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A Developed Artificial Bee Colony Algorithm Based on Cloud Model

Author

Listed:
  • Ye Jin

    (School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, China)

  • Yuehong Sun

    (School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, China
    Jiangsu Key Laboratory for NSLSCS, Nanjing Normal University, Nanjing 210023, China)

  • Hongjiao Ma

    (School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, China)

Abstract

The Artificial Bee Colony (ABC) algorithm is a bionic intelligent optimization method. The cloud model is a kind of uncertainty conversion model between a qualitative concept T ˜ that is presented by nature language and its quantitative expression, which integrates probability theory and the fuzzy mathematics. A developed ABC algorithm based on cloud model is proposed to enhance accuracy of the basic ABC algorithm and avoid getting trapped into local optima by introducing a new select mechanism, replacing the onlooker bees’ search formula and changing the scout bees’ updating formula. Experiments on CEC15 show that the new algorithm has a faster convergence speed and higher accuracy than the basic ABC and some cloud model based ABC variants.

Suggested Citation

  • Ye Jin & Yuehong Sun & Hongjiao Ma, 2018. "A Developed Artificial Bee Colony Algorithm Based on Cloud Model," Mathematics, MDPI, vol. 6(4), pages 1-18, April.
    • Handle: RePEc:gam:jmathe:v:6:y:2018:i:4:p:61-:d:141727
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    Citations

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    Cited by:

    1. Ramdhan Halid Siregar & Yuwaldi Away & Tarmizi & Akhyar, 2023. "Minimizing Power Losses for Distributed Generation (DG) Placements by Considering Voltage Profiles on Distribution Lines for Different Loads Using Genetic Algorithm Methods," Energies, MDPI, vol. 16(14), pages 1-25, July.
    2. Hanjie Hu & Yu Wu & Jinfa Xu & Qingyun Sun, 2018. "Path Planning for Autonomous Landing of Helicopter on the Aircraft Carrier," Mathematics, MDPI, vol. 6(10), pages 1-20, September.
    3. Shahed Mahmud & Ripon K. Chakrabortty & Alireza Abbasi & Michael J. Ryan, 2022. "Switching strategy-based hybrid evolutionary algorithms for job shop scheduling problems," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1939-1966, October.

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