Abstract
This paper introduces a novel version of the particle swarm optimisation (PSO) algorithm which we call self-organising swarm SOSwarm. SOSwarm can be used for unsupervised learning. In the algorithm, input vectors are projected into a lower-dimensional map space producing a visual representation of the input data in a manner similar to a self-organising map (SOM). In SOSwarm, particles react to input data during the learning process by modifying their velocities using an adaptation of the PSO velocity update function. SOSwarm is successfully applied to ten benchmark problems drawn from the UCI Machine Learning repository. The paper also demonstrates how the canonical SOM can be explored within the PSO paradigm. Illustrating this linkage between the heretofore distinct literatures of SOM and PSO opens up several new avenues of research for the development of novel self-organising algorithms.
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
Brabazon A, O’Neill M (2006) Biologically inspired algorithms for financial modelling. Springer, Berlin
Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, Oxford
Chung F-L, Wang S, Deng Z, Shu C, Hu D (2006) Clustering analysis of gene expression data based on semi-supervised visual clustering algorithm. Soft Comput. 10(11): 981–993
De Falco I, Tarantino E, Delia Cioppa A, Gagliardi F (2005) A novel grammar-based genetic programming approach to clustering. In: Proceedings of the 2005 ACM symposium on applied computing, Santa Fe, New Mexico, pp 928–932
De Falco I, Tarantino E, Delia Cioppa A, Fontanella F (2006) An innovative approach to genetic programmingf́9based clustering. Adv Soft Comput 55–64
Dempster A, Laird N, Rubin D (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B 39(1): 1–38
Deneubourg J, Gross S, Franks N, Sendova-Franks A, Detrain C, Chretien L (1991) The dynamics of collective sorting robot-like ants and ant-like robots. In: Meyer J, Wilson S(eds) Proceedings of 1st conference on simulation of adaptive behavior: from animals to animats (SAB 90). MIT Press, Cambridge, pp 356–365
Dunn J (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybern 3: 32–57
Franti P, Kivijarvi J, Kaukoranta T, Nevalainen O (1997) Genetic algorithms for large scale clustering problems. Comput J 40: 547–554
Garai G, Chaudhuri B (2004) A novel genetic algorithm for automatic clustering. Pattern Recognit Lett 25(2): 173–187
Gurney K (1997) An introduction to neural networks. University College London Press, London
Hettich S, Blake CL, Merz CJ (1998) UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.htm. University of California, Department of Information and Computer Science, Irvine, CA
Jiang K, Liao Q-M, Xiong Y (2006) A novel white blood cell segmentation scheme based on feature space clustering. Soft Comput 10(1): 12–19
Johnson S (1967) Hierarchical clustering schemes. Psychometrika 2: 241–254
Karakasidis T, Georgiou D (2004) Partitioning elements of the Periodic Table via fuzzy clustering technique. Soft Comput 8(3): 231–236
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, pp 1942–1948
Kennedy J, Eberhart R, Shi Y (2001) Swarm intelligence. Morgan Kauffman, San Mateo
Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43: 59–69
Kohonen T (1990) The self-organizing map. Proc IEEE 78(9): 1464–1480
Kohonen T (1998) The SOM methodology. In: Deboeck G, Kohonen T Visual explorations in finance with self-organizing maps. Springer, Berlin
Lumer E, Faieta B (1994) Diversity and adaptation in populations of clustering ants. In: Proceedings of third international conference on simulation of adaptive behaviour, pp 501–508
MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley symposium on mathematical statistics and probability, vol 1. University of California Press, Berkeley, pp 281–297
Maulik U, Bandyopadhyay S (2000) Genetic algorithm-based clustering technique. Pattern Recognit 33: 1455–1465
Omran M, Engelbrecht AP, Salman A (2005) Particle swarm optimization method for image clustering. Int J Pattern Recognit Artif Intell 19(3): 297–322
Omran MGH, Salman A, Engelbrecht AP (2006) Dynamic clustering using particle swarm optimization with application in image segmentation. Pattern Anal Appl 8(4): 332–344
O’Neill M, Ryan C (2003) Grammatical evolution: evolutionary automatic programming in an arbitrary language. Kluwer Academic Publishers, Boston Computation 5(4): 349–358
O’Neill M, Brabazon A (2006) Grammatical swarm: the generation of programs by social programming. Nat Comput 5: 443–462
O’Neill M, Brabazon A, Adley C (2004) The automatic generation of programs for classification using grammatical swarm. In: Proceedings of the congress on evolutionary computation CEC 2004. IEEE Press, Portland, pp 104–110
O’Neill M, Brabazon A (2004) Grammatical swarm. In: Proceedings of the genetic and evolutionary computation conference GECCO 2004. Springer, Seattle, pp 163–174
Rahimi-Vahed AR, Mirghorbani SM, Rabbani M (2007) A new particle swarm algorithm for a multi-objective mixed-model assembly line sequencing problem. Soft Comput 11(10): 997–1012
Smith M, Bull L (2005) Genetic programming with a genetic algorithm for feature construction and selection. Genet Program Evol Mach 6(3): 265–281
Tseng L, Yang S (2001) A genetic approach to the automatic clustering problem. Pattern Recognit 34: 415–424
Wang P, Liu Z-Q, Yang S-Q (2007) Investigation on unsupervised clustering algorithms for video shot categorization. Soft Comput 11(4): 355–360
Xiao X, Dow E, Eberhart R, Miled Z, Oppelt R (2003) Gene-clustering using self-organizing maps and particle swarm optimization. In: Proceedings of the IEEE international parallel and distributed processing symposium (IPDPS), 22–26 April 2003. IEEE Press, Nice
Xiao X, Dow E, Eberhart R, Miled Z, Oppelt R (2004) A hybrid self-organizing maps and particle swarm optimization approach. Concur Comput Pract Exp 16(9): 895–915
Yue X, Abraham A, Chi Z-X, Hao Y-Y, Mo H (2007) Artificial immune system inspired behavior-based anti-spam filter. Soft Comput. 11(8): 729–740
Yang C, Yi Z (2008) Document clustering using locality preserving indexing and support vector machines. Soft Comput (published online 17 Oct 2007, in press)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
O’Neill, M., Brabazon, A. Self-organising swarm (SOSwarm). Soft Comput 12, 1073–1080 (2008). https://doi.org/10.1007/s00500-007-0274-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-007-0274-8