Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

A new meta-heuristic algorithm based on chemical reactions for partitional clustering problems

  • Research Paper
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

In the field of engineering, heuristic algorithms are widely adopted to solve variety of optimization problems. These algorithms have proven its efficacy over classical algorithms. It is seen that chemical reactions consist of an efficient computational procedure to design a new product. The formation of new product contains numbers of objects, states, events and well defined procedural steps. A meta-heuristic algorithm inspired through chemical reaction is developed, called artificial chemical reaction optimization (ACRO) algorithm. In this work, an ACRO algorithm is adopted to solve partitional clustering problems. But, this algorithm suffers with slow convergence rate and sometimes stuck in local optima. To handle these aforementioned problems, two operators are inculcated in ACRO algorithm. The performance of proposed algorithm is tested over well-known clustering datasets. The simulation results confirm that proposed ACRO algorithm is an effective and competitive algorithm to solve 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

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Teppola P, Mujunen SP, Minkkinen P (1999) Adaptive fuzzy C-means clustering in process monitoring. Chemom Intell Lab Syst 45(1):23–38

    Google Scholar 

  2. Zhou H, Liu Y (2008) Accurate integration of multi-view range images using k-means clustering. Pattern Recogn 41(1):152–175

    MATH  Google Scholar 

  3. Webb A (2002) Statistical pattern recognition. Wiley., New Jersey, pp 361–406

    MATH  Google Scholar 

  4. Dunn III WJ, Greenberg MJ Callejas SS (1976) Use of cluster analysis in the development of structure-activity relations for antitumor triazenes. J Med Chem 19(11):1299–1301

    Google Scholar 

  5. Anaya AR, Boticario JG (2011) Application of machine learning techniques to analyses student interactions and improve the collaboration process. Expert Syst Appl 38(2):1171–1181

    Google Scholar 

  6. Hung YS, Chen KLB, Yang CT, Deng GF (2013) Web usage mining for analyzing elder self-care behavior patterns. Expert Syst Appl 40(2):775–783

    Google Scholar 

  7. Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213(3):267–289

    MATH  Google Scholar 

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

    Google Scholar 

  9. Kaveh A, Share MAM, Moslehi M (2013) Magnetic charged system search: a new meta-heuristic algorithm for optimization. Acta Mech 224(1):85–107

    MATH  Google Scholar 

  10. 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

    Google Scholar 

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

    MathSciNet  Google Scholar 

  12. Shah-Hosseini H (2011) Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation. Int J Comput Sci Eng 6(1–2):132–140

    Google Scholar 

  13. Baykasoğlu A, Özbakır L, Tapkan P (2007) Artificial bee colony algorithm and its application to generalized assignment problem. In: Swarm intelligence, focus on ant and particle swarm optimization. InTech, UK

    Google Scholar 

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

    Google Scholar 

  15. Alatas B (2012) A novel chemistry based metaheuristic optimization method for mining of classification rules. Expert Syst Appl 39(12):11080–11088

    Google Scholar 

  16. He Y, Pan W, Lin J (2006) Cluster analysis using multivariate normal mixture models to detect differential gene expression with microarray data. Comput Stat Data Anal 51(2):641–658

    MathSciNet  MATH  Google Scholar 

  17. MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Fifth berkeley symposium on mathematics. Statistics and probability. University of California Press, pp. 281–297

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

    Google Scholar 

  19. Ismkhan H (2018) Ik-means–+: an iterative clustering algorithm based on an enhanced version of the k-means. Pattern Recogn 79:402–413

    Google Scholar 

  20. Tzortzis G, Likas A (2014) The MinMax k-Means clustering algorithm. Pattern Recogn 47(7):2505–2516

    Google Scholar 

  21. Malinen MI, Mariescu-Istodor R, Fränti P (2014) K-means*: Clustering by gradual data transformation. Pattern Recogn 47(10):3376–3386

    Google Scholar 

  22. 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

    Google Scholar 

  23. 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

    Google Scholar 

  24. Shelokar PS, Jayaraman VK, Kulkarni BD (2004) An ant colony approach for clustering. Anal Chim Acta 509(2):187–195

    Google Scholar 

  25. Wan M, Wang C, Li L, Yang Y (2012) Chaotic ant swarm approach for data clustering. Appl Soft Comput 12(8):2387–2393

    Google Scholar 

  26. Huang CL, Huang WC, Chang HY, Yeh YC, Tsai CY (2013) Hybridization strategies for continuous ant colony optimization and particle swarm optimization applied to data clustering. Appl Soft Comput 13(9):3864–3872

    Google Scholar 

  27. Menéndez HD, Otero FE, Camacho D (2016) Medoid-based clustering using ant colony optimization. Swarm Intell 10(2):123–145

    Google Scholar 

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

    Google Scholar 

  29. Chuang LY, Hsiao CJ, Yang CH (2011) Chaotic particle swarm optimization for data clustering. Expert systems with Appl 38(12):14555–14563

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  32. 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

    Google Scholar 

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

    Google Scholar 

  34. Chu SC, Tsai PW, Pan JS (2006, August) Cat swarm optimization. In Pacific rim international conference on artificial intelligence. Springer, Berlin, Heidelberg, pp. 854–858

  35. Mohapatra P, Chakravarty S, Dash PK (2016) Microarray medical data classification using kernel ridge regression and modified cat swarm optimization based gene selection system. Swarm Evolutionary Comput 28:144–160

    Google Scholar 

  36. Kumar Y, Sahoo G (2015) A hybrid data clustering approach based on improved cat swarm optimization and K-harmonic mean algorithm. Ai Commun 28(4):751–764

    MathSciNet  Google Scholar 

  37. 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

    Google Scholar 

  38. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: González JR, Pelta DA, Cruz C, Terrazas G, Krasnogor N (eds) Nature inspired cooperative strategies for optimization (NICSO 2010), vol 284. Springer, Berlin, Heidelberg, pp 65–74

    Google Scholar 

  39. 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

    Google Scholar 

  40. Aboubi Y, Drias H, Kamel N (2016) BAT-CLARA: BAT-inspired algorithm for clustering LARge applications. IFAC-PapersOnLine 49(12):243–248

    Google Scholar 

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

  42. Zhan ZH, Zhang J, Li Y, Chung SH (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern B Cybern 39:1362–1381

    Google Scholar 

  43. Gandomi AH, Yang XS, Alavi AH, Talatahari S (2013) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22(6):1239–1255

    Google Scholar 

  44. Kumar Y, Sahoo G (2016) A hybridize approach for data clustering based on cat swarm optimization. Int J Inf Commun Technol 9(1):117–141

    MathSciNet  Google Scholar 

  45. Baral A, Behera HS (2013) A novel chemical reaction-based clustering and its performance analysis. Int J Bus Intell Data Min 8(2):184–198

    Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Hakam Singh & Yugal Kumar

  2. Department of Computer Science and Engineering, Amity School of Engineering, Amity University, Noida, India

    Sumit Kumar

Authors
  1. Hakam Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yugal Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sumit Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hakam Singh.

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

Singh, H., Kumar, Y. & Kumar, S. A new meta-heuristic algorithm based on chemical reactions for partitional clustering problems. Evol. Intel. 12, 241–252 (2019). https://doi.org/10.1007/s12065-019-00221-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-019-00221-w

Keywords

Search

Navigation