Abstract
Quantum Particle Swarm Optimization (QPSO) is a global convergence guaranteed search method which introduces the Quantum theory into the basic Particle Swarm Optimization (PSO). QPSO performs better than normal PSO on several benchmark problems. However, QPSO’s quantum bit(Qubit) is still in Hilbert space’s unit circle with only one variable, so the quantum properties have been undermined to a large extent. In this paper, the Bloch Sphere encoding mechanism is adopted into QPSO, which can vividly describe the dynamic behavior of the quantum. In this way, the diversity of the swarm can be increased, and the local minima can be effectively avoided. The proposed algorithm, named Bloch QPSO (BQPSO), is tested with PID controller parameters optimization problem. Experimental results demonstrate that BQPSO has both stronger global search capability and faster convergence speed, and it is feasible and effective in solving some complex optimization problems.
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
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proc. IEEE Conf. On Neural Network, pp. 1942–1948 (1995)
Angeline, P.J.: Evolutionary Optimization Versus Particle Swarm Optimization. In: Philosophy and Performance Differences. Evolutionary Programming VIII. LNCS, vol. 1477, pp. 601–610. Springer, Heidelberg (1998)
Eberhart, R.C., Shi, Y.H.: Comparison between Genetic Algorithm and Particle Swarm Optimization. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 611–616. Springer, Heidelberg (1998)
Krink, T., Vesterstorm, J., Riget, J.: Particle Swarm Optimization with Spatial Particle Extension. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 2, pp. 1474–1479 (2002)
Van den Bergh, F.: An Analysis of Particle Swarm Optimizers. PhD Thesis. University of Pretoria, South Africa (2001)
Li, P.C., Li, S.Y.: Quantum particle swarms algorithm for continuous space optimization. Journal of Quantum Electronics 24(4), 463–468 (2007)
Al-Rabadi, A.N.: New dimensions in non-classical neural computing, part II: quantum, nano, and optical. International Journal of Intelligent Computing and Cybernetics 2(3), 513–573
Xing, Z., Duan, H.: An Improved Quantum Evolutionary Algorithm with 2 crossovers. In: Yu, W., He, H., Zhang, N. (eds.) ISNN 2009. LNCS, vol. 5551, pp. 735–744. Springer, Heidelberg (2009)
Eberhart, R.C., Shi, Y.H.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the International Congress on Evolutionary Computation, Piscataway, pp. 84–88. IEEE Press, Los Alamitos (2000)
Li, P.C., Li, S.Y.: Quantum Computation and Quantum Optimization Algorithm, pp. 113–117. Harbin Institute of Technology Press (2009)
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
Du, Y., Duan, H., Liao, R., Li, X. (2010). Improved Quantum Particle Swarm Optimization by Bloch Sphere. 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_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-13495-1_17
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)