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An Improved Artificial Bee Colony Algorithm Based on Particle Swarm Optimization and Differential Evolution

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Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9226))

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Abstract

In order to improve the problem that Artificial Bee Colony (ABC) is good at exploring but lack of exploitation, two new solution search strategies named PSO-DE-PABC and PSO-DE-GABC are proposed based on Particle Swarm Optimization (PSO) and Differential Evolution (DE). PSO-DE-PABC generates new candidate position around the random particle to improve divergence. PSO-DE-GABC generates new candidate position around the global best solution to accelerate the convergence, and differential vectors are also used to increase the divergence. Besides, Dimension Factor (DF) is introduced to control the search rate of the algorithms. A new scout strategy considering current swarm state is used to replace the original random scout strategy to enhance the local search ability. Comparison with basic ABC, GABC (Gbest-guided ABC) and ABC/best algorithm is given on 10 groups of standard benchmark function. The results show that PSO-DE-GABC and PSO-DE-PABC have better convergence rate and accuracy.

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Acknowledgement

The work in this paper is in part supported by Wuhan University of Science and Technology city college scientific research project under Grant No. 2014cyybky011.

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Authors and Affiliations

  1. Faculty of Information Engineering, Wuhan University of Science and Technology City College, Wuhan, 430083, China

    Fengli Zhou & Yanxia Yang

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  1. Fengli Zhou
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  2. Yanxia Yang
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Correspondence to Fengli Zhou .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

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Zhou, F., Yang, Y. (2015). An Improved Artificial Bee Colony Algorithm Based on Particle Swarm Optimization and Differential Evolution. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_3

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  • DOI: https://doi.org/10.1007/978-3-319-22186-1_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22185-4

  • Online ISBN: 978-3-319-22186-1

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