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Improved Quantum Evolutionary Computation Based on Particle SwarmOptimization and Two-Crossovers |
| DUAN Hai-Bin, XING Zhi-Hui |
| School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191 |
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| Cite this article: |
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DUAN Hai-Bin, XING Zhi-Hui 2009 Chin. Phys. Lett. 26 120304 |
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Abstract A quantum evolutionary computation (QEC) algorithm with particle swarm optimization (PSO) and two-crossovers is proposed to overcome identified limitations. PSO is adopted to update the Q-bit automatically, and two-crossovers are applied to improve the convergence quality in the basic QEC model. This hybrid strategy can effectively employ both the ability to jump out of the local minima and the capacity of searching the global optimum. The performance of the proposed approach is compared with basic QEC on the standard unconstrained scalable benchmark problem that numerous hard combinatorial optimization problems can be formulated. The experimental results show that the proposed method outperforms the basic QEC quite significantly.
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| Keywords:
03.67.Ac
12.20.Ds
87.19.Lv
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Received: 25 August 2009
Published: 27 November 2009
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| PACS: |
03.67.Ac
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(Quantum algorithms, protocols, and simulations)
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12.20.Ds
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(Specific calculations)
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87.19.lv
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(Learning and memory)
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