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

YILDIZ B. S., Pholdee N., Bureerat S., YILDIZ A. R., Sait S. M.

ENGINEERING WITH COMPUTERS, vol.38, no.5, pp.4207-4219, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 38 Issue: 5
  • Publication Date: 2022
  • Doi Number: 10.1007/s00366-021-01368-w
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.4207-4219
  • Keywords: Grasshopper optimization algorithm, Elite opposition-based learning, Welded beam, Vehicle crashworthiness, Multi-clutch disc, Hydrostatic thrust bearing design, Three-bar truss, Cantilever beam suspension arm, BIO-INSPIRED OPTIMIZER, DESIGN OPTIMIZATION, CRASHWORTHINESS
  • Bursa Uludag University Affiliated: Yes


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.