Abstract
This paper is the first attempt to introduce a new concept of the birth and death of particles via time variant particle population size to improve the adaptation of Particle Swarm Optimization (PSO). Here a dynamic particle population based PSO algorithm (DPPSO) is proposed based on a time-variant particle population function which contains the attenuation item and undulate item. The attenuation item makes the population decrease gradually in order to reduce the computational cost because the particles have the tendency of convergence as time passes. The undulate item consists of periodical phases of ascending and descending. In the ascending phase, new particles are randomly produced to avoid the particle swarm being trapped in the local optimal point, while in the descending phase, particles with lower ability gradually die so that the optimization efficiency is improved. The test on four benchmark functions shows that the proposed algorithm effectively reduces the computational cost and greatly improves the global search ability.
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.: Particle swarm optimization [J]. Conf. Neural Networks 11, 1942–1948 (1995)
Shi, Y., Eberhart, R.: Empirical study of particle swarm optimization. In: International Conference on Evolutionary Computation [A], Washington, pp. 1945–1950 (1999)
Ran, H., Yong-Ji, W., Qing, W., Jin-Hui, Z., Chen-Yong, H.: An Improved Particle Swarm Optimization Based on Self-Adaptive Escape Velocity. Journal of Software 16(12) (2005)
Ratnaweera, A., Halgamuge, S.K., Watson, H.C.: Self-Organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients [J]. Evolutionary Computation 8(3), 240–255 (2004)
Fan, H.Y.: A modification to particle swarm optimization algorithm [J]. Engineering Computation 19(8), 970–989 (2002)
van den Bergh, F., Engelbrecht, A.P.: A Cooperative Approach to Particle Swarm Optimization [J]. IEEE Transactions on Evolutionary 8(3) (2004)
Kennedy, J., Mendes, R.: Neighborhood Topologies in Full-Informed and Best-of-Neighborhood Particle Swarm [J]. In: Soft Computing in Industrial Application. Proceedings of the 2003 IEEE International Workshop (2003)
El-Gallad, A., El-Hawary, M., Sallam, A., Kalas, A.: Enhancing the Particle Swarm Optimizer Via Proper Parameters Selection [A]. In: Proceedings of the 2002 IEEE Canadian Conference on Electrical & Computer Engineering (2002)
Hu, X.H., Eberhart, R.C.: Adaptive particle swarm optimization: Detection and response to dynamic system [A]. In: Proc. Congress on Evolutionary Computation, pp. 1666–1670 (2002)
Clerc, M., Kennedy, J.: The particle swarm——Explosion, stability, and convergence in a multidimensional complex space[J]. IEEE Trans. on Evolutionary Computation 6(1), 58–73 (2002)
Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization [A]. In: Proc. 2000 Congress Evolutionary Computation [C], pp. 84–88. IEEE Press, Los Alamitos (2000)
Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization [J]. Lecture Notes in Computer Science—Evolutionary Programming VII 14(47), 591–600 (1998)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sun, S., Ye, G., Liang, Y., Liu, Y., Pan, Q. (2007). Dynamic Population Size Based Particle Swarm Optimization. In: Kang, L., Liu, Y., Zeng, S. (eds) Advances in Computation and Intelligence. ISICA 2007. Lecture Notes in Computer Science, vol 4683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74581-5_42
Download citation
DOI: https://doi.org/10.1007/978-3-540-74581-5_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74580-8
Online ISBN: 978-3-540-74581-5
eBook Packages: Computer ScienceComputer Science (R0)