Abstract
Swarm intelligence describes the ability of groups of social animals and insects to exhibit highly organized and complex problem-solving behaviors that allow the group as a whole to accomplish tasks which are beyond the capabilities of any individual. This phenomenon found in nature is the inspiration for swarm intelligence algorithms—systems that utilize the emergent patterns found in natural swarms to solve computational problems. In this paper, we will show that due to their implicitly parallel structure, swarm intelligence algorithms of all sorts can benefit from GPU-based implementations. To this end, we present the ClusterFlockGPU algorithm, a swarm intelligence data mining algorithm for partitional cluster analysis based on the flocking behaviors of birds and implemented with CUDA. Our results indicate that ClusterFlockGPU is competitive with other swarm intelligence and traditional clustering methods. Furthermore, the algorithm exhibits a nearly linear time complexity with respect to the number of data points being analyzed and running time is not affected by the dimensionality of the data being clustered, thus making it well-suited for high-dimensional data sets. With the GPU-based implementation adopted here, we find that ClusterFlockGPU is up to 55x times faster than a sequential implementation and its time complexity is significantly reduced to nearly O(n).
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References
Charles JS, Potok TE, Patton R, Cui X (2007) Flocking-based document clustering on the graphics processing unit. NICSO, In, pp 27–37
Cui X, Potok TE (2006) A distributed agent implementation of multiple species flocking model for document partitioning clustering. Lect Notes Comput Sci 4149:124–137
Dorigo M, Caro GD (1999) Ant algorithms for discrete optimization. Artif Life 5:137–172
Dorigo M, Stützle T (1999) The ant colony optimization meta-heuristic. New ideas in optimization, McGraw Hill, London, pp 11–32
Dorigo M, StĂĽtzle T (2004) Ant colony optimization. MIT Press, Cambridge
Grosan C, Abraham A, Chis M (2006) Swarm intelligence in data mining. Stud Comput Intell 34:1–20
Handl J, Knowles J, Dorigo M (2003) On the performance of ant-based clustering. In: 3rd International conference on hybrid intelligent systems, IOS Press, Amsterdam, pp 204–213.
Irvine UC (2010) Uci machine learning repository. http://archive.ics.uci.edu/ml/
Merwe DVD, Engelbrecht A (2003) Data clustering using particle swarm optimization. Proceedings of IEEE congress on evolutionary computation, IEEE, Canberra, In, pp 215–220
Olfati-Saber R (2004) Flocking for multi-agent dynamic systems: algorithms and theory. Technical report CIT-CDS 2004–005, California Institute of Technology.
Palathingal P, Cui X, Potok TE (2005) Document clustering using particle swarm optimization. Special issue on Efficient heuristics for information, organization, (Special issue).
Parpinelli RS, Lopes HS, Freitas AA (2001) An ant colony based system for data mining: application to medial data. In: Proceedings of the genetic and evolutionary computation conference 2001, Morgan Kaufmann Publishers, San Francisco, pp 791–797.
Reynolds CW (1987) Flocks, herds, and schools: a distributed behavioral model. Comput Graph 21(4), (Anaheim, California, ACM SIGGRAPH ’87 Conference Proceedings), pp 25–34.
Veenhuis C, Köppen M (2006) Data swarm clustering. Stud. Comput Intell 34:221–241
Weiss RM (2011) GPU-accelerated ant colony optimization. In: Wen-mei Hwu W (ed) GPU computing gems emerald, Chap. 22. Morgan Kaufmann Publishing. ISBN: 978-0-12-384988-5.
Zhang Y, Mueller F, Cui X, Potok T (2011) Data-intensive document clustering on graphics processing unit (GPU) clusters. J Parallel Distrib Comput Data Intensive Comput 71(2):211–224
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Weiss, R.M. (2013). Accelerating Swarm Intelligence Algorithms with GPU-Computing. In: Yuen, D., Wang, L., Chi, X., Johnsson, L., Ge, W., Shi, Y. (eds) GPU Solutions to Multi-scale Problems in Science and Engineering. Lecture Notes in Earth System Sciences. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16405-7_31
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DOI: https://doi.org/10.1007/978-3-642-16405-7_31
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