Abstract
In the field of data analysis, clustering is a powerful technique which groups the data into different subsets using a distance function. Data belonging to the same subset are similar in nature and offer heterogeneity to the data that reside in other subsets. Clustering has proved its potentiality in various fields such as bioinformatics, pattern recognition, image processing and many more. In this paper, a two-step artificial bee colony (ABC) algorithm is proposed for efficient data clustering. In two-step ABC algorithm, the initial positions of food sources are identified using the K-means algorithm instead of random initialization. Along this, to discover the promising search areas, an improved solution search equation based on social behavior of PSO is applied in the onlooker bee phase of ABC algorithm and abandoned food source location is found by using Hooke and Jeeves-based direct search method. Five benchmark and two artificial datasets are applied to validate the proposed modifications in the ABC algorithm, and results of this study are compared with other well-known clustering algorithms. Both the experimental and statistical analyses show that improvements in ABC algorithm have an advantage over the conventional ABC algorithm for solving clustering problems.
Similar content being viewed by others
References
Bakhtiyari K, Husain H (2014) Fuzzy model of dominance emotions in affective computing. Neural Comput Appl 25:1467–1477
Jordehi AR, Jasni J (2011) A comprehensive review on methods for solving FACTS optimization problem in power systems. Int Rev Electr Eng 6:1916–1926
Jordehi AR, Jasni J (2012) Approaches for FACTS optimization problem in power systems. In: IEEE international conference on power engineering and optimization (PEDCO), pp 355–360
Taghavi M, Bakhtiyari K, Scavino E (2013) Agent-based computational investing recommender system. In: Proceedings of the 7th ACM conference on recommender systems, pp 455–458
Jordehi AR, Joorabian M (2011) Optimal placement of multi-type FACTS devices in power systems using evolution strategies. In: 5th international conference on power engineering and optimization (PEOCO), pp 352–357
Jordehi AR, Jasni J, Wahab N, Kadir MZ (2013) Particle swarm optimization applications in FACTS optimization problem. In: IEEE 7th international conference on power engineering and optimization (PEOCO), pp 193–198
Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: IEEE international conference on systems, man, and cybernetics, computational cybernetics and simulation, vol 5, pp 4104–4108
Jordehi AR (2014) Particle swarm optimization for dynamic optimization problems: a review. Neural Comput Appl 25:1507–1516
Jordehi AR, Jasni J, Wahab N, Kadir MZ, Javadi MS (2015) Enhanced leader PSO (ELPSO): a new algorithm for allocating distributed TCSC’s in power systems. Int J Electr Power Energy Syst 64:771–784. doi:10.1016/j.ijepes.2014.07.058
Jordehi AR, Jasni J, Wahab N, Kadir MZ, Javadi MS (2015) Enhanced leader PSO (ELPSO): a new algorithm for allocating distributed TCSC’s in power systems. Int J Electr Power Energy Syst 64:771–784. doi:10.1016/j.ijepes.2014.07.058
Jordehi AR (2015) Enhanced leader PSO (ELPSO): a new PSO variant for solving global optimization problems. Appl Soft Comput 26:401–417
Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Michigan
Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315
Jordehi AR (2014) Optimal setting of TCSC’s in power systems using teaching–learning-based optimization algorithm. Neural Comput Appl. doi:10.1007/s00521-014-1791-x
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Erciyes University, Kayseri, Turkey, Technical Report-TR06
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471
Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132
Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173
Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B Cybern 26(1):29–41
Blum C (2005) Ant colony optimization: introduction and recent trends. Phys Life Rev 2(4):353–373
Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213(3–4):267–289
Kaveh A, Talatahari S (2010) Optimal design of skeletal structures via the charged system search algorithm. Struct Multidiscip Optim 41(6):893–911
Dai C, Chen W, Zhu Y, Zhang X (2009) Seeker optimization algorithm for optimal reactive power dispatch. IEEE Trans Power Syst 24(3):1218–1231
Jordehi AR (2015) Seeker optimization (human group optimization) algorithm with chaos. J Exp Theor Artif Intell 27(6):753–762
Erol OK, Eksin I (2006) A new optimization method: big bang–big crunch. Adv Eng Softw 37(2):106–111
Jordehi AR (2014) A chaotic-based big bang–big crunch algorithm for solving global optimization problems. Neural Comput Appl 25:1329–1335
Shi Y (2011) Brain storm optimization algorithm. In: Advances in swarm intelligence. Springer, Berlin, pp 303–309
Jordehi AR (2015) Brainstorm optimization algorithm (BSOA): an efficient algorithm for finding optimal location and setting of FACTS devices in electric power systems. Int J Electr Power Energy Syst 69:48–57
Yang XS (2010) A new meta-heuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO), pp 65–74
Jordehi AR (2015) Chaotic bat swarm optimization (CBSO). Appl Soft Comput 26:523–530
Jordehi R (2011) Heuristic methods for solution of FACTS optimization problem in power systems. In: IEEE student conference on research and development, pp 30–35
Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–322
Lingras P, Huang X (2005) Statistical, evolutionary, and neurocomputing clustering techniques: cluster-based vs object-based approaches. Artif Intell Rev 23(1):3–29
Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678
Scheunders P (1997) A genetic c-means clustering algorithm applied to color image quantization. Pattern Recognit 30(6):859–866
Gomez-Muñoz VM, Porta-Gándara MA (2002) Local wind patterns for modeling renewable energy systems by means of cluster analysis techniques. Renew Energy 2:171–182
Mitra S, Banka H (2006) Multi-objective evolutionary bi clustering of gene expression data. Pattern Recognit 39:2464–2477
Chang DX, Zhang XD, Zheng CW (2009) A genetic algorithm with gene rearrangement for K-means clustering. Pattern Recognit 42:1210–1222
Ester M, Kriegel HP, Sander J (1996) A density-based algorithm for discovering clusters in large spatial data bases with noise. In: Proceedings of the 2nd international conference on knowledge discovery and data mining, pp 226–231
Madeira SC, Oliveira AL (2004) Bi clustering algorithms for biological data analysis: a survey. IEEE Trans Comput Bioinform 1(1):24–45
Dehuri S, Ghosh A, Mall R (2006) Genetic algorithms for multi-criterion classification and clustering in data mining. Int J Comput Inf Syst 4(3):143–154
Hong Y, Kwong S, Chang YC, Ren QS (2008) Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm. Pattern Recognit 41:2742–2756
Murthy CA, Chowdhury N (1996) In search of optimal clusters using genetic algorithms. Pattern Recognit Lett 17(8):825–832
Tseng LY, Bien Yang S (2001) A genetic approach to the automatic clustering problem. Pattern Recognit 34(2):415–424
Krishna K, Murty MN (1999) Genetic K-means algorithm. IEEE Trans Syst Man Cybern B Cybern 29(3):433–439
Hong Y, Kwong S (2008) To combine steady-state genetic algorithm and ensemble learning for data clustering. Pattern Recognit Lett 29(9):1416–1423
Van der Merwe DW, Engelbrecht AP (2003) Data clustering using particle swarm optimization. In: IEEE congress on evolutionary computation, vol 1, pp 215–220
Kao YT, Zahara E, Kao IW (2008) A hybridized approach to data clustering. Expert Syst Appl 34(3):1754–1762
Tsai CY, Kao IW (2011) Particle swarm optimization with selective particle regeneration for data clustering. Expert Syst Appl 38(6):6565–6576
Shelokar PS, Jayaraman VK, Kulkarni BD (2004) An ant colony approach for clustering. Anal Chim Acta 509(2):187–195
Niknam T, Amiri B (2010) An efficient hybrid approach based on PSO, ACO and <i>k</i> means for cluster analysis. Appl Soft Comput 10(1):183–197
Satapathy SC, Naik A (2011) Data clustering based on teaching–learning-based optimization. In: Swarm, evolutionary, and memetic computing. Springer, Berlin, pp 148–156
Sahoo AJ, Kumar Y (2014) Modified teacher learning based optimization method for data clustering. In: Advances in signal processing and intelligent recognition systems. Springer, Berlin, pp 429–437
Al-Sultan KS (1995) A Tabu search approach to the clustering problem. Pattern Recognit 28:1443–1451
Santosa B, Ningrum MK (2009) Cat swarm optimization for clustering. In: IEEE international conference of soft computing and pattern recognition, pp 54–59
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
Kumar Y, Sahoo G (2015) Gaussian cat swarm optimization algorithm based on Monte Carlo method for data clustering. Int J Comput Sci Eng (in press)
Karaboga D, Ozturk C (2011) A novel clustering approach: artificial bee colony (ABC) algorithm. Appl Soft Comput 11(1):652–657
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
Zhang Y, Wu L, Wang S, Huo Y (2011) Chaotic artificial bee colony used for cluster analysis. In: Intelligent computing and information science. Springer, Berlin, pp 205–211
Kumar Y, Sahoo G (2014) A charged system search approach for data clustering. Prog Artif Intell 2(2–3):153–166
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
Kang F, Li JJ, Xu Q (2009) Structural inverse analysis by hybrid simplex artificial bee colony algorithms. Comput Struct 87:861–870
Kang F, Li JJ, Ma ZY (2011) Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions. Inf Sci 181:3508–3531
Alatas B (2010) Chaotic bee colony algorithms for global numerical optimization. Expert Syst Appl 37:5682–5687
Duan HB, Xu CF, Xing ZH (2010) A hybrid artificial bee colony optimization and quantum evolutionary algorithm for continuous optimization problems. Int J Neural Syst 20:39–50
Banharnsakun A, Achalakul T, Sirinaovakul B (2011) The best-so-far selection in artificial bee colony algorithm. Appl Soft Comput 11:2888–2901
Li GQ, Niu PF, Xiao XJ (2012) Development and investigation of efficient artificial bee colony algorithm for numerical function optimization. Appl Soft Comput 12:320–332
Gao WF, Liu SY, Huang LL (2013) A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE Trans Syst Man Cybern Part B 43:1011–1024
Coelho LS, Alotto P (2011) Gaussian artificial bee colony algorithm approach applied to Loney’s solenoid benchmark problem. IEEE Trans Magn 47:1326–1329
Gao WF, Liu SY (2012) A modified artificial bee colony algorithm. Comput Oper Res 39:687–697
Alam MS, Kabir MW, Islam MM (2010) Self-adaptation of mutation step size in artificial bee colony algorithm for continuous function optimization. In: Proceedings of the 13th international conference on computer and information technology, vol 3, pp 23–25
Kang F, Li J, Li H (2013) Artificial bee colony algorithm and pattern search hybridized for global optimization. Appl Soft Comput 13(4):1781–1791
Jadhav HT, Roy R (2013) Gbest guided artificial bee colony algorithm for environmental/economic dispatch considering wind power. Expert Syst Appl 40(16):6385–6399
MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol 1, pp 281–297
Hooke R, Jeeves TA (1961) Direct search solution of numerical and statistical problems. J ACM 8(2):212–229
Wang YJ, Zhang JS (2007) Global optimization by an improved differential evolutionary algorithm. Appl Math Comput 188(1):669–680
Al-Sultan KS, Al-Fawzan MA (1997) A tabu search Hooke and Jeeves algorithm for unconstrained optimization. Eur J Oper Res 103(1):198–208
Rios-Coelho AC, Sacco WF, Henderson N (2010) A Metropolis algorithm combined with Hooke–Jeeves local search method applied to global optimization. Appl Math Comput 217(2):843–853
Torczon V (1997) On the convergence of pattern search algorithms. SIAM J Optim 7(1):1–25
http://archive.ics.uci.edu/ml/. Access date 8 Nov 2014
Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1(1):3–18
García S, Fernández A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf Sci 180(10):2044–2064
Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
kumar, Y., Sahoo, G. A two-step artificial bee colony algorithm for clustering. Neural Comput & Applic 28, 537–551 (2017). https://doi.org/10.1007/s00521-015-2095-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-015-2095-5