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A Complex-valued Encoding Bat Algorithm for Solving 0–1 Knapsack Problem

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Abstract

This paper proposes a novel complex-valued encoding bat algorithm (CPBA) for solving 0–1 knapsack problem. The complex-valued encoding method which can be considered as an efficient global optimization strategy is introduced to the bat algorithm. Based on the two-dimensional properties of the complex number, the real and imaginary parts of complex number are updated separately. The proposed algorithm can effectively diversify bat population and improving the convergence performance. The CPBA enhances exploration ability and is effective for solving both small-scale and large-scale 0–1 knapsack problem. Finally, numerical simulation is carried out, and the comparison results with some existing algorithms demonstrate the validity and stability of the proposed algorithm.

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Acknowledgments

This work is supported by National Science Foundation of China under Grant No. 61165015; 61463007. Key Project of Guangxi Science Foundation under Grant No. 2012GXNSFDA053028, and the Innovation Project of Guangxi Graduate EducationunderGrantNo.gxun-chx2014089.

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Authors and Affiliations

  1. College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, 530006, China

    Yongquan Zhou, Liangliang Li & Mingzhi Ma

  2. Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Nanning, 530006, China

    Yongquan Zhou

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  1. Yongquan Zhou
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  2. Liangliang Li
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  3. Mingzhi Ma
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Correspondence to Yongquan Zhou.

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Zhou, Y., Li, L. & Ma, M. A Complex-valued Encoding Bat Algorithm for Solving 0–1 Knapsack Problem. Neural Process Lett 44, 407–430 (2016). https://doi.org/10.1007/s11063-015-9465-y

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  • DOI: https://doi.org/10.1007/s11063-015-9465-y

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