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A modified particle swarm optimization for large-scale numerical optimizations and engineering design problems

Author

Listed:
  • Hao Liu

    (University of Science and Technology Liaoning)

  • Yue Wang

    (University of Science and Technology Liaoning)

  • Liangping Tu

    (University of Science and Technology Liaoning)

  • Guiyan Ding

    (University of Science and Technology Liaoning)

  • Yuhan Hu

    (University of Science and Technology Liaoning)

Abstract

Particle swarm optimization (PSO) has attracted the attention of many researchers because of its simple concept and easy implementation. However, it suffers from premature convergence due to quick loss of population diversity. Meanwhile, real-world engineering design problems are generally nonlinear or large-scale or constrained optimization problems. To enhance the performance of PSO for solving large-scale numerical optimizations and engineering design problems, an adaptive disruption strategy which originates from the disruption phenomenon of astrophysics, is proposed to shift the abilities between global exploration and local exploitation. Meanwhile, a Cauchy mutation is utilized to a certain dimension of the best particle to help particle jump out the local optima. Nine well-known large-scale unconstrained problems, ten complicated shifted and/or rotated functions and four famous constrained engineering problems are utilized to validate the performance of the proposed algorithm compared against those of state-of-the-art algorithms. Experimental results and statistic analysis confirm effectiveness and promising performance of the proposed algorithm.

Suggested Citation

  • Hao Liu & Yue Wang & Liangping Tu & Guiyan Ding & Yuhan Hu, 2019. "A modified particle swarm optimization for large-scale numerical optimizations and engineering design problems," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2407-2433, August.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:6:d:10.1007_s10845-018-1403-1
    DOI: 10.1007/s10845-018-1403-1
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    References listed on IDEAS

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    1. M. Ali & W. Zhu, 2013. "A penalty function-based differential evolution algorithm for constrained global optimization," Computational Optimization and Applications, Springer, vol. 54(3), pages 707-739, April.
    2. Liu, Jianjun & Wu, Changzhi & Wu, Guoning & Wang, Xiangyu, 2015. "A novel differential search algorithm and applications for structure design," Applied Mathematics and Computation, Elsevier, vol. 268(C), pages 246-269.
    3. Haiyan Lu & Weiqi Chen, 2006. "Dynamic-objective particle swarm optimization for constrained optimization problems," Journal of Combinatorial Optimization, Springer, vol. 12(4), pages 409-419, December.
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    Cited by:

    1. Lenin Nagarajan & Siva Kumar Mahalingam & Jayakrishna Kandasamy & Selvakumar Gurusamy, 2022. "A novel approach in selective assembly with an arbitrary distribution to minimize clearance variation using evolutionary algorithms: a comparative study," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1337-1354, June.
    2. Raghav Prasad Parouha & Pooja Verma, 2022. "An innovative hybrid algorithm for bound-unconstrained optimization problems and applications," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1273-1336, June.
    3. Cosmena Mahapatra & Ashish Payal & Meenu Chopra, 2020. "Swarm intelligence based centralized clustering: a novel solution," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1877-1888, December.
    4. Zhijian Xiong & Jingming Yang & Zhiwei Zhao & Yongqiang Wang & Zhigang Yang, 2023. "Maximum angle evolutionary selection for many-objective optimization algorithm with adaptive reference vector," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 961-984, March.
    5. Robson Flavio Castro & Moacir Godinho-Filho & Roberto Fernandes Tavares-Neto, 2022. "Dispatching method based on particle swarm optimization for make-to-availability," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1021-1030, April.
    6. 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.
    7. Daniele Marini & Jonathan R. Corney, 2021. "Concurrent optimization of process parameters and product design variables for near net shape manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 611-631, February.

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