Abstract
Particle swarm optimization (PSO) is a new robust swarm intelligence technique, which has exhibited good performance on well-known numerical test problems. Though many improvements published aims to increase the computational efficiency, there are still many works need to do. Inspired by evolution programming theory, this paper proposes a new adaptive particle swarm optimization in which the velocity threshold dynamically changes during the course of a simulation. Seven benchmark functions are used to testify the new algorithm, and the results showed clearly the new adaptive PSO leads to a significantly better performance, although the performance improvements were found to be dependent on problems.
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Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43 (1995)
Merwe, v.d., Engelbrecht, A.P.: Data clusting using particle swarm optimization. In: Proceedings of IEEE Congress on Evolutionary Computation, Canbella, Australia, pp. 215–220 (2003)
Wachowiak, M.P., Smolikova, R., Zheng, Y.F., Zurada, J.M., Elmaghraby, A.S.: An approach to multimodal biomedical image registration ulilizing particle swarm optimization. IEEE Transaction on Evolutionary Computation 8, 289–301 (2004)
Tillett, J., Rao, R., Sahin, F.: Cluster-head identification in ad hoc sensor networks using particle swarm optimization. In: Proceedings of IEEE Congress on Evolutionary Computation, Honolulu Hawaii, USA, pp. 201–205 (2002)
Coello, C.A., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. IEEE Transaction on Evolutionary Computation 8, 256–279 (2004)
Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization. In: Proceedings of the Seventh Annual Conference on Evolutionary Programming, New York, pp. 591–600 (1998)
Yasuda, K., Iwasaki, N.: Adaptive particle swarm optimization using velocity information of swarm. In: Proceedings of the IEEE International Conference on System, Man and Cybernetics, Hague, Netherlands, pp. 3475–3481 (2004)
Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence Through Simulated Evolution. Wiley, New York (1966)
Fogel, D.B.: Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway, NJ (1995)
Yao, X., Liu, Y., Lin, G.M.: Evolutionary programming made faster. IEEE Transaction on Evolutionary Computation 3, 82–102 (1999)
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© 2006 Springer-Verlag Berlin Heidelberg
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Cui, Z., Zeng, J., Sun, G. (2006). Adaptive Velocity Threshold Particle Swarm Optimization. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_47
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DOI: https://doi.org/10.1007/11795131_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36297-5
Online ISBN: 978-3-540-36299-9
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