Abstract
In this paper a previous successful research on chaos enhanced particle swarm optimization algorithm (PSO) is expanded. The possibility of adaptive change of control parameters of chaotic systems that is used as a pseudo-random number generator for the velocity calculation in PSO algorithm is investigated. To evaluate the performance of newly designed algorithm the CEC´ 13 benchmark set was used.
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.
Similar content being viewed by others
References
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufmann Publishers (2001)
Nickabadi, A., Ebadzadeh, M.M., Safabakhsh, R.: A novel particle swarm optimization algorithm with adaptive inertia weight. Applied Soft Computing 11(4), 3658–3670 (2011)
Yuhui, S., Eberhart, R.: A modified particle swarm optimizer. In: IEEE World Congress on Computational Intelligence, May 4-9, pp. 69–73 (1998)
Caponetto, R., Fortuna, L., Fazzino, S., Xibilia, M.G.: Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Transactions on Evolutionary Computation 7(3), 289–304 (2003)
Araujo, E., Coelho, L.: Particle swarm approaches using Lozi map chaotic sequences to fuzzy modelling of an experimental thermal-vacuum system. Applied Soft Computing 8(4), 1354–1364 (2008)
Alatas, B., Akin, E., Ozer, B.A.: Chaos embedded particle swarm optimization algorithms. Chaos, Solitons & Fractals 40(4), 1715–1734 (2009) ISSN 0960-0779
Pluhacek, M., Senkerik, R., Davendra, D., Kominkova Oplatkova, Z., Zelinka, I.: On the behavior and performance of chaos driven PSO algorithm with inertia weight. Computers & Mathematics with Applications 66, 122–134 (2013)
Pluhacek, M., Senkerik, R., Zelinka, I.: Particle swarm optimization algorithm driven by multichaotic number generator. Soft Comput. 18(4), 631–639 (2014), doi:10.1007/s00500-014-1222-z
Pluhacek, M., Senkerik, R., Davendra, D., Zelinka, I., Designing, P.I.D., Controller For, D.C.: Designing PID Controller For DC Motor System By Means of Enhanced PSO Algorithm with Discrete Chaotic Lozi Map. In: Proceedings of the 26th European Conference on Modelling and Simulation, ECMS 2012, pp. 405–409 (2012) ISBN 978-0-9564944-4-3
Sprott, J.C.: Chaos and Time-Series Analysis. Oxford University Press (2003)
Liang, J.J., Qu, B.-Y., Suganthan, P.N., Hernández-Díaz Alfredo, G.: Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session and Competition on Real-Parameter Optimization. Technical Report 201212, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore (January 2013)
Zelinka, I., Senkerik, R., Pluhacek, M.: Do evolutionary algorithms indeed require randomness? In: 2013 IEEE Congress on Evolutionary Computation (CEC), June 20-23, pp. 2283–2289 (2013), doi:10.1109/CEC.2 013.6557841
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Pluhacek, M., Senkerik, R., Zelinka, I., Davendra, D. (2014). Tuning the Lozi Map in Chaos Driven PSO Inspired by the Multi-chaotic Approach. In: Zelinka, I., Suganthan, P., Chen, G., Snasel, V., Abraham, A., Rössler, O. (eds) Nostradamus 2014: Prediction, Modeling and Analysis of Complex Systems. Advances in Intelligent Systems and Computing, vol 289. Springer, Cham. https://doi.org/10.1007/978-3-319-07401-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-07401-6_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07400-9
Online ISBN: 978-3-319-07401-6
eBook Packages: EngineeringEngineering (R0)