Abstract
For many real-world changeable problems over time, the goal of optimization is not only to acquire an optimal solution, but also to track its progression through the search space as closely as possible. In this paper, an improved detection technique at the particle level is designed. Then, a new method of response, learning from the changing global optimum for new environments guided by population diversity, is designed. It defines response condition as well as part of particles to be reset and flying direction after a change. Then, the parabolic benchmark functions with various severities are used to test, compared with the Eberhart-PSO and APSO, and the results show the modified strategies are effective in tracking changes.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proc. of the IEEE International Conf.on Neural Networks, vol. IV, pp. 1942–1948. IEEE Press, Piscataway, ND (1995)
Xiaohui, H., Eberhart, R.C.: Adaptive Particle Swarm Optimization: Detection and Response to Dynamic Systems. In: Proceedings of the IEEE Congress on Evolutionary Computation, Honolulu, Hawaii, USA, pp. 1666–1670. IEEE Computer Society Press, Los Alamitos (2002)
Carlisle, A., Dozier, G.: Adapting Particle Swarm Optimization to Dynamic Environments. In: Proceeding, ICAI, Las Vegas,NV, vol. 1, pp. 429–434 (2000)
Esquivel, S.C., Coello Coello, C.A.: Particle Swarm Optimization in Non-stationary Environments. In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) IBERAMIA 2004. LNCS (LNAI), vol. 3315, pp. 757–766. Springer, Heidelberg (2004)
Zhang, X., Du, Y., Qin, Z., Qin, G., Lu, D.: A Modified Particle Swarm Optimizer for Tracking Dynamic Systems. Advances in Natural Computation, 592–601 (2005)
Carlisle, A., Dozier, G.: Tracking Changing Extrema with Adaptive Particle Swarm Optimizer. In: ISSCI, 2002. World Automation Congress, Orlando, FL, USA (2002)
Blackwell, T., Branke, D.: Multi-swarm Optimization in Dynamic Environments. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 489–500. Springer, Heidelberg (2004)
Cui, C.X., Hardin, T.: Tracking non-stationary Optimal Solution by Particle Swarm Optimizer. In: Proceedings of the Sixth International Conference on Software Engineering
Li, X., Dam, K.H.: Comparing Particle Swarms for Tracking Extrema in Dynamic Environments. Congress on Evolutionary Computation 3, 1772–1779 (2003)
Hu, X., Eberhart, R.C.: Tracking dynamic systems with PSO: where’s the cheese? In: Proceedings of the workshop on Particle Swarm Optimization (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hu, J., Zeng, J., Tan, Y. (2007). A Diversity-Guided Particle Swarm Optimizer for Dynamic Environments. In: Li, K., Fei, M., Irwin, G.W., Ma, S. (eds) Bio-Inspired Computational Intelligence and Applications. LSMS 2007. Lecture Notes in Computer Science, vol 4688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74769-7_27
Download citation
DOI: https://doi.org/10.1007/978-3-540-74769-7_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74768-0
Online ISBN: 978-3-540-74769-7
eBook Packages: Computer ScienceComputer Science (R0)