Abstract
The paper develops a self-organization particle swarm optimization (SOPSO) with the aim to alleviate the premature convergence. SOPSO emphasizes the information interactions between the particle-lever and the swarm-lever, and introduce feedback to simulate the function. Through the feedback information, the particles can perceive the swarm-lever state and adopt favorable behavior model to modify their behavior, which not only can modify the exploitation and the exploration of the algorithm adaptively, but also can vary the diversity of the swarm and contribute to a global optimum output in the swarm. Relative experiments have been done; the results show SOPSO performs very well on benchmark problems, and outperforms the basic PSO in 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. In: Proc. IEEE Conference on Neural Networks, vol. 11, pp. 1942–1948. IEEE Service Center, Perth, Australia (1995)
Bergh, F.V.D., Engelbrecht, A.: Particle swarm weight initialization in multi-layer perception artificial neural networks. In: Development and Practice of Artificial Intelligence Techniques, Durban, South Africa, pp. 41–45 (1999)
Bergh, F.V.D., Engelbrecht, A.P.: Cooperative Learning in Neural Networks using Particle Swarm Optimizers. South African Computer Journal 26(11), 84–90 (2000)
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 (2002)
Fukuyama, Y., Yoshida, H.: A Particle Swarm Optimization for Reactive Power and Voltage Control in Electric Power Systems. In: Proc. Congress on Evolutionary Computation, Seoul, Korea, pp. 87–93. IEEE Service Center, Piscataway (2001)
Zeng, J.C., Jie, J., Cui, Z.H.: Particle Swarm Optimization. Science Press, Beijing (2004)
Suganthan, P.N.: Particle Swarm Optimizer with Neighborhood Operator. In: Proc. Congress on Evolutionary Computation, Washington D.C, USA, July, pp. 1958–1961. IEEE Service Center, Piscataway (1999)
Li, X.D.: Adaptively Choosing Neighborhood Using Species in a Particle Swarm Optimizer for Multimodal Function Optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 105–116 (2004)
Xie, X.F., Z, W.J., B, D.C.: Optimizing Semiconductor Devices by Self-organizing Particle Swarm, Congress on Evolutionary Computaion, Oregon,USA, pp. 2017–2022 (2004)
Jacques, R., Jakob, S.V.: A Diversity-Guided Particle Swarm Optimizer –the ARPSO, http://citeseer.nj.nec.com/riget02diversityguided.html
Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proc. Conference on Evolutionary Computation, pp. 69–73. IEEE Press, Piscataway (1998)
Kennedy, J.: Small Worlds and Mega-Minds: Effects of Neighborhood Topology on Particle Swarm Performance. In: Proceedings of the Congress of Evolutionary Computation, vol. 3, pp. 1931–1938. IEEE Press, Los Alamitos (1938)
Shi, Y., Eberhart, R.C.: Empirical study of particle swarm optimization. In: Proc. Congress on Evolutionary Computation, pp. 1945–1950. IEEE Service Center, Piscataway (1999)
Shi, Y., Eberhart, R.C.: Fuzzy Adaptive Particle Swarm Optimization. In: Proc. Congress on Evolutionary Computation, pp. 101–106. IEEE service Center, Seoul, Korea (2001)
Ursem, R.K.: Diversity-Guided Evolutionary Algorithms. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 462–474. Springer, Heidelberg (2002)
Iwasaki, N., Yasuda, K.: Adaptive Particle Swarm Optimization via Velocity Feedback. In: The 36th ISCIE International symposium on Stochastic Systems Theory and Its Applications, B7-5, pp. 116–117 (2004)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence From Natural to Artificial Systems, pp. 1–22. Oxford University Press Inc., Oxford (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jie, J., Zeng, J., Han, C. (2006). Self-Organization Particle Swarm Optimization Based on Information Feedback. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_120
Download citation
DOI: https://doi.org/10.1007/11881070_120
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
Online ISBN: 978-3-540-45902-6
eBook Packages: Computer ScienceComputer Science (R0)