Evolutionary Intelligence

, Volume 12, Issue 2, pp 241–252 | Cite as

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

  • Hakam SinghEmail author
  • Yugal Kumar
  • Sumit Kumar
Research Paper


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.


Artificial chemical reaction optimization Clustering Meta-heuristic algorithms Chemical reaction 



  1. 1.
    Teppola P, Mujunen SP, Minkkinen P (1999) Adaptive fuzzy C-means clustering in process monitoring. Chemom Intell Lab Syst 45(1):23–38Google Scholar
  2. 2.
    Zhou H, Liu Y (2008) Accurate integration of multi-view range images using k-means clustering. Pattern Recogn 41(1):152–175zbMATHGoogle Scholar
  3. 3.
    Webb A (2002) Statistical pattern recognition. Wiley., New Jersey, pp 361–406zbMATHGoogle Scholar
  4. 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):1299–1301Google Scholar
  5. 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):1171–1181Google Scholar
  6. 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):775–783Google Scholar
  7. 7.
    Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213(3):267–289zbMATHGoogle Scholar
  8. 8.
    Kumar Y, Sahoo G (2014) A charged system search approach for data clustering. Progress Artif Intell 2(2–3):153–166Google Scholar
  9. 9.
    Kaveh A, Share MAM, Moslehi M (2013) Magnetic charged system search: a new meta-heuristic algorithm for optimization. Acta Mech 224(1):85–107zbMATHGoogle Scholar
  10. 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):3621–3645Google Scholar
  11. 11.
    Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inform Sci 222:175–184MathSciNetGoogle Scholar
  12. 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):132–140Google Scholar
  13. 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, UKGoogle Scholar
  14. 14.
    Alatas B (2011) ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst Appl 38(10):13170–13180Google Scholar
  15. 15.
    Alatas B (2012) A novel chemistry based metaheuristic optimization method for mining of classification rules. Expert Syst Appl 39(12):11080–11088Google Scholar
  16. 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):641–658MathSciNetzbMATHGoogle Scholar
  17. 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. 281–297Google Scholar
  18. 18.
    Žalik KR (2008) An efficient k′-means clustering algorithm. Pattern Recogn Lett 29(9):1385–1391Google Scholar
  19. 19.
    Ismkhan H (2018) Ik-means–+: an iterative clustering algorithm based on an enhanced version of the k-means. Pattern Recogn 79:402–413Google Scholar
  20. 20.
    Tzortzis G, Likas A (2014) The MinMax k-Means clustering algorithm. Pattern Recogn 47(7):2505–2516Google Scholar
  21. 21.
    Malinen MI, Mariescu-Istodor R, Fränti P (2014) K-means*: Clustering by gradual data transformation. Pattern Recogn 47(10):3376–3386Google Scholar
  22. 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 104:156–164Google Scholar
  23. 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):29–41Google Scholar
  24. 24.
    Shelokar PS, Jayaraman VK, Kulkarni BD (2004) An ant colony approach for clustering. Anal Chim Acta 509(2):187–195Google Scholar
  25. 25.
    Wan M, Wang C, Li L, Yang Y (2012) Chaotic ant swarm approach for data clustering. Appl Soft Comput 12(8):2387–2393Google Scholar
  26. 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):3864–3872Google Scholar
  27. 27.
    Menéndez HD, Otero FE, Camacho D (2016) Medoid-based clustering using ant colony optimization. Swarm Intell 10(2):123–145Google Scholar
  28. 28.
    Cura T (2012) A particle swarm optimization approach to clustering. Expert Syst Appl 39(1):1582–1588Google Scholar
  29. 29.
    Chuang LY, Hsiao CJ, Yang CH (2011) Chaotic particle swarm optimization for data clustering. Expert systems with Appl 38(12):14555–14563Google Scholar
  30. 30.
    Hatamlou A (2017) A hybrid bio-inspired algorithm and its application. Appl Intell 47(4):1059–1067Google Scholar
  31. 31.
    Jiang B, Wang N (2014) Cooperative bare-bone particle swarm optimization for data clustering. Soft Comput 18(6):1079–1091Google Scholar
  32. 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 67:172–182Google Scholar
  33. 33.
    Hatamlou A, Hatamlou M (2013) PSOHS: an efficient two-stage approach for data clustering. Memetic Comput 5(2):155–161Google Scholar
  34. 34.
    Chu SC, Tsai PW, Pan JS (2006, August) Cat swarm optimization. In Pacific rim international conference on artificial intelligence. Springer, Berlin, Heidelberg, pp. 854–858Google Scholar
  35. 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 28:144–160Google Scholar
  36. 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):751–764MathSciNetGoogle Scholar
  37. 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):2681–2697Google Scholar
  38. 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 65–74Google Scholar
  39. 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):599–603Google Scholar
  40. 40.
    Aboubi Y, Drias H, Kamel N (2016) BAT-CLARA: BAT-inspired algorithm for clustering LARge applications. IFAC-PapersOnLine 49(12):243–248Google Scholar
  41. 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 73–82Google Scholar
  42. 42.
    Zhan ZH, Zhang J, Li Y, Chung SH (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern B Cybern 39:1362–1381Google Scholar
  43. 43.
    Gandomi AH, Yang XS, Alavi AH, Talatahari S (2013) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22(6):1239–1255Google Scholar
  44. 44.
    Kumar Y, Sahoo G (2016) A hybridize approach for data clustering based on cat swarm optimization. Int J Inf Commun Technol 9(1):117–141MathSciNetGoogle Scholar
  45. 45.
    Baral A, Behera HS (2013) A novel chemical reaction-based clustering and its performance analysis. Int J Bus Intell Data Min 8(2):184–198Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringJaypee University of Information TechnologyWaknaghatIndia
  2. 2.Department of Computer Science and EngineeringAmity School of Engineering, Amity UniversityNoidaIndia

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