OJAppS  Vol.5 No.6 , June 2015
Improved Quantum-Behaved Particle Swarm Optimization
Author(s) Jianping Li
ABSTRACT
To enhance the performance of quantum-behaved PSO, some improvements are proposed. First, an encoding method based on the Bloch sphere is presented. In this method, each particle carries three groups of Bloch coordinates of qubits, and these coordinates are actually the approximate solutions. The particles are updated by rotating qubits about an axis on the Bloch sphere, which can simultaneously adjust two parameters of qubits, and can automatically achieve the best matching of two adjustments. The optimization process is employed in the n-dimensional space [-1, 1]n, so this approach fits to many optimization problems. The experimental results show that this algorithm is superior to the original quantum-behaved PSO.

Cite this paper
Li, J. (2015) Improved Quantum-Behaved Particle Swarm Optimization. Open Journal of Applied Sciences, 5, 240-250. doi: 10.4236/ojapps.2015.56025.
References
[1]   Kennedy, J. and Eberhart, R.C. (1995) Particle Swarms Optimization. Proceedings of IEEE International Conference on Neural Networks, 4, 1942-1948.
http://dx.doi.org/10.1109/icnn.1995.488968

[2]   Guo, W.Z., Chen, G.L. and Peng, S.J. (2011) Hybrid Particle Swarm Optimization Algorithm for VLSI Circuit Partitioning. Journal of Software, 22, 833-842.
http://dx.doi.org/10.3724/SP.J.1001.2011.03980

[3]   Hamid, M., Saeed, J., Seyed, M., et al. (2013) Dynamic Clustering Using Combinatorial Particle Swarm Optimization. Applied Intelligence, 38, 289-314.
http://dx.doi.org/10.1007/s10489-012-0373-9

[4]   Lin, S.W., Ying, K.C. and Chen, S.C. (2008) Particle Swarm Optimization for Parameter Determination and Feature Slection of Support Vector Machines. Expert Systems with Applications, 35, 1817-1824. http://dx.doi.org/10.1016/j.eswa.2007.08.088

[5]   Yamina, M. and Ben, A. (2012) Psychological Model of Particle Swarm Optimization Based Multiple Emotions. Applied Intelligence, 36, 649-663.
http://dx.doi.org/10.1007/s10489-011-0282-3

[6]   Cai, X.J., Cui, Z.H. and Zeng, J.C. (2008) Dispersed Particle Swarm Optimization. Information Processing Letters, 105, 231-235. http://dx.doi.org/10.1016/j.ipl.2007.09.001

[7]   Bergh, F. and Engelbrecht, A.P. (2005) A Study of Particle Swarm Optimization Particle Trajectories. Information Science, 176, 937-971.

[8]   Chatterjee, A. and Siarry, P. (2007) Nonlinear Inertia Weight Variation for Dynamic Adaptation in Particle Swarm Optimization. Computers & Operations Research, 33, 859-871.
http://dx.doi.org/10.1016/j.cor.2004.08.012

[9]   Lu, Z.S. and Hou, Z.R. (2004) Particle Swarm Optimization with Adaptive Mutation. Acta Electronica Sinica, 32, 416-420.

[10]   Liu, Y., Qin, Z. and Shi, Z.W. (2007) Center Particle Swarm Optimization. Neurocomputing, 70, 672-679. http://dx.doi.org/10.1016/j.neucom.2006.10.002

[11]   Liu, B., Wang, L. and Jin, Y.H. (2005) Improved Particle Swarm Optimization Combined with Chaos. Chaos Solitons & Fractals, 25, 1261-1271.
http://dx.doi.org/10.1016/j.chaos.2004.11.095

[12]   Luo, Q. and Yi, D.Y. (2008) A Co-Evolving Framework for Robust Particle Swarm Optimization. Applied Mathematics and Computation, 199, 611-622.
http://dx.doi.org/10.1016/j.amc.2007.10.017

[13]   Zhang, Y.J. and Shao, S.F. (2011) Cloud Mutation Particle Swarm Optimization Algorithm Based on Cloud Model. Pattern Recognition & Artificial Intelligence, 24, 90-95.

[14]   Zhu, H.M. and Wu, Y.P. (2010) A PSO Algorithm with High Speed Convergence. Control and Decision, 25, 20-24.

[15]   Wang, K. and Zheng, Y.J. (2012) A New Particle Swarm Optimization Algorithm for Fuzzy Optimization of Armored Vehicle Scheme Design. Applied Intelligence, 37, 520-526.
http://dx.doi.org/10.1007/s10489-012-0345-0

[16]   Salman, A.K. and Andries, P.E. (2012) A Fuzzy Particle Swarm Optimization Algorithm for Computer Communication Network Topology Design. Applied Intelligence, 36, 161-177.
http://dx.doi.org/10.1007/s10489-010-0251-2

[17]   Mohammad, S.N., Mohammad, R.A. and Maziar, P. (2012) LADPSO: Using Fuzzy Logic to Conduct PSO Algorithm. Applied Intelligence, 37, 290-304.
http://dx.doi.org/10.1007/s10489-011-0328-6

[18]   Zheng, Y.J. and Chen, S.Y. (2013) Cooperative Particle Swarm Optimization for Multi-Objective Transportation Planning. Applied Intelligence, 39, 202-216.
http://dx.doi.org/10.1007/s10489-012-0405-5

[19]   Jose, G.N. and Enrique, A. (2012) Parallel Multi-Swarm Optimizer for Gene Selection in DNA Microarrays. Applied Intelligence, 37, 255-266.
http://dx.doi.org/10.1007/s10489-011-0325-9

[20]   Sun, J., Feng, B. and Xu, W.B. (2004) Particle Swam Optimization with Particles Having Quantum Behavior. Proceedings of IEEE Conference on Evolutionary Computation, 1, 325-331.

[21]   Sun, J., Feng, B. and Xu, W.B. (2004) A Global Search Strategy of Quantum-Behaved Particle Swarm Optimization. Proceedings of IEEE Conference on Cybernetics and Intelligent Systems, 1, 111-116.

[22]   Sun, J., Xu, W.B. and Feng, B. (2005) Adaptive Parameter Control for Quantum-Behaved Particle Swarm Optimization on Individual Level. Proceedings of IEEE Conference on Cybernetics and Intelligent Systems, 4, 3049-3054.

[23]   Said, M.M. and Ahmed, A.K. (2005) Investigation of the Quantum Particle Swarm Optimization Technique for Electromagnetic Applications. Proceedings of IEEE Antennas and Propagation Society International Symposium, Washington DC, 3-8 July 2005, 45-48.

[24]   Sun, J., Xu, W.B. and Fang, W. (2006) Quantum-Behaved Particle Swarm Optimization Algorithm with Controlled Di- versity. Proceedings of International Conference on Computational Science, University of Reading, 28-31 May 2006, 847-854.

[25]   Xia, M.L., Sun, J. and Xu, W.B. (2008) An Improved Quantum-Behaved Particle Swarm Optimization Algorithm with Weighted Mean Best Position. Applied Mathematics and Computation, 205, 751-759. http://dx.doi.org/10.1016/j.amc.2008.05.135

[26]   Fang, W., Sun, J., Xie, Z.P. and Xu, W.B. (2010) Convergence Analysis of Quantum-Behaved Particle Swarm Optimi- zation Algorithm and Study on Its Control Parameter. Acta Physica Sinica, 59, 3686-3693.

[27]   Said, M.M. and Ahmed, A.K. (2006) Quantum Particle Swarm Optimization for Electromagnetic. IEEE Transactions on Antennas and Propagation, 54, 2765-2775.

[28]   Gao, W.F., Liu, S.Y. and Huang, L.L. (2012) A Global Best Artificial Bee Colony Algorithm for Global Optimization. Journal of Computational and Applied Mathematics, 236, 2741-2753.
http://dx.doi.org/10.1016/j.cam.2012.01.013

[29]   Adam, P.P., Jaroslaw, J. and Napiorkowski, A.K. (2012) Differential Evolution Algorithm with Separated Groups for Multi-Dimensional Optimization Problems. European Journal of Operational Research, 216, 33-46. http://dx.doi.org/10.1016/j.ejor.2011.07.038

[30]   Liu, G., Li, Y.X., Nie, X. and Zheng, H. (2012) A Novel Clustering-Based Differential Evolution with 2 Multi-Parent Crossovers for Global Optimization. Applied Soft Computing, 12, 663-681.
http://dx.doi.org/10.1016/j.asoc.2011.09.020

[31]   Suganthan, P.N., Hansen, N. and Liang, J.J. (2005) Problem Definitions and Evaluation Criteria for the CEC2005 Special Session on Real Parameter Optimization.
http://www.ntu.edu.sg/home/EPNSugan

[32]   Noman, N. and Iba, H. (2008) Accelerating Differential Evolution Using an Adaptive Local Search. IEEE Transactions on Evolutionary Computation, 12, 107-125.
http://dx.doi.org/10.1109/TEVC.2007.895272

 
 
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