Abstract
In order to solve discrete optimization problem, this paper proposes a quantum-inspired bacterial swarming optimization (QBSO) algorithm based on bacterial foraging optimization (BFO). The proposed QBSO algorithm applies the quantum computing theory to bacterial foraging optimization, and thus has the advantages of both quantum computing theory and bacterial foraging optimization. Also, we use the swarming pattern of birds in block introduced in particle swarm optimization (PSO). Then we evaluate the efficiency of the proposed QBSO algorithm through four classical benchmark functions. Simulation results show that the designed algorithm is superior to some previous intelligence algorithms in both convergence rate and convergence accuracy.
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References
Kennedy, J., Eberhart, J.: Discrete binary version of the particle swarm optimization. In: Proc. IEEE International Conference on Computational Cybernetics and Simulation, pp. 4104–4108 (1997)
Zhao, Y., Zheng, J.L.: Multiuser detection using the particle swarm optimization algorithm in DS-CDMA communication systems. J. Tsinghua. Univ (Sci. & Tech.) 44, 840–842 (2004)
Han, K.H., Kim, J.H.: Genetic quantum algorithm and its application to combinatorial optimization problems. In: Proceedings of the 2000 IEEE Conference on Evolutionary Computation, pp. 1354–1360. IEEE Press, Piscataway (2000)
Li, B., Zhuang, Z.-Q.: Genetic Algorithm Based-On the Quantum Probability Representation. In: Yin, H., Allinson, N.M., Freeman, R., Keane, J.A., Hubbard, S. (eds.) IDEAL 2002. LNCS, vol. 2412, pp. 500–505. Springer, Heidelberg (2002)
Gao, H.Y., Diao, M.: Quantum particle swarm optimization for MC-CDMA multiuser detection. In: 2009 International Conference on Artificial Intelligence and Computational Intelligence, vol. 2, pp. 132–136 (2009)
Mishra, S.: A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation. IEEE Transaction on Evolutionary Computation 9, 61–73 (2005)
Gao, H.Y., Cao, J.L., Diao, M.: A simple quantum-inspired particle swarm optimization and its application. Information Technology Journal 10(12), 2315–2321 (2011)
Zhao, Z.J., Peng, Z., Zheng, S.L., Shang, J.N.: Cognitive radio spectrum allocation using evolutionary algorithms. IEEE Transactions on Wireless Communications 8(9), 4421–4425 (2009)
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Cao, J., Gao, H. (2012). A Quantum-inspired Bacterial Swarming Optimization Algorithm for Discrete Optimization Problems. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30976-2_4
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DOI: https://doi.org/10.1007/978-3-642-30976-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30975-5
Online ISBN: 978-3-642-30976-2
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