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Hybrid Particle Swarm Optimization with Bat Algorithm

  • Conference paper
Genetic and Evolutionary Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 329))

Abstract

In this paper, a communication strategy for hybrid Particle Swarm Optimization (PSO) with Bat Algorithm (BA) is proposed for solving numerical optimization problems. In this work, several worst individuals of particles in PSO will be replaced with the best individuals in BA after running some fixed iterations, and on the contrary, the poorer individuals of BA will be replaced with the finest particles of PSO. The communicating strategy provides the information flow for the particles in PSO to communicate with the bats in BA. Six benchmark functions are used to test the behavior of the convergence, the accuracy, and the speed of the approached method. The results show that the proposed scheme increases the convergence and accuracy more than BA and PSO up to 3% and 47% respectively.

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Author information

Authors and Affiliations

  1. Department of Electronics Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan

    Tien-Szu Pan & Thi-Kien Dao

  2. Faculty of Information Technology, Haiphong Private University, Hai Phong, Vietnam

    Trong-The Nguyen

  3. School of Computer Science, Engineering and Mathematics, Flinders University, Bedford Park, Australia

    Shu-Chuan Chu

Authors
  1. Tien-Szu Pan
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  2. Thi-Kien Dao
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  3. Trong-The Nguyen
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  4. Shu-Chuan Chu
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Editor information

Editors and Affiliations

  1. HIGH-TECH Industry Development Zone, Nanchang Institute of Technology, Jiangxi Province, China

    Hui Sun

  2. Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, Magong City, Taiwan

    Chin-Yu Yang

  3. Innovative Information Industry Research Center, Shenzhen University Harbin Institute of Technology, Shenzhen, China

    Chun-Wei Lin

  4. Harbin Institute of Technology Innovative Info. Industry Res. Center, Shenzhen University, Shenzhen, China

    Jeng-Shyang Pan

  5. Department of Computer Science Faculty of Elec. Eng. & Comp. Sci., VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Vaclav Snasel

  6. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA

    Ajith Abraham

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© 2015 Springer International Publishing Switzerland

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Pan, TS., Dao, TK., Nguyen, TT., Chu, SC. (2015). Hybrid Particle Swarm Optimization with Bat Algorithm. In: Sun, H., Yang, CY., Lin, CW., Pan, JS., Snasel, V., Abraham, A. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 329. Springer, Cham. https://doi.org/10.1007/978-3-319-12286-1_5

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  • DOI: https://doi.org/10.1007/978-3-319-12286-1_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12285-4

  • Online ISBN: 978-3-319-12286-1

  • eBook Packages: EngineeringEngineering (R0)

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