Google Scholar 

  37. Aboubi Y, Drias H, Kamel N (2016) BAT-CLARA: BAT-inspired algorithm for Clustering LARge Applications. IFAC-Papers OnLine 49(12):243–248

    Article  Google Scholar 

  38. Neelima S, Satyanarayana N, Murthy PK (2018) Minimizing frequent itemsets using hybrid ABCBAT algorithm. Data engineering and intelligent computing. Springer, Singapore, pp 91–97

    Google Scholar 

  39. Rahman MA, Islam MZ (2014) A hybrid clustering technique combining a novel genetic algorithm with K-Means. Knowl-Based Syst 71:345–365

    Article  Google Scholar 

  40. Liu R, Jiao L, Zhang X, Li Y (2012) Gene transposon based clone selection algorithm for automatic clustering. Inf Sci 204:1–22

    Article  Google Scholar 

  41. Cao F, Liang J, Jiang G (2009) An initialization method for the K-Means algorithm using neighborhood model. Comput Math Appl 58(3):474–483

    Article  MathSciNet  MATH  Google Scholar 

  42. Sahoo G (2017) A two-step artificial bee colony algorithm for clustering. Neural Comput Appl 28(3):537–551

    Article  Google Scholar 

  43. Erisoglu M, Calis N, Sakallioglu S (2011) A new algorithm for initial cluster centers in k-means algorithm. Pattern Recogn Lett 32(14):1701–1705

    Article  Google Scholar 

  44. Hatamlou A (2012) In search of optimal centroids on data clustering using a binary search algorithm. Pattern Recogn Lett 33(13):1756–1760

    Article  Google Scholar 

  45. Žalik KR (2008) An efficient k′-means clustering algorithm. Pattern Recogn Lett 29(9):1385–1391

    Article  Google Scholar 

  46. Kumar Y, Singh PK (2019) A chaotic teaching learning based optimization algorithm for clustering problems. Appl Intell 49(3):1036–1062

    Article  Google Scholar 

  47. Fränti P, Sieranoja S (2018) K-means properties on six clustering benchmark datasets. Appl Intell 48(12):4743–4759

    Article  MATH  Google Scholar 

  48. Taherdangkoo M, Shirzadi MH, Yazdi M, Bagheri MH (2013) A robust clustering method based on blind, naked mole-rats (BNMR) algorithm. Swarm Evol Comput 10:1–11

    Article  Google Scholar 

  49. Senthilnath J, Omkar SN, Mani V (2011) Clustering using firefly algorithm: performance study. Swarm Evol Comput 1(3):164–171

    Article  Google Scholar 

  50. Han X, Quan L, Xiong X, Almeter M, Xiang J, Lan Y (2017) A novel data clustering algorithm based on modified gravitational search algorithm. Eng Appl Artif Intell 61:1–7

    Article  Google Scholar 

  51. Zhang C, Ouyang D, Ning J (2010) An artificial bee colony approach for clustering. Expert Syst Appl 37(7):4761–4767

    Article  Google Scholar 

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

    Article  Google Scholar 

  53. Kumar Y, Sahoo G (2015) Hybridization of magnetic charge system search and particle swarm optimization for efficient data clustering using neighborhood search strategy. Soft Comput 19(12):3621–3645

    Article  Google Scholar 

  54. Zhou Y, Zhou Y, Luo Q, Abdel-Basset M (2017) A simplex method-based social spider optimization algorithm for clustering analysis. Eng Appl Artif Intell 64:67–82

    Article  Google Scholar 

  55. Boushaki SI, Kamel N, Bendjeghaba O (2018) A new quantum chaotic cuckoo search algorithm for data clustering. Expert Syst Appl 96:358–372

    Article  MATH  Google Scholar 

  56. Chang D, Zhao Y, Zheng C, Zhang X (2012) A genetic clustering algorithm using a message-based similarity measure. Expert Syst Appl 39(2):2194–2202

    Article  Google Scholar 

  57. Xiao J, Yan Y, Zhang J, Tang Y (2010) A quantum-inspired genetic algorithm for k-means clustering. Expert Syst Appl 37(7):4966–4973

    Article  Google Scholar 

  58. Bijari K, Zare H, Veisi H, Bobarshad H (2018) Memory-enriched big bang–big crunch optimization algorithm for data clustering. Neural Comput Appl 29(6):111–121

    Article  Google Scholar 

  59. Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017) A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Appl Soft Comput 60:423–435

    Article  Google Scholar 

  60. Pakrashi A, Chaudhuri BB (2016) A Kalman filtering induced heuristic optimization based partitional data clustering. Inf Sci 369:704–717

    Article  Google Scholar 

  61. Kang Q, Liu S, Zhou M, Li S (2016) A weight-incorporated similarity-based clustering ensemble method based on swarm intelligence. Knowl-Based Syst 104:156–164

    Article  Google Scholar 

  62. Wang R, Zhou Y, Qiao S, Huang K (2016) Flower pollination algorithm with bee pollinator for cluster analysis. Inf Process Lett 116(1):1–14

    Article  Google Scholar 

  63. Hatamlou A, Hatamlou M (2013) PSOHS: an efficient two-stage approach for data clustering. Memetic Comput 5(2):155–161

    Article  Google Scholar 

  64. Jiang B, Wang N (2014) Cooperative bare-bone particle swarm optimization for data clustering. Soft Comput 18(6):1079–1091

    Article  Google Scholar 

  65. Kumar Y, Singh PK (2018) Improved cat swarm optimization algorithm for solving global optimization problems and its application to clustering. Appl Intell 48(9):2681–2697

    Article  Google Scholar 

  66. Kwedlo W (2011) A clustering method combining differential evolution with the K-means algorithm. Pattern Recogn Lett 32(12):1613–1621

    Article  Google Scholar 

  67. Yin M, Hu Y, Yang F, Li X, Gu W (2011) A novel hybrid K-harmonic means and gravitational search algorithm approach for clustering. Expert Syst Appl 38(8):9319–9324

    Article  Google Scholar 

  68. Jiang H, Yi S, Li J, Yang F, Hu X (2010) Ant clustering algorithm with K-harmonic means clustering. Expert Syst Appl 37(12):8679–8684

    Article  Google Scholar 

  69. Kaur A, Kumar Y (2021) A new metaheuristic algorithm based on water wave optimization for data clustering. Evol Intell 2021:1–25

    Google Scholar 

  70. Kuo RJ, Zheng YR, Nguyen TPQ (2021) Metaheuristic-based possibilistic fuzzy k-modes algorithms for categorical data clustering. Inf Sci 557:1–15

    Article  MathSciNet  MATH  Google Scholar 

  71. Hu KC, Tsai CW, Chiang MC (2020) A multiple-search multi-start framework for metaheuristics for clustering problems. IEEE Access 8:96173–96183

    Article  Google Scholar 

  72. Aljarah I, Mafarja M, Heidari AA, Faris H, Mirjalili S (2020) Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach. Knowl Inf Syst 62(2):507–539

    Article  Google Scholar 

  73. Zhou X, Zhang R, Wang X, Huang T, Yang C (2020) Kernel intuitionistic fuzzy c-means and state transition algorithm for clustering problem. Soft Comput 24(20):15507–15518

    Article  Google Scholar 

  74. Jensi R, Jiji GW (2016) An improved krill herd algorithm with global exploration capability for solving numerical function optimization problems and its application to data clustering. Appl Soft Comput 46:230–245

    Article  Google Scholar 

  75. Hatamlou A (2017) A hybrid bio-inspired algorithm and its application. Appl Intell 47(4):1059–1067

    Article  Google Scholar 

  76. Bouyer A, Hatamlou A (2018) An efficient hybrid clustering method based on improved cuckoo optimization and modified particle swarm optimization algorithms. Appl Soft Comput 67:172–182

    Article  Google Scholar 

  77. Xiang WL, Zhu N, Ma SF, Meng XL, An MQ (2015) A dynamic shuffled differential evolution algorithm for data clustering. Neurocomputing 158:144–154

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh, India

    Yugal Kumar & Arvinder Kaur

Authors
  1. Yugal Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arvinder Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yugal Kumar.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, Y., Kaur, A. Variants of bat algorithm for solving partitional clustering problems. Engineering with Computers 38 (Suppl 3), 1973–1999 (2022). https://doi.org/10.1007/s00366-021-01345-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-021-01345-3

Keywords

Search

Navigation