Skip to main content
SpringerLink
Log in

Applications and Advancements of Nature-Inspired Optimization Algorithms in Data Clustering: A Detailed Analysis

  • Conference paper
  • First Online:
Computational Intelligence in Data Mining

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 990))

  • 718 Accesses

Abstract

In the last decade, nature-inspired optimization algorithm has been a keen interest among the researchers of optimization community. Most of nature-inspired algorithms are developed through the simulating behavior of natural agents in nature. In comparison with evolutionary- and swarm-based algorithms, these are most effective techniques for all real-life applications. Although both swarm- and evolutionary-based algorithms are one of the subsets of nature-inspired optimization algorithm but the efficiency and effectiveness of such algorithm make them more attractive to use in various data mining problems. Among the other tasks of data mining, it has been always a challenging task to solve clustering problem, which is unsupervised in nature. In this paper, a brief study has been conducted on the applications of nature-inspired optimization algorithms in clustering techniques. Also, few challenging issues along with the advancements of various nature-inspired optimization algorithms are realized in the field of clustering.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Khan, G.M.: Evolutionary computation. In: Evolution of Artificial Neural Development, pp. 29–37. Springer, Cham (2018)

    Google Scholar 

  2. Kennedy, J.: Particle swarm optimization. Encyclopedia of Machine Learning, pp. 760–766. Springer, Boston, MA (2011)

    Google Scholar 

  3. Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)

    Google Scholar 

  4. Yang, Xin-She, Deb, Suash: Cuckoo search: recent advances and applications. Neural Comput. Appl. 24(1), 169–174 (2014)

    Article  Google Scholar 

  5. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  6. Yang, X.-S.: Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Comput. 2(2), 78–84 (2010)

    Article  Google Scholar 

  7. Cuevas, E., Cienfuegos, M.: A new algorithm inspired in the behavior of the social-spider for constrained optimization. Expert Syst. Appl. 41(2), 412–425 (2014)

    Article  Google Scholar 

  8. Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer, Berlin, Heidelberg (2010)

    Chapter  Google Scholar 

  9. Merrikh-Bayat, F.: A numerical optimization algorithm inspired by the strawberry plant. arXiv preprint arXiv:1407.7399 (2014)

  10. Salhi, A., Fraga, E.S.: Nature-inspired optimisation approaches and the new plant propagation algorithm, pp. K2–1 (2011)

    Google Scholar 

  11. Muhammad, S., Salhi, A.: A seed-based plant propagation algorithm: the feeding station model. Sci. World J. 2015 (2015)

    Google Scholar 

  12. Chu, S.-C., Tsai, P.-W., Pan, J.-S.: Cat swarm optimization. In: Pacific Rim International Conference on Artificial Intelligence. Springer, Berlin, Heidelberg (2006)

    Google Scholar 

  13. Van Laarhoven, P.J., Aarts, E.H.: Simulated annealing. In: Simulated Annealing: theory and Applications, pp. 7–15. Springer, Dordrecht (1987)

    Google Scholar 

  14. de Castro, L.N., Timmis, J.: Artificial immune systems: a novel paradigm to pattern recognition. Artif. Neural Netw. pattern Recognit. 1, 67–84 (2002)

    Google Scholar 

  15. Dorigo, M., Stützle, T.: The ant colony optimization metaheuristic: algorithms, applications, and advances. In: Handbook of Metaheuristics, pp. 250–285. Springer, Boston, MA (2003)

    Chapter  Google Scholar 

  16. Storn, R., Price, K.V.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  17. Passino, K.M.: Bacterial foraging optimization. In: Innovations and Developments of Swarm Intelligence Applications, pp. 219–234. IGI Global (2012)

    Google Scholar 

  18. Abbass, H.A.: MBO: marriage in honey bees optimization-a haplometrosis polygynous swarming approach. In: Proceedings of the 2001 Congress on Evolutionary Computation CEC2001 (2001)

    Google Scholar 

  19. Li, X.-L., et al.: Applications of artificial fish school algorithm in combinatorial optimization problems. J. Shandong Univ. (Eng. Sci.) 5, 015 (2004)

    Google Scholar 

  20. Muller, S.D., et al.: Optimization based on bacterial chemotaxis. IEEE Trans. Evol. Comput. 6(1), 16–29 (2002)

    Article  Google Scholar 

  21. Sun, J.Z., et al.: An improved social cognitive optimization algorithm. Appl. Mechan. Materials. Vol. 427. Trans Tech Publications, 2013

    Google Scholar 

  22. Haddad, O.B., Afshar, A., Mariño, M.A.: Honey-bees mating optimization (HBMO) algorithm: a new heuristic approach for water resources optimization. Water Resour. Manag. 20.5, 661–680 (2006)

    Article  Google Scholar 

  23. Mehrabian, A.R., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Ecol. Inform. 1(4), 355–366 (2006)

    Article  Google Scholar 

  24. Eusuff, M.M., Lansey, K.E.: Optimization of water distribution network design using the shuffled frog leaping algorithm. J. Water Resour. Plan. Manag. 129(3), 210–225 (2003) (cited By (since 1996) 297)

    Google Scholar 

  25. Formato, R.A.: Central force optimization: a new metaheuristic with applications in applied electromagnetics. Prog. Electromagn. Res. PIER 77, 425–491 (2007)

    Article  Google Scholar 

  26. Hamed, S.-H.: Problem solving by intelligent water drops. In: IEEE Congress on Evolutionary Computation, 2007. CEC 2007, pp. 3226–3231. IEEE (2007)

    Google Scholar 

  27. Rabanal, P., Rodríguez, I., Rubio, F.: Using river formation dynamics to design heuristic algorithms. In: Unconventional Computation, pp. 163–177. Springer (2007)

    Google Scholar 

  28. Havens, T.C., Spain, C.J., Salmon, N.G., Keller, J.M.: Roach infestation optimization. In: Swarm Intelligence Symposium, 2008. SIS 2008. IEEE, pp. 1–7. IEEE (2008)

    Google Scholar 

  29. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. Control Syst. IEEE 22(3), 52–67 (2002)

    Google Scholar 

  30. Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: Gsa: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)

    Article  MATH  Google Scholar 

  31. Kashan, A.H.: League championship algorithm: a new algorithm for numerical function optimization. In: International Conference of Soft Computing and Pattern Recognition, 2009. SOCPAR’09, pp. 43–48. IEEE (2009)

    Google Scholar 

  32. Kashan, A.H.: A new metaheuristic for optimization: optics inspired optimization (OIO). Comput. Oper. Res. 55, 99–125 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  33. Comellas Padró, F.D.P., Martínez Navarro, J., et al.: Bumblebees: a multiagent combinatorial optimization algorithm inspired by social insect behaviour (2011)

    Google Scholar 

  34. Niu, B., Wang, H.: Bacterial colony optimization. Discrete Dynamics in Nature and Society 2012 (2012)

    MATH  Google Scholar 

  35. Mucherino, A., Seref, O.: Monkey search: a novel metaheuristic search for global optimization. Data Min. Syst. Anal. Optim. Biomed. 953, 162–173 (2007)

    Article  Google Scholar 

  36. Alatas, B.: ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst. Appl. 38(10), 13170–13180 (2011)

    Article  Google Scholar 

  37. Lam, A.S., Li, V.O.: Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans. Evol. Comput. 14(3), 381–399 (2010)

    Article  Google Scholar 

  38. Findik, O.: Bull optimization algorithm based on genetic operators for continuous optimization problems. Turk. J. Electr. Eng. Comput. Sci. 23 (Sup 1), 2225–2239 (2015)

    Article  Google Scholar 

  39. Yazdani, M., Jolai, F.: Lion optimization computational design and engineering 3(1), 24–36 (2016)

    Google Scholar 

  40. Wang, G.-G., Deb, S., Coelho, L.S.: Elephant algorithm (LOA): a nature-inspired metaheuristic algorithm. J. Herding Optim, 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI). IEEE (2015)

    Google Scholar 

  41. Tang, R., et al.: Wolf search algorithm with ephemeral memory. In: 2012 Seventh International Conference on Digital Information Management (ICDIM). IEEE (2012)

    Google Scholar 

  42. Jung, S.H.: Queen-bee evolution for genetic algorithms. Electron. Lett. 39(6), 575–576 (2003)

    Article  Google Scholar 

  43. Yang, X.-S.: Flower pollination algorithm for global optimization. Unconv. Comput. Nat. Comput. 240–249 (2012)

    Google Scholar 

  44. Yang, X.-S., Karamanoglu, M., He, X.: Multi objective flower algorithm for optimization. Proced. Comput. Sci. 18, 861–868 (2013)

    Article  Google Scholar 

  45. Hatamlou, A.: Black hole: a new heuristic optimization approach for data clustering. Inf. Sci. (2012)

    Google Scholar 

  46. Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M.: The bees algorithm-a novel tool for complex optimization problems. In: Proceedings of the 2nd Virtual International Conference on Intelligent Production Machines and Systems (IPROMS 2006), pp. 454–459 (2006)

    Chapter  Google Scholar 

  47. Krishnanand, K.N., Ghose, D.: Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. In: Swarm Intelligence Symposium, 2005. SIS 2005. Proceedings 2005 IEEE, pp. 84–91. IEEE (2005)

    Google Scholar 

  48. Krishnanand, K.N., Ghose, D.: Glowworm swarm optimisation: a new method for optimising multi-modal functions. Int. J. Comput. Intell. Stud. 1(1), 93–119 (2009)

    Article  Google Scholar 

  49. Asyali, M.H., Colak, D., Demirkaya, O., Inan, M.S.: Gene expression profile classification: a review. Curr. Bioinform. 1(1), 55–73 (2006)

    Article  Google Scholar 

  50. Lin, P.L., Huang, P.W., Kuo, C.H., Lai, Y.H.: A size-insensitive integrity-based fuzzy c-means method for data clustering. Pattern Recognit. 47(5), 2042–2056 (2014)

    Article  Google Scholar 

  51. Nayak, J., Naik, B., Behera, H.S.: Fuzzy C-means (FCM) clustering algorithm: a decade review from 2000 to 2014. In: Computational Intelligence in Data Mining, vol. 2, pp. 133–149. Springer, New Delhi (2015)

    Google Scholar 

  52. Hartigan, J.A., Wong, M.A.: Algorithm AS 136: a K-means clustering algorithm. Appl. Stat. 28(1), 100–108 (1979)

    Article  MATH  Google Scholar 

  53. Jiang, Bo, Wang, Ning, Wang, Liping: Particle swarm optimization with age-group topology for multimodal functions and data clustering. Commun. Nonlinear Sci. Numer. Simul. 18(11), 3134–3145 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  54. Aljarah, I., Ludwig, S.A.: MapReduce intrusion detection system based on a particle swarm optimization clustering algorithm. 2013 IEEE Congress on Evolutionary Computation (CEC). IEEE (2013)

    Google Scholar 

  55. Silva Filho, T.M., et al.: Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization. Expert Syst. Appl. 42(17–18) 6315–6328 (2015)

    Article  Google Scholar 

  56. Alswaitti, M., Albughdadi, M., Isa, N.A.M.: Density-based particle swarm optimization algorithm for data clustering. Expert Syst. Appl. 91, 170–186 (2018)

    Article  Google Scholar 

  57. Alam, S., Dobbie, G., Riddle, P.: An evolutionary particle swarm optimization algorithm for data clustering. In: Swarm Intelligence Symposium, 2008. SIS 2008, IEEE. IEEE (2008)

    Google Scholar 

  58. Nanda, S.J., Panda, G.: Automatic clustering algorithm based on multi-objective immunized PSO to classify actions of 3D human models. Eng. Appl. Arti. Intell. 26(5–6) 1429–1441 (2013)

    Article  Google Scholar 

  59. Tsai, C.-W., et al.: A fast particle swarm optimization for clustering. Soft Comput. 19(2), 321–338 (2015)

    Article  Google Scholar 

  60. Azab, S.S., Hady, M.F.A., Hefny, H.A., Local best particle swarm optimization for partitioning data clustering. 2016 12th International on Computer Engineering Conference (ICENCO). IEEE (2016)

    Google Scholar 

  61. Wang, J., et al.: Evaluate clustering performance and computational efficiency for PSO based fuzzy clustering methods in processing big imbalanced data. In: 2017 IEEE International Conference on Communications (ICC). IEEE (2017)

    Google Scholar 

  62. Abualigah, L.M., Khader, A.T.: Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J. Supercomput. 73(11), 4773–4795 (2017)

    Article  Google Scholar 

  63. Karaboga, D., Ozturk, C.: A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Appl. Soft Comput. 11(1), 652–657 (2011)

    Article  Google Scholar 

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

    Article  Google Scholar 

  65. Forsati, R., Keikha, A., Shamsfard, M.: An improved bee colony optimization algorithm with an application to document clustering. Neurocomputing 159, 9–26 (2015)

    Article  Google Scholar 

  66. Ozturk, C., Hancer, E., Karaboga, D.: Improved clustering criterion for image clustering with artificial bee colony algorithm. Pattern Anal. Appl. 18(3), 587–599 (2015)

    Article  MathSciNet  Google Scholar 

  67. Goel, S., Sharma, A., Bedi, P.: Cuckoo search clustering algorithm: a novel strategy of biomimicry. 2011 World Congress on Information and Communication Technologies (WICT). IEEE (2011)

    Google Scholar 

  68. Garg, S., Batra, S.: Fuzzified cuckoo based clustering technique for network anomaly detection. Comput. Electr. Eng. (2017)

    Google Scholar 

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

    Google Scholar 

  70. Ye, S., et al.: K-means clustering algorithm based on improved Cuckoo search algorithm and its application. In: 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA). IEEE (2018)

    Google Scholar 

  71. İnkaya, T., Kayalıgil, S., Özdemirel, N.E.: Ant colony optimization based clustering methodology. Appl. Soft Comput. 28, 301–311 (2015)

    Article  Google Scholar 

  72. Zhang, L., Cao, Q.: A novel ant-based clustering algorithm using the kernel method. Inf. Sci. 181(20), 4658–4672 (2011)

    Article  MathSciNet  Google Scholar 

  73. Chen, L., Xu, X.-H., Chen, Y.-X.: An adaptive ant colony clustering algorithm. In: 2004. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, vol. 3. IEEE (2004)

    Google Scholar 

  74. Han, Y., Shi, P.: An improved ant colony algorithm for fuzzy clustering in image segmentation. Neurocomputing 70(4-6), 665–671 (2007)

    Article  Google Scholar 

  75. Izakian, H., Abraham, A., Snášel, V.: Fuzzy clustering using hybrid fuzzy c-means and fuzzy particle swarm optimization. 2009. NaBIC 2009. World Congress on Nature & Biologically Inspired Computing. IEEE (2009)

    Google Scholar 

  76. Marinakis, Y., Marinaki, M., Matsatsinis, N.: A stochastic nature inspired metaheuristic for clustering analysis. Int. J. Bus. Intell. Data Min. 3(1), 30–44 (2008)

    Article  Google Scholar 

  77. Wu, Fang-xiang: Genetic weighted k-means algorithm for clustering large-scale gene expression data. BMC Bioinform. 9(6), S12 (2008)

    Article  Google Scholar 

  78. Marinakis, Y., Marinaki, M., Matsatsinis, N.: A hybrid discrete artificial bee colony-GRASP algorithm for clustering. 2009. CIE 2009. International Conference on Computers & Industrial Engineering. IEEE (2009)

    Google Scholar 

  79. Huang, C.-L., et al.: Hybridization strategies for continuous ant colony optimization and particle swarm optimization applied to data clustering. Appl. Soft Comput. 13(9), 3864–3872 (2013)

    Article  Google Scholar 

  80. Song, W., et al.: A hybrid evolutionary computation approach with its application for optimizing text document clustering. Expert Syst. Appl. 42(5), 2517–2524 (2015)

    Article  Google Scholar 

  81. Handl, J., Meyer, B.: Ant-based and swarm-based clustering. Swarm Intell. 1(2), 95–113 (2007)

    Article  Google Scholar 

  82. Niknam, T., et al.: An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering. Eng. Appl. Artif. Intell. 24(2) 306–317 (2011)

    Article  Google Scholar 

  83. Hassanzadeh, T., Meybodi, M.R.: A new hybrid approach for data clustering using firefly algorithm and K-means. 2012 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP). IEEE (2012)

    Google Scholar 

  84. Prasad, D., Rajendra, P.V., Naganjaneyulu, Prasad, K.S.: A hybrid swarm optimization for energy efficient clustering in multi-hop wireless sensor network. Wirel. Pers. Commun. 94(4), 2459–2471 (2017)

    Google Scholar 

  85. Kumar, A., Kumar, D., Jarial, S.: A novel hybrid K-means and artificial bee colony algorithm approach for data clustering. Decis. Sci. Lett. 7(1), 65–76 (2018)

    Google Scholar 

  86. Nayak, J., et al.: An improved firefly fuzzy c-means (FAFCM) algorithm for clustering real world data sets. In: Advanced Computing, Networking and Informatics, vol. 1, pp. 339–348. Springer, Cham (2014)

    Google Scholar 

  87. de Barros Franco, D.G., Steiner, M.T.A.: Clustering of solar energy facilities using a hybrid fuzzy c-means algorithm initialized by metaheuristics. J. Clean. Product. 191, 445–457 (2018)

    Article  Google Scholar 

  88. He, Y., Hui, S.C., Sim, Y.: A novel ant-based clustering approach for document clustering. In: Asia Information Retrieval Symposium. Springer, Berlin, Heidelberg (2006)

    Chapter  Google Scholar 

  89. Banharnsakun, A.: A MapReduce-based artificial bee colony for large-scale data clustering. Pattern Recognit. Lett. 93, 78–84 (2017)

    Article  Google Scholar 

  90. Bijari, K., et al.: Memory-enriched big bang–big crunch optimization algorithm for data clustering. Neural Comput. Appl. 29(6), 111–121 (2018)

    Article  Google Scholar 

  91. Roy, R., Anuradha, J.: A modified brainstorm optimization for clustering using hard c-means. 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). IEEE (2015)

    Google Scholar 

  92. Saida, I.B., Nadjet, K., Omar, B.: A new algorithm for data clustering based on cuckoo search optimization. In: Genetic and Evolutionary Computing, pp. 55–64. Springer, Cham (2014)

    MATH  Google Scholar 

  93. Cobos, C., et al.: Clustering of web search results based on the cuckoo search algorithm and balanced Bayesian information criterion. Inf. Sci. 281, 248–264 (2014)

    Article  Google Scholar 

  94. Soliman, O.S., Saleh, D.A.: Multi-objective c-means data clustering algorithm using self-adaptive differential evolution. (2015)

    Google Scholar 

  95. Krishnasamy, G., Kulkarni, A.J., Paramesran, R.: A hybrid approach for data clustering based on modified cohort intelligence and K-means. Expert Syst. Appl. 41(13), 6009–6016 (2014)

    Article  Google Scholar 

  96. Nayak, S.K., et al.: A modified differential evolution-based fuzzy multi-objective approach for clustering. Int. J. Manag. Decis. Mak. 16(1), 24–49 (2017)

    Article  Google Scholar 

  97. Abshouri, A.A., Bakhtiary, A.: A new clustering method based on firefly and KHM. J. Commun. Comput. 9(4), 387–391 (2012)

    Google Scholar 

  98. Łukasik, S., et al.: Data clustering with grasshopper optimization algorithm. 2017 Federated Conference on Computer Science and Information Systems (FedCSIS). IEEE (2017)

    Google Scholar 

  99. Fathian, M., Amiri, B.: A honeybee-mating approach for cluster analysis. Int. J. Adv. Manuf. Technol. 38(7-8), 809–821 (2008)

    Article  Google Scholar 

  100. Fathian, M., Amiri, B., Maroosi, A.: Application of honey-bee mating optimization algorithm on clustering. Appl. Math. Comput. 190(2), 1502–1513 (2007)

    MathSciNet  MATH  Google Scholar 

  101. Hamou, R.M., Amine, A., Rahmani, M.: A new biomimetic approach based on social spiders for clustering of text. In: Software Engineering Research, Management and Applications 2012, pp. 17–30. Springer, Berlin, Heidelberg (2012)

    Chapter  Google Scholar 

  102. Banumathy, D., Selvarajan, S.: Bacterial foraging optimized fuzzy c means clustering for efficient disease prediction. Res. J. Biotechnol. 12, 280–288 (2017)

    Google Scholar 

  103. Serapião, A.B., et al:. Combining K-means and K-harmonic with fish school search algorithm for data clustering task on graphics processing units. Appl. Soft Comput. 41, 290–304 (2016)

    Article  Google Scholar 

  104. Fan, C., Zhang, T., Yang, Z., Wang, L.: A text clustering algorithm hybriding invasive weed optimization with K-means. In: 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th International Conference on Autonomic and Trusted Computing and 2015 IEEE 15th International Conference on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), pp. 1333–1338. IEEE (2015)

    Google Scholar 

  105. Fan, S., Ding, S., Xue, Y.: Self-adaptive kernel K-means algorithm based on the shuffled frog leaping algorithm. Softw. Comput. 22(3), 861–872 (2018)

    Article  Google Scholar 

  106. Yadav, S., Nanda, S.J.: League championship algorithm for clustering. 2015 IEEE Power, Communication and Information Technology Conference (PCITC), IEEE (2015)

    Google Scholar 

  107. Wangchamhan, T., Chiewchanwattana, S., Sunat, K.: Efficient algorithms based on the k-means and chaotic league championship algorithm for numeric, categorical, and mixed-type data clustering. Expert Syst. Appl. 90, 146–167 (2017)

    Article  Google Scholar 

  108. Das, P., Das, D.K., Dey, S.: A new class topper optimization algorithm with an application to data clustering. In: IEEE Transactions on Emerging Topics in Computing (2018)

    Google Scholar 

  109. Chander, S., Vijaya, P., Dhyani, P.: Multi kernel and dynamic fractional lion optimization algorithm for data clustering. Alex. Eng. J. 57(1), 267–276 (2018)

    Article  Google Scholar 

  110. Nayak, J., et al.: Hybrid chemical reaction based metaheuristic with fuzzy c-means algorithm for optimal cluster analysis. Expert Syst. Appl. 79, 282–295 (2017)

    Article  Google Scholar 

  111. Nayak, J., Naik, B., Behera, H.S.: Cluster analysis using firefly-based k-means algorithm: a combined approach. In: Computational Intelligence in Data Mining, pp. 55–64. Springer, Singapore (2017)

    Google Scholar 

  112. Kanungo, D.P., et al.: Hybrid clustering using elitist teaching learning-based optimization: an improved hybrid approach of TLBO. Int. J. Rough Sets Data Anal. (IJRSDA) 3(1), 1–19 (2016)

    Article  Google Scholar 

  113. Nayak, J., et al.: A hybrid elicit teaching learning based optimization with fuzzy c-means (ETLBO-FCM) algorithm for data clustering. Ain Shams Eng. J. (2016)

    Google Scholar 

  114. Wang, R., et al.: Flower pollination algorithm with bee pollinator for cluster analysis. Inf. Process. Lett. 116(1), 1–14 (2016)

    Article  Google Scholar 

  115. Rafi, M., et al.: Solving document clustering problem through meta heuristic algorithm: black hole. In: Proceedings of the 2nd International Conference on Machine Learning and Soft Computing. ACM (2018)

    Google Scholar 

  116. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, 532410, Andhra Pradesh, India

    Janmenjoy Nayak, Paidi Dinesh & Kanithi Vakula

  2. Department of Computer Application, Veer Surendra Sai University of Technology, Burla, Sambalpur, 768018, Odisha, India

    Bighnaraj Naik

  3. Faculty of Communication Sciences, University of Teramo, Coste Sant’Agostino Campus, Teramo, Italy

    Danilo Pelusi

Authors
  1. Janmenjoy Nayak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paidi Dinesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kanithi Vakula
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bighnaraj Naik
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Danilo Pelusi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bighnaraj Naik .

Editor information

Editors and Affiliations

  1. Department of Information Technology, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  2. Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, Andhra Pradesh, India

    Janmenjoy Nayak

  3. Department of Computer Application, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India

    Bighnaraj Naik

  4. Faculty of Communication Sciences, University of Teramo, Teramo, Italy

    Danilo Pelusi

Appendix

Appendix

ABC

Artificial bee colony

ACO

Ant colony optimization

BCO

Bee colony optimization

CMC

Contraceptive method choice

CPSO

Chaotic PSO

CPSFC

Chaotic particle swarm fuzzy clustering

CS

Cuckoo search

CSS

Charged system search

DE

Differential evolution

EPSO

Evolutionary PSO

FCM

Fuzzy c-means

F-CBCT

Fuzzified cuckoo based clustering technique

FCM-IDPSO

Fuzzy c-means-improved self-adaptive particle swarm optimization

FCM2-IDPSO

Fuzzy c-means2-improved self-adaptive particle swarm optimization

FCM-PSO

Fuzzy c-means-particle swarm optimization

FPSO

Fuzzy PSO

GA

Genetic algorithm

GRASP

Greedy randomized adaptive search procedure

GWKMA

Genetic weighted k-means algorithm

HBM

Honeybee mating

IBCO

Improved Bee colony optimization

MFCC

Mel frequency cepstral coefficient

PSO

Particle swarm optimization

QPSO

Quantum-behaved PSO

SI

Swarm intelligence

UCI

UC Irvine

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nayak, J., Dinesh, P., Vakula, K., Naik, B., Pelusi, D. (2020). Applications and Advancements of Nature-Inspired Optimization Algorithms in Data Clustering: A Detailed Analysis. In: Behera, H., Nayak, J., Naik, B., Pelusi, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 990. Springer, Singapore. https://doi.org/10.1007/978-981-13-8676-3_62

Download citation

Publish with us

Policies and ethics

Search

Navigation