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An Improved Quantum-Inspired Evolutionary Algorithm for Knapsack Problems

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Cloud Computing and Security (ICCCS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10603))

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Abstract

As a well-known combinatorial optimization problem, knapsack problems commonly arise in security areas. In this paper, an improved quantum-inspired evolutionary algorithm (PEQIEA) is proposed to solve knapsack problems. In PEQIEA, in each iteration, the state preference of the elite group is used to update the group. The elite group of each iteration consists of a certain number of individuals which are selected by their fitness values. A state preference is proposed to improve the efficiency of the algorithm. A new quantum-inspired gate is obtained by the elite group and their state preference. The Q-gate is then used to make the evolution of the group. The parameters in PEQIEA, which affect the accuracy and efficiency of the algorithm, are discussed empirically. The performance of PEQIEA is then evaluated through extensive experiments.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant Nos. 51577046, 51607004, the State Key Program of National Natural Science Foundation of China under Grant No. 51637004, the national key research and development plan “important scientific instruments and equipment development” Grant No. 2016YFF0102200, Anhui Provincial Natural Science Foundation No. 1608085QF157, and Key projects of Anhui Province university outstanding youth talent support program No. gxyqZD2016207. This work was supported by the China Scholar Council.

Author information

Authors and Affiliations

  1. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, 230009, China

    Sheng Xiang, Yigang He, Liuchen Chang, Kehan Wu & Chaolong Zhang

  2. School of Physics and Electronic Engineering, Anqing Normal University, Anqing, 246011, China

    Chaolong Zhang

  3. Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, E3B 5A3, Canada

    Sheng Xiang & Liuchen Chang

Authors
  1. Sheng Xiang
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  2. Yigang He
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  3. Liuchen Chang
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  4. Kehan Wu
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  5. Chaolong Zhang
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Corresponding authors

Correspondence to Yigang He or Chaolong Zhang .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. National Dong Hwa University, Shoufeng, Taiwan

    Han-Chieh Chao

  3. China Information Technology Security Evaluation Center, Nanjing, China

    Xingang You

  4. Department of Computer Science, Purdue University, West Lafayette, Indiana, USA

    Elisa Bertino

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Xiang, S., He, Y., Chang, L., Wu, K., Zhang, C. (2017). An Improved Quantum-Inspired Evolutionary Algorithm for Knapsack Problems. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10603. Springer, Cham. https://doi.org/10.1007/978-3-319-68542-7_60

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  • DOI: https://doi.org/10.1007/978-3-319-68542-7_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68541-0

  • Online ISBN: 978-3-319-68542-7

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