期刊
ENGINEERING WITH COMPUTERS
卷 38, 期 5, 页码 4207-4219出版社
SPRINGER
DOI: 10.1007/s00366-021-01368-w
关键词
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
The paper introduces a new optimization algorithm - elite opposition-based learning grasshopper optimization method (EOBL-GOA), and through validation with multiple engineering design problems, the results indicate that the method is considered one of the best algorithms reported in the literature.
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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