Abstract
For improving the search performance of a canonical particle swarm optimizer (CPSO), we propose a newly canonical particle swarm optimizer with diversive curiosity (CPSO/DC). A crucial idea here is to introduce diversive curiosity into the CPSO to comprehensively manage the trade-off between exploitation and exploration for alleviating stagnation. To demonstrate the effectiveness of the proposed method, computer experiments on a suite of five-dimensional benchmark problems are carried out. We investigate the characteristics of the CPSO/DC, and compare the search performance with other methods. The obtained results indicate that the search performance of the CPSO/DC is superior to that by EPSO, ECPSO and RGA/E, but is inferior to that by PSO/DC for the Griewank and Rastrigin 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
Berlyne, D.: Conflict, Arousal, and Curiosity. McGraw-Hill Book Co., New York (1960)
Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2000)
Day, H.: Curiosity and the Interested Explorer. Performance and Instruction 21(4), 19–22 (1982)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43 (1995)
El-Abd, M., Kamel, M.S.: A Taxonomy of Cooperative Particle Swarm Optimizers. International Journal of Computational Intelligence Research 4(2), 137–144 (2008)
Juang, C.-F.: A Hybrid of Genetic Algorithm and Particle Swarm Optimization for Recurrent Network Design. IEEE Transactions on Systems, Man and Cybernetics Part B 34(2), 997–1006 (2004)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, Piscataway, New Jersey, USA, pp. 1942–1948 (1995)
Kennedy, J.: In Search of the Essential Particle Swarm. In: Proceedings of the 2006 IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, pp. 6158–6165 (2006)
Lane, J., Engelbrecht, A., Gain, J.: Particle Swarm Optimization with Spatially Meaningful Neighbours. In: Proceedings of Swarm Intelligence Symposium (SIS 2008), St. Louis, MO, USA, pp. 1–8 (2008)
Loewenstein, G.: The Psychology of Curiosity: A Review and Reinterpretation. Psychological Bulletin 116(1), 75–98 (1994)
Poli, R.: Analysis of the Publications on the Applications of Particle Swarm Optimisation. Journal of Artificial Evolution and Applications 2008(1), 1–10 (2008)
Reyes-Sierra, M., Coello, C.A.C.: Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art. International Journal of Computational Intelligence Research 2(3), 287–308 (2006)
Wohlwill, J.F.: A Conceptual Analysis of Exploratory Behavior in Advances in Intrinsic Motivation and Aesthetics. Plenum Press, New York (1981)
Zhang, H., Ishikawa, M.: Characterization of particle swarm optimization with diversive curiosity. Journal of Neural Computing & Applications, 409–415 (2009)
Zhang, H., Ishikawa, M.: Particle Swarm Optimization with Diversive Curiosity and Its Identification. In: Ao, S., et al. (eds.) Trends in Communication Technologies and Engineering Science. Lecture Notes in Electrical Engineering, vol. 33, pp. 335–349. Springer, Netherlands (2009)
Zhang, H., Ishikawa, M.: The performance verification of an evolutionary canonical particle swarm optimizer. Neural Networks 23(4), 510–516 (2010)
http://www.ntu.edu.sg/home/epnsugan/index_files/CEC-05/Tech-Report-May-30-05.pdf
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, H., Zhang, J. (2010). The Performance Measurement of a Canonical Particle Swarm Optimizer with Diversive Curiosity. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13495-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-13495-1_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13494-4
Online ISBN: 978-3-642-13495-1
eBook Packages: Computer ScienceComputer Science (R0)