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A Novel Discrete Grey Wolf Optimizer for Solving the Bounded Knapsack Problem

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Computational Intelligence and Intelligent Systems (ISICA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 986))

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Abstract

Grey Wolf Optimizer (GWO) is a recently proposed metaheuristic optimizer inspired by the leadership hierarchy and hunting mechanism of grey wolves. In order to solve the bounded knapsack problem by the GWO, a novel Discrete Grey Wolf Optimizer (DGWO) is proposed in this paper. On the basis of DGWO, the crossover strategy of the genetic algorithm is introduced to enhance its local search ability, and infeasible solutions are processed by a Repair and Optimization method based on the greedy strategy, which could not only ensure the effectiveness but also speed up the convergence. Experiment using three kinds of large-scale instances of the bounded knapsack problem is carried out to verify the validity and stability of the DGWO. By comparing and analyzing the results with other well-established algorithms, computational results show that the convergence speed of the DGWO is faster than that of other algorithms, solutions of these instances of the bounded knapsack problem are all well obtained with approximation ratio bound close to 1.

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Acknowledgments

This work was supported by the Scientific Research Project Program of Colleges and Universities in Hebei Province (ZD2016005), and the Natural Science Foundation of Hebei Province (F2016403055).

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Authors and Affiliations

  1. College of Information and Engineering, Hebei GEO University, Shijiazhuang, 050031, China

    Zewen Li, Yichao He, Huanzhe Li, Ya Li & Xiaohu Guo

Authors
  1. Zewen Li
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  2. Yichao He
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  3. Huanzhe Li
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  4. Ya Li
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  5. Xiaohu Guo
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Corresponding author

Correspondence to Yichao He .

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Editors and Affiliations

  1. School of Information Science and Technology, Jiujiang University, Jiujiang, China

    Hu Peng

  2. School of Information Science and Technology, Jiujiang University, Jiujiang, China

    Changshou Deng

  3. School of Computer, Wuhan University, Wuhan, China

    Zhijian Wu

  4. School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

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Li, Z., He, Y., Li, H., Li, Y., Guo, X. (2019). A Novel Discrete Grey Wolf Optimizer for Solving the Bounded Knapsack Problem. In: Peng, H., Deng, C., Wu, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2018. Communications in Computer and Information Science, vol 986. Springer, Singapore. https://doi.org/10.1007/978-981-13-6473-0_10

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  • DOI: https://doi.org/10.1007/978-981-13-6473-0_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6472-3

  • Online ISBN: 978-981-13-6473-0

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