Abstract
In this paper we present a simulation tool for the visualization of the impact of different probability distributions on Particle Swarm Optimization (PSO). PSO is influenced by a high number of random values in order to simulate a more nature like behaviour. Based on these random numbers the optimization process may vary. Usually the uniform distribution is chosen but regarding certain underlying fitness functions this may not the best choice. To test the influence of different probability distributions on PSO and to compare the different approaches, the presented simulation system consist of a simple user interface and allows the integration of own distribution formulas in order to test their impact on PSO.
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References
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Network, Perth, Australia, pp. 1942–1948 (1995)
Bratton, D., Kennedy, J.: Defining a standard for particle swarm optimization. In: Swarm Intelligence Symposium, pp. 120–127 (2007)
Shi, Y., Eberhart, R.: Parameter selection in particle swarm optimization. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 591–600. Springer, Heidelberg (1998)
Bogon, T., Poursanidis, G., Lattner, A.D., Timm, I.J.: Automatic Parameter Configuration of Particle Swarm Optimization by Classification of Function Features. In: Dorigo, M., et al. (eds.) ANTS 2010. LNCS, vol. 6234, pp. 554–555. Springer, Heidelberg (2010)
Pan, J.S., Huang, H.C., Jain, L.C.: Intelligent watermarking techniques. World Scientific, River Edge (2004)
Kennedy, J.: Bare bones particle swarms. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, SIS 2003, pp. 80–87. IEEE Servoce Center, Piscataway (2003)
Feng, P., Xiaohui, H., Eberhart, R.C., Yaobin, C.: An analysis of Bare Bones Particle Swarm. In: IEEE Swarm Intelligence Symposium. IEEE, Piscataway (2008)
Richer, T.J., Blackwell, T.M.: The Lévy Particle Swarm. In: IEEE Congress on Evolutionary Computation, CEC 2006, pp. 808–815 (2006)
Kennedy, J.: Dynamic-probabilistic particle swarms. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, GECCO 2005, pp. 201–207. ACM, New York (2005)
Li, C., Liu, Y., Zhou, A., Kang, L., Wang, H.: A fast particle swarm optimization algorithm with cauchy mutation and natural selection strategy. In: Kang, L., Liu, Y., Zeng, S. (eds.) ISICA 2007. LNCS, vol. 4683, pp. 334–343. Springer, Heidelberg (2007)
Krohling, R., dos Santos Coelho, L.: Pso-e: Particle swarm with exponential distribution. In: IEEE Congress on Evolutionary Computation, CEC 2006, pp. 1428–1433 (2006)
Thangaraj, R., Pant, M., Deep, K.: Initializing pso with probability distributions and low-discrepancy sequences: The comparative results. In: World Congress on Nature Biologically Inspired Computing, NaBIC 2009, pp. 1121–1126 (December 2009)
Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley Professional (1994)
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Bogon, T., Lorig, F., Timm, I.J. (2013). Visualizing the Impact of Probability Distributions on Particle Swarm Optimization. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38703-6_14
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DOI: https://doi.org/10.1007/978-3-642-38703-6_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38702-9
Online ISBN: 978-3-642-38703-6
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