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Enhanced shuffled frog-leaping algorithm for solving numerical function optimization problems

Author

Listed:
  • Chao Liu

    (Yanshan University)

  • Peifeng Niu

    (Yanshan University
    National Engineering Research Center for Equipment and Technology of Cold Strip Rolling)

  • Guoqiang Li

    (Yanshan University)

  • Yunpeng Ma

    (Yanshan University)

  • Weiping Zhang

    (Qinhuangdao Institute of Technology)

  • Ke Chen

    (Yanshan University)

Abstract

The shuffled frog-leaping algorithm (SFLA) is a relatively new meta-heuristic optimization algorithm that can be applied to a wide range of problems. After analyzing the weakness of traditional SFLA, this paper presents an enhanced shuffled frog-leaping algorithm (MS-SFLA) for solving numerical function optimization problems. As the first extension, a new population initialization scheme based on chaotic opposition-based learning is employed to speed up the global convergence. In addition, to maintain efficiently the balance between exploration and exploitation, an adaptive nonlinear inertia weight is introduced into the SFLA algorithm. Further, a perturbation operator strategy based on Gaussian mutation is designed for local evolutionary, so as to help the best frog to jump out of any possible local optima and/or to refine its accuracy. In order to illustrate the efficiency of the proposed method (MS-SFLA), 23 well-known numerical function optimization problems and 25 benchmark functions of CEC2005 are selected as testing functions. The experimental results show that the enhanced SFLA has a faster convergence speed and better search ability than other relevant methods for almost all functions.

Suggested Citation

  • Chao Liu & Peifeng Niu & Guoqiang Li & Yunpeng Ma & Weiping Zhang & Ke Chen, 2018. "Enhanced shuffled frog-leaping algorithm for solving numerical function optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1133-1153, June.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:5:d:10.1007_s10845-015-1164-z
    DOI: 10.1007/s10845-015-1164-z
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    References listed on IDEAS

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    1. S. Zhang & T. N. Wong, 2018. "Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 585-601, March.
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    Cited by:

    1. Lin Sun & Suisui Chen & Jiucheng Xu & Yun Tian, 2019. "Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation," Complexity, Hindawi, vol. 2019, pages 1-20, February.
    2. Zhao, Ruxin & Wang, Yongli & Liu, Chang & Hu, Peng & Li, Yanchao & Li, Hao & Yuan, Chi, 2020. "Selfish herd optimizer with levy-flight distribution strategy for global optimization problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).

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