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Parallel chaotic local search enhanced harmony search algorithm for engineering design optimization

Author

Listed:
  • Jin Yi

    (Huazhong University of Science and Technology)

  • Xinyu Li

    (Huazhong University of Science and Technology)

  • Chih-Hsing Chu

    (National Tsing-Hua University)

  • Liang Gao

    (Huazhong University of Science and Technology)

Abstract

In this paper, we present a parallel chaotic local search enhanced harmony search algorithm (MHS–PCLS) for solving engineering design optimization problems. The concept of chaos has been previously successfully applied in metaheuristics. However, chaos sequences are sensitive to their initial conditions and cause unstable performance in algorithms. The proposed parallel chaotic local search method searches from several different initial points and diminishes the sensitivity of the initial condition, thereby increasing the robustness of the harmony search method. Numerical benchmark problems are tested to validate the effectiveness of MHS–PCLS. The simulation results confirm that MHS–PCLS obtains superior results for mathematical examples compared to other harmony search variants. Several well-known constrained engineering design problems are also tested using the new approach. The computational results demonstrate that the proposed MHS–PCLS algorithm requires a smaller number of function evaluations and in the majority of cases delivers improved and more robust results compare to other algorithms.

Suggested Citation

  • Jin Yi & Xinyu Li & Chih-Hsing Chu & Liang Gao, 2019. "Parallel chaotic local search enhanced harmony search algorithm for engineering design optimization," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 405-428, January.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:1:d:10.1007_s10845-016-1255-5
    DOI: 10.1007/s10845-016-1255-5
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    Cited by:

    1. Zhang, Jinzhong & Zhang, Gang & Kong, Min & Zhang, Tan & Wang, Duansong & Chen, Rui, 2023. "CWOA: A novel complex-valued encoding whale optimization algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 151-188.
    2. Uğur Erkin Kocamaz & Alper Göksu & Harun Taşkın & Yılmaz Uyaroğlu, 2021. "Control of chaotic two-predator one-prey model with single state control signals," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1563-1572, August.
    3. Yiying Zhang & Zhigang Jin, 2022. "Comprehensive learning Jaya algorithm for engineering design optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1229-1253, June.

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