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Bat algorithm with triangle-flipping strategy for numerical optimization

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Abstract

Bat algorithm (BA) is a novel population-based evolutionary algorithm inspired by echolocation behavior. Due to its simple concept, BA has been widely applied to various engineering applications. As an optimization approach, the global search characteristics determine the optimization performance and convergence speed. In BA, the global search capability is dominated by the velocity updating. How to update the velocity of bats may seriously affect the performance of BA. In this paper, we propose a triangle-flipping strategy to update the velocity of bats. Three different triangle-flipping strategies with five different designs are introduced. The optimization performance is verified by CEC2013 benchmarks in those designs against the standard BA. Simulation results show that the hybrid triangle-flipping strategy is superior to other algorithms.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under no. 61663028, Natural Science Foundation of Shanxi Province under Grant no. 201601D011045, and International Science & Technology Cooperation Program of China under Grant no. 2014DFR70280.

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Authors and Affiliations

  1. School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, 030024, Shanxi, China

    Xingjuan Cai, Zhihua Cui & Jianghui Cai

  2. Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang, 330099, China

    Hui Wang

  3. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Yu Xue

  4. Department of Control Science and Engineering, Tongji University, Shanghai, 201804, China

    Lei Wang

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  1. Xingjuan Cai
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  4. Jianghui Cai
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  5. Yu Xue
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Correspondence to Zhihua Cui.

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Cai, X., Wang, H., Cui, Z. et al. Bat algorithm with triangle-flipping strategy for numerical optimization. Int. J. Mach. Learn. & Cyber. 9, 199–215 (2018). https://doi.org/10.1007/s13042-017-0739-8

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