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Quantum-Inspired Wolf Pack Algorithm to Solve the 0-1 Knapsack Problem

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  • Yangjun Gao
  • Fengming Zhang
  • Yu Zhao
  • Chao Li

Abstract

This paper proposes a Quantum-Inspired wolf pack algorithm (QWPA) based on quantum encoding to enhance the performance of the wolf pack algorithm (WPA) to solve the 0-1 knapsack problems. There are two important operations in QWPA: quantum rotation and quantum collapse. The first step enables the population to move to the global optima and the second step helps to avoid the trapping of individuals into local optima. Ten classical and four high-dimensional knapsack problems are employed to test the proposed algorithm, and the results are compared with other typical algorithms. The statistical results demonstrate the effectiveness and global search capability for knapsack problems, especially for high-level cases.

Suggested Citation

  • Yangjun Gao & Fengming Zhang & Yu Zhao & Chao Li, 2018. "Quantum-Inspired Wolf Pack Algorithm to Solve the 0-1 Knapsack Problem," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-10, June.
    • Handle: RePEc:hin:jnlmpe:5327056
    DOI: 10.1155/2018/5327056
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    Cited by:

    1. Yule Wang & Wanliang Wang, 2021. "Quantum-Inspired Differential Evolution with Grey Wolf Optimizer for 0-1 Knapsack Problem," Mathematics, MDPI, vol. 9(11), pages 1-21, May.

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