Abstract
A hybrid clustering method is proposed in this paper based on artificial immune system and simulated annealing. An integration of simulated annealing and immunity-based algorithm, combining the merits of both these approaches, is used for developing an efficient clustering method. Tuning the parameters of method is investigated using Taguchi method in order to select the optimum levels of parameters. Proposed method is implemented and tested on three real datasets. In addition, its performance is compared with other well-known meta-heuristics methods, such as ant colony optimization, genetic algorithm, simulated annealing, Tabu search, honey-bee mating optimization, and artificial immune system. Computational simulations show very encouraging results in terms of the quality of solution found, the average number of function evaluations and the processing time required, comparing with mentioned methods.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Han J, Kamber M (2001) Data mining concepts and techniques. Morgan Kaufman, San Francisco
Rajendran C, Ziegler H (2004) Ant-colony algorithms for permutation flow-shop scheduling to minimize makespan/total flowtime of jobs. Eur J Oper Res 155:26–38
Safari E, Sadjadi SJ, Shahanaghi K (2010) Scheduling flowshops with condition-based maintenance constraint to minimize expected makespan. Int J Adv Manuf Technol 46:757–767
C. Kahraman, O. Engin, M.K. Yilmaz (2009), A new artificial immune system algorithm for multiobjective fuzzy flow shop problems, vol. 2, no. 3, pp. 236–247
Chen PC, Chen CW, Chiang WL (2009) GA-based modified adaptive fuzzy sliding mode controller for nonlinear systems. Expert Syst Appl 36(3):5872–5879
Shelokar PS, Jayaraman VK, Kulkarni BD (2004) An ant colony approach for clustering. Anal Chim Acta 509(1):187–195
Sung CS, Jin HW (2000) A tabu-search-based heuristic for clustering. Pattern Recogn 33(3):849–858
Mualik U, Bandyopadhyay S (2000) Genetic algorithm-based clustering technique. Pattern Recogn 33(2):1455–1465
Selim SZ, Al-Sultan K (1991) A simulated annealing algorithm for the clustering problem. Pattern Recogn 24(10):1003–1008
Fathian M, Amiri B, Maroosi A (2007) Application of honey-bee mating optimization algorithm on clustering. Appl Math Comput 190:1502–1513
Timmis J, Honec A, Stibord T, Clarka E (2008) Theoretical advances in artificial immune systems. Theoretical Computer Science 403(1):11–32
J Timmis, MJ Neal (2000) A resource limited artificial immune system for data analysis. Research and Development in Intelligent Systems XVII, Proceedings of the ES2000, Cambridge, pp. 19–32
Tavakkoli-Moghaddam R, Rahimi-Vahed A, Mirzaei A (2007) A hybrid multi-objective immune algorithm for a flow shop scheduling problem with bi-objectives: weighted mean completion time and weighted mean tardiness. Inf Sci 177(22):5072–5090
Kumar A, Prakash A, Shankar R, Tiwari MK (2006) Psycho-clonal algorithm based approach to solve continuous flow shop scheduling problem. Expert Syst Appl 31(3):504–514
Tsai J-T, Ho W-H, Liu T-K, Chou J-H (2007) Improved immune algorithm for global numerical optimization and job-shop scheduling problems. Appl Math Comput 94(2):406–424
Chandrasekaran M, Asokan P, Kumanan S, Balamurugan T, Nickolas S (2006) Solving job shop scheduling problems using artificial immune system. Int J Adv Manuf Technol 31:580–593
de Castro LN, Timmis J (2002) Artificial immune systems: a new computational intelligence approach. Springer, Berlin
Dasgupta D, Forrest S (1996) Novelty detection in time series data using ideas from immunology. Proc ISCA’96, Reno, Nevada, pp. 19–21
Forrest S, Hoffmeyr SA (2000) Engineering an immune system. Graft 4(5):5–9
Burnet FM (1959) The clonal selection theory of acquired immunity. Cambridge University Press, Cambridge
L.N. De Castro, J. Timmis (2002), An artificial immune network for multimodal function optimization, Proceedings of the IEEE Congress on Evolutionary Computation, vol. 1, IEEE Press, Piscataway, NJ, pp. 674–699
J. Kelsey, J. Timmis (2003) Immune inspired somatic contiguous hyper mutation for function optimization. Genetic and Evolutionary Computation Conference. Lecture Notes in Computer Science, vol. 2723. Springer, Berlin, pp. 207–218
De Castro LN, Von Zuben FJ (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evol Comput 6(3):239–251
Forrest S, Perelson AS, Allen L, Cherukuri R (1994) Self-nonself discrimination in a computer. IEEE Symposium on Research in Security and Privacy. IEEE Computer Society Press, Los Alamos
Jerne NK (1974) Towards a network theory of the immune system. Ann Immunol (Inst Pasteur) 125C:373–389
De Castro LN, von Zuben FJ (2001) aiNet: an artificial immune network for data analysis. Idea Group Publishing, Hershey, pp 231–259 (Chapter 12)
Hiller FS (2003) Handbook of metahueristics. Kluwer, Boston
Jeff F, Hamada W, Michael C (2002) Experiments: planning, analysis, and parameter design optimization. Wiley, New York
Box G, Draper E, Norman P (2007) Response surfaces, mixtures, and ridge analyses, second edition of empirical model-building and response surfaces. Wiley, New York
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Abdi, K., Fathian, M. & Safari, E. A novel algorithm based on hybridization of artificial immune system and simulated annealing for clustering problem. Int J Adv Manuf Technol 60, 723–732 (2012). https://doi.org/10.1007/s00170-011-3632-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-011-3632-8