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SGOA: annealing-behaved grasshopper optimizer for global tasks

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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.

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Acknowledgment

This research is supported by National Natural Science Foundation of China (62076185, 71803136, U1809209), the Ministry of Education of Humanities and Social Science Project of Wenzhou Business College (20YJA790090), the Characteristic Innovation Project of Guangdong Universities in 2020 (2020KTSCX302), Guangdong Natural Science Foundation (2018A030313339), Scientific Research Team Project of Shenzhen Institute of Information Technology (SZIIT2019KJ022).

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  1. Fangjun Kuang, Huiling Chen have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China

    Caiyang Yu & Huiling Chen

  2. Department of Information Technology, Wenzhou Vocational College of Science and Technology, Wenzhou, 325006, Zhejiang, China

    Mengxiang Chen

  3. State Grid Hebei Information & Telecommunication Branch, Shijiazhuang, 050021, China

    Kai Cheng

  4. School of Digital Media, Shenzhen Institute of Information Technology, Shenzhen, 518172, China

    Xuehua Zhao & Chao Ma

  5. School of Information Engineering, Wenzhou Business College, Wenzhou, 325035, China

    Fangjun Kuang

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Yu, C., Chen, M., Cheng, K. et al. SGOA: annealing-behaved grasshopper optimizer for global tasks. Engineering with Computers 38 (Suppl 5), 3761–3788 (2022). https://doi.org/10.1007/s00366-020-01234-1

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  • DOI: https://doi.org/10.1007/s00366-020-01234-1

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