Abstract
We previously proposed multiple particle swarm optimizers with diversive curiosity (MPSOα/DC). Its main features are to introduce diversive curiosity and localized random search into MPSO to comprehensively manage the trade-off between exploitation and exploration for preventing stagnation and improving the search efficiency. In this paper, we further extend these features to multiple particle swarm optimizers with inertia weight and multiple canonical particle swarm optimizers to create two analogues, called MPSOIWα/DC and MCPSOα/DC. To demonstrate the effectiveness of these proposals, computer experiments on a suite of multidimensional benchmark problems are carried out. The obtained results show that the search performance of the MPSOα/DC is superior to that of both the MPSOIWα/DC and MCPSOα/DC, and they have better search efficiency compared to other methods such as the convenient cooperative PSO and a real-coded genetic algorithm.
Keywords
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
van den Bergh, F., Engelbrecht, A.P.: A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation 8(3), 225–239 (2004)
Berlyne, D.: Conflict, Arousal, and Curiosity. McGraw-Hill Book Co., New York (1960)
Chang, J.F., Chu, S.C., Roddick, J.F., Pan, J.S.: A parallel particle swarm optimization algorithm with communication strategies. Journal of Information Science and Engineering 21, 809–818 (2005)
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)
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, Perth, Australia, pp. 1942–1948 (1995)
Loewenstein, G.: The Psychology of Curiosity: A Review and Reinterpretation. Psychological Bulletin 116(1), 75–98 (1994)
Meissner, M., Schmuker, M., Schneider, G.: Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training. BMC Bioinformatics 7(125) (2006)
Niu, B., Zhu, Y., He, X.: Multi-population Cooperation Particle Swarm Optimization. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds.) ECAL 2005. LNCS (LNAI), vol. 3630, pp. 874–883. Springer, Heidelberg (2005)
Shi, Y., Eberhart, R.C.: A modified particle swarm optimiser. In: Proceedings of the IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, USA, pp. 69–73 (1998)
Zhang, H., Ishikawa, M.: Evolutionary Particle Swarm Optimization – Metaoptimization Method with GA for Estimating Optimal PSO Methods. In: Castillo, O., et al. (eds.) Trends in Intelligent Systems and Computer Engineering. LNEE, vol. 6, pp. 75–90. Springer, Heidelberg (2008)
Zhang, H., Ishikawa, M.: Characterization of particle swarm optimization with diversive curiosity. Journal of Neural Computing & Applications, 409–415 (2009)
Zhang, H., Ishikawa, M.: The performance verification of an evolutionary canonical particle swarm optimizers. Neural Networks 23(4), 510–516 (2010)
Zhang, H.: Multiple Particle Swarm Optimizers with Diversive Curiosity. In: Proceedings of the International MultiConference of Engineers and Computer Scientists 2010 (IMECS 2010), Hong Kong, pp. 174–179 (2010)
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. (2010). A New Expansion of Cooperative Particle Swarm Optimization. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_72
Download citation
DOI: https://doi.org/10.1007/978-3-642-17537-4_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17536-7
Online ISBN: 978-3-642-17537-4
eBook Packages: Computer ScienceComputer Science (R0)