Abstract
This work merges ideas from two very different areas: Particle Swarm Optimisation and Evolutionary Game Theory. In particular, we are looking to integrate strategies from the Prisoner Dilemma, namely cooperate and defect, into the Particle Swarm Optimisation algorithm. These strategies represent different methods to evaluate each particle’s next position. At each iteration, a particle chooses to use one or the other strategy according to the outcome at the previous iteration (variation in its fitness). We compare some variations of the newly introduced algorithm with the standard Particle Swarm Optimiser on five benchmark problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco (2001)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Franken, N., Engelbrecht, A.P.: Co-Evolutionary Particle Swarm Optimization Applied to the 7×7 Seega Game. IEEE Transactions on Evolutionary Computation 9(6), 562–579 (2005)
Abdelbar, A.M., Ragab, S., Mitri, S.: Particle Swarm Optimization Approaches to Coevolve Strategies for the Iterated PrisonerÕs Dilemma. In: Proceedings 2004 IEEE International Joint Conference on Neural Networks, vol. 1, pp. 243–248 (2004)
Pavlidis, N.G., Parsopoulos, K.E., Vrahatis, M.N.: Computing Nash Equilibria through Computational Intelligence Methods. Journal of Computational and Applied Mathematics 175, 113–136 (2005)
Cui, Z., Cai, X., Zeng, J., Sun, G.: Predicted-Velocity Particle Swarm Optimization Using Game-Theoretic Approach. In: Huang, D.-S., Li, K., Irwin, G.W. (eds.) ICIC 2006. LNCS (LNBI), vol. 4115, pp. 145–154. Springer, Heidelberg (2006)
Myerson, R.B.: GameTheory: Analysis of Conflict. Harvard University Press, Cambridge (1991)
Axelrod, R.: The Evolution of Cooperation. Basic Books, Inc., New York (1984)
Weibull, J.W.: Evolutionary Game Theory. MIT Press, Boston (1995)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Di Chio, C., Di Chio, P., Giacobini, M. (2008). An Evolutionary Game-Theoretical Approach to Particle Swarm Optimisation. In: Giacobini, M., et al. Applications of Evolutionary Computing. EvoWorkshops 2008. Lecture Notes in Computer Science, vol 4974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78761-7_63
Download citation
DOI: https://doi.org/10.1007/978-3-540-78761-7_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78760-0
Online ISBN: 978-3-540-78761-7
eBook Packages: Computer ScienceComputer Science (R0)