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Enhanced grasshopper optimization algorithm using elite opposition-based learning for solving real-world engineering problems

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Abstract

Optimizing real-life engineering design problems are challenging and somewhat difficult if optimum solutions are expected. The development of new efficient optimization algorithms is crucial for this task. In this paper, a recently invented grasshopper optimization algorithm is upgraded from its original version. The method is improved by adding an elite opposition-based learning methodology to an elite opposition-based learning grasshopper optimization algorithm. The new optimizer, which is elite opposition-based learning grasshopper optimization method (EOBL-GOA), is validated with several engineering design probles such as a welded beam design problem, car side crash problem, multiple clutch disc problem, hydrostatic thrust bearing problem, three-bar truss, and cantilever beam problem, and finally used for the optimization of a suspension arm of the vehicles. The optimum results reveal that the EOBL-GOA is among the best algorithms reported in the literature.

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Authors and Affiliations

  1. Department of Electric and Energy, Bursa Uludağ University, Görükle, 16059, Turkey

    Betül Sultan Yildiz

  2. Sustainable Infrastructure Research and Development Center, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, 40002, Thailand

    Nantiwat Pholdee & Sujin Bureerat

  3. Department of Automotive Engineering, Bursa Uludağ University, Görükle, 16059, Turkey

    Ali Riza Yildiz

  4. Department of Computer Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia

    Sadiq M. Sait

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  1. Betül Sultan Yildiz
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  2. Nantiwat Pholdee
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  4. Ali Riza Yildiz
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  5. Sadiq M. Sait
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Correspondence to Betül Sultan Yildiz.

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Yildiz, B.S., Pholdee, N., Bureerat, S. et al. Enhanced grasshopper optimization algorithm using elite opposition-based learning for solving real-world engineering problems. Engineering with Computers 38, 4207–4219 (2022). https://doi.org/10.1007/s00366-021-01368-w

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