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Engineering with Computers

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07.06.2021 | Original Article

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

verfasst von: Betül Sultan Yildiz, Nantiwat Pholdee, Sujin Bureerat, Ali Riza Yildiz, Sadiq M. Sait

Erschienen in: Engineering with Computers | Ausgabe 5/2022

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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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Titel: Enhanced grasshopper optimization algorithm using elite opposition-based learning for solving real-world engineering problems
verfasst von: Betül Sultan Yildiz
Nantiwat Pholdee
Sujin Bureerat
Ali Riza Yildiz
Sadiq M. Sait
Publikationsdatum: 07.06.2021
Verlag: Springer London
Erschienen in: Engineering with Computers / Ausgabe 5/2022
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI: https://doi.org/10.1007/s00366-021-01368-w

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