Abstract
Particle swarm optimization (PSO) is a stochastic, population-based optimization technique that is inspired by the emigrant behavior of a flock of birds searching for food. In this paper, a nonlinear function of decreasing inertia weight that adapts to current performance of PSO search is presented. Meanwhile, a dynamic mechanism to adjust decrease rates is also suggested. Through the experimental study, the new PSO algorithm with adaptive dynamic weight scheme is compared to the exiting models in terms of various benchmark functions. The computational experience shows some great promise.
Chapter PDF
Similar content being viewed by others
Keywords
References
Goldberg, D.E.: Genetic Algorithms in Search Optimization, and Machine Learning. Addison-Welsey, Reading (1989)
Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Proceedings of CAL’91—European Conference on Artificial Life, pp. 134–142. Elsevier Publishing, Paris, France (1998)
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. pp. 39–43, IEEE Service Center, Piscataway, NJ, Nagoya, Japan (1995)
Angeline, P.J.: Evolutionary optimization versus particle swarm optimization: philosophy and performance differences. In: Porto, V.W., Waagen, D. (eds.) Evolutionary Programming VII. LNCS, vol. 1447, pp. 601–610. Springer, Berlin (1998)
Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 69–73. IEEE Press, Piscataway (1998)
Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization. In: Evolutionary Programming VII. Proc. EP98, pp. 591–600. Springer, New York (1998)
Chatterjee, A., Siarry, P.: Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Computers & Operations Research 33, 859–871 (2006)
Clerc, M.: Discrete Particle Swarm Optimization Illustrated by the Traveling Salesman Problem. Technical report (2000)
Parsopoulos, K.E., Vrahatis, M.N.: Initializing the particle swarm optimizer using the nonlinear simplex method. In: Grmela, A., Mastorakis, N.E. (eds.): Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, pp. 216–221. WSEAS Press (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fan, SK.S., Chang, JM. (2007). A Modified Particle Swarm Optimizer Using an Adaptive Dynamic Weight Scheme. In: Duffy, V.G. (eds) Digital Human Modeling. ICDHM 2007. Lecture Notes in Computer Science, vol 4561. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73321-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-73321-8_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73318-8
Online ISBN: 978-3-540-73321-8
eBook Packages: Computer ScienceComputer Science (R0)