Abstract
This paper presents a new variant of Particle Swarm Optimization algorithm named QPSO for solving global optimization problems. QPSO is an integrated algorithm making use of a newly defined, multiparent, quadratic crossover operator in the Basic Particle Swarm Optimization (BPSO) algorithm. The comparisons of numerical results show that QPSO outperforms BPSO algorithm in all the twelve cases taken in this study.
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
Angeline P. J.: Evolutionary Optimization versus Particle Swarm Optimization: Philosophy and Performance Difference. The 7th Annual Conference on Evolutionary Programming, San Diego, USA, (1998).
Hu, X., Eberhart, R. C., and Shi, Y.: Swarm Intelligence for Permutation Optimization: A Case Study on n-Queens problem. In Proc. of IEEE Swarm Intelligence Symposium, pp. 243–246 (2003).
Miranda, V., and Fonseca, N.: EPSO — Best-of-two-worlds Meta-heuristic Applied to Power System problems. In Proc. of the IEEE Congress on Evolutionary Computation, Vol. 2, pp. 1080–1085 (2002).
Miranda, V., and Fonseca, N.: EPSO — Evolutionary Particle Swarm Optimization, a New Algorithm with Applications in Power Systems. In Proc. of the Asia Pacific IEEE/PES Transmission and Distribution Conference and Exhibition, Vol. 2, pp. 745–750 (2002).
Ting, T-O., Rao, M. V. C., Loo, C. K., and Ngu, S-S.: A New Class of Operators to Accelerate Particle Swarm Optimization. In Proc. of the IEEE Congresson Evolutionary Computation, Vol. 4, pp. 2406–2410 (2003).
Yao, X., and Liu, Y.: Fast Evolutionary Programming. In L. J. Fogel, P. J. Angeline, and T. B. Back, editors, Proceedings of the Fifth Annual Conference on Evolutionary Programming, MIT Press, pp. 451–460 (1996).
Yao, X., Liu, Y., and Lin, G.: Evolutionary Programming made Faster. IEEE Transactions on Evolutionary Computation, Vol. 3(2), pp. 82–102 (1999).
Angeline, P. J.: Using Selection to Improve Particle Swarm Optimization. In Proc. of the IEEE Congress on Evolutionary Computation, IEEE Press, pp. 84–89 (1998).
Clerc, M.: Think Locally, Act Locally: The Way of Life of Cheap-PSO, an Adaptive PSO. Technical Report, http: // clerc.maurice.free.fr/pso/, (2001).
Kennedy, J.: The Particle Swarm: Social Adaptation of Knowledge. IEEE International Conference on Evolutionary Computation (Indianapolis, Indiana), IEEE Service Center, Piscataway, NJ, pg. 303–308 (1997).
Eberhart, R.C., and Shi, Y.: Particle Swarm Optimization: developments, Applications and Resources. IEEE International Conference on Evolutionary Computation, pg. 81–86 (2001).
Shi, Y. H., and Eberhart, R. C.: A Modified Particle Swarm Optimizer. IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, pg. 69–73 (1998).
Ali, M. M., and Torn, A.: Population Set Based Global Optimization Algorithms: Some Modifications and Numerical Studies. www.ima.umn.edu/preprints/, (2003).
Engelbrecht, A. P.: Fundamentals of Computational Swarm Intelligence. John Wiley & Sons Ltd., (2005).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Pant, M., Thangaraj, R., Abraham, A. (2007). A New PSO Algorithm with Crossover Operator for Global Optimization Problems. In: Corchado, E., Corchado, J.M., Abraham, A. (eds) Innovations in Hybrid Intelligent Systems. Advances in Soft Computing, vol 44. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74972-1_29
Download citation
DOI: https://doi.org/10.1007/978-3-540-74972-1_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74971-4
Online ISBN: 978-3-540-74972-1
eBook Packages: EngineeringEngineering (R0)