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Engineering with Computers
Erschienen in: Engineering with Computers 5/2022

03.01.2021 | Original Article

SGOA: annealing-behaved grasshopper optimizer for global tasks

verfasst von: Caiyang Yu, Mengxiang Chen, Kai Cheng, Xuehua Zhao, Chao Ma, Fangjun Kuang, Huiling Chen

Erschienen in: Engineering with Computers | Sonderheft 5/2022

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Abstract

An improved grasshopper optimization algorithm (GOA) is proposed in this paper, termed as SGOA, which combines simulated annealing (SA) mechanism with the original GOA that is a natural optimizer widely used in finance, medical and other fields, and receives more promising results based on grasshopper behavior. To compare performance of the SGOA and other algorithms, an investigation to select CEC2017 benchmark function as the test set was carried out. Also, the Friedman assessment was performed to check the significance of the proposed method against other counterparts. In comparison with ten meta-heuristic algorithms such as differential evolution (DE), the proposed SGOA can rank first in the CEC2017, and also ranks first in comparison with ten advanced algorithms. The simulation results reveal that the SA strategy notably improves the exploration and exploitation capacity of GOA. Moreover, the SGOA is also applied to engineering problems and parameter optimization of the kernel extreme learning machine (KELM). After optimizing the parameters of KELM using SGOA, the model was applied to two datasets, Cleveland Heart Dataset and Japanese Bankruptcy Dataset, and they achieved an accuracy of 79.2% and 83.5%, respectively, which were better than the KELM model obtained other algorithms. In these practical applications, it is indicated that the proposed SGOA can provide effective assistance in settling complex optimization problems with impressive results.
Vorheriger Artikel Impact of in-plane follower force on the frequency response of the hybrid angle-ply laminated system via dynamic simulation and generalized differential quadrature framework
Nächster Artikel Novel approach to evaluate rock mass fragmentation in block caving using unascertained measurement model and information entropy with flexible credible identification criterion

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Metadaten
Titel
SGOA: annealing-behaved grasshopper optimizer for global tasks
verfasst von
Caiyang Yu
Mengxiang Chen
Kai Cheng
Xuehua Zhao
Chao Ma
Fangjun Kuang
Huiling Chen
Publikationsdatum
03.01.2021
Verlag
Springer London
Erschienen in
Engineering with Computers / Ausgabe Sonderheft 5/2022
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-020-01234-1

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