Abstract
A novel quantum swarm evolutionary algorithm is presented based on quantum-inspired evolutionary algorithm in this article. The proposed algorithm adopts quantum angle to express Q-bit and improved particle swarm optimization to update automatically. The simulated effectiveness is examined in solving 0-1 knapsack problem.
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Benioff, P.: The Computer as a Physical System: a Microscopic Quantum Mechanical Hamiltonian Model of Computers as Represented by Turing Machines. J. Stat. Phys. 22, 563–591 (1980)
Feynman, R.: Simulating Physics with Computers. Internat. J. Theoret. Phys. 21(6), 467–488 (1982)
Grover, L.K.: Algorithms for Quantum Computation: Discrete Logarithms and Factoring. In: Proceedings of the 35th Annual Symposium on Foundations of Computer Science, Piscataway, NJ, pp. 124–134. IEEE Press, Los Alamitos (1994)
Shor, P.W.: Quantum Computing. Documenta Mathematica, Extra Volume. In: Proceedings of the International Congress of Mathematicians, Berlin, Germany, pp. 467–486 (1998)
Han, K.H., Kim, J.H.: Quantum-inspired Evolutionary Algorithm for a Class of Combinatorial Optimization. IEEE Transactions on Evolutionary Computation 6(6), 580–593 (2002)
Han, K.H., Kim, J.H.: Quantum-Inspired Evolutionary Algorithms with a New Termination Criterion, HεGate, and Two-Phase Scheme. IEEE Transactions on Evolutionary Computation 8(2), 156–169 (2004)
Huang, Y.X., Zhou, C.G., Zou, S.X., Wang, Y.: A Fuzzy Neural Network System Based On the Class Cover and Particle Swarm Optimization. Computer Research and Development (in Chinese) 41(7), 1053–1061 (2004)
Wang, Y., Zhou, C.G., Huang, Y.X., Feng, X.Y.: Training Minimal Uncertainty Neural Networks by Bayesian Theorem and Particle Swarm Optimization. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds.) ICONIP 2004. LNCS, vol. 3316, pp. 579–584. Springer, Heidelberg (2004)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceeding of IEEE International Conference on Neural Networks, Perth, Australia, vol. IV, pp. 1942–1948 (1995)
Heuristic Algorithm Tool Kit: Copyright 2002, Lars Aurdal/Rikshospitalet. Available, http://www.idi.ntnu.no/~lau/Forelesninger/
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Wang, Y., Feng, XY., Huang, YX., Zhou, WG., Liang, YC., Zhou, CG. (2005). A Novel Quantum Swarm Evolutionary Algorithm for Solving 0-1 Knapsack Problem. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_99
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DOI: https://doi.org/10.1007/11539117_99
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
Online ISBN: 978-3-540-31858-3
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