Improve the Hunger Games search algorithm to optimize the GoogleNet model.
The setting of parameter values will directly affect the performance of the neural network, and the manual parameter tuning speed is slow, and it is difficult to find the optimal combination of parameters. Based on this, this paper applies the improved Hunger Games search algorithm to find the optim...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0305653 |
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| _version_ | 1850169596794896384 |
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| author | Yanqiu Li Shizheng Qu Huan Liu |
| author_facet | Yanqiu Li Shizheng Qu Huan Liu |
| author_sort | Yanqiu Li |
| collection | DOAJ |
| description | The setting of parameter values will directly affect the performance of the neural network, and the manual parameter tuning speed is slow, and it is difficult to find the optimal combination of parameters. Based on this, this paper applies the improved Hunger Games search algorithm to find the optimal value of neural network parameters adaptively, and proposes an ATHGS-GoogleNet model. Firstly, adaptive weights and chaos mapping were integrated into the hunger search algorithm to construct a new algorithm, ATHGS. Secondly, the improved ATHGS algorithm was used to optimize the parameters of GoogleNet to construct a new model, ATHGS-GoogleNet. Finally, in order to verify the effectiveness of the proposed algorithm ATHGS and the model ATHGS-GoogleNet, a comparative experiment was set up. Experimental results show that the proposed algorithm ATHGS shows the best optimization performance in the three engineering experimental designs, and the accuracy of the proposed model ATHGS-GoogleNet reaches 98.1%, the sensitivity reaches 100%, and the precision reaches 99.5%. |
| format | Article |
| id | doaj-art-3944495ee5c64fefacea1ca99fae6b9f |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-3944495ee5c64fefacea1ca99fae6b9f2025-08-20T02:20:41ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-01198e030565310.1371/journal.pone.0305653Improve the Hunger Games search algorithm to optimize the GoogleNet model.Yanqiu LiShizheng QuHuan LiuThe setting of parameter values will directly affect the performance of the neural network, and the manual parameter tuning speed is slow, and it is difficult to find the optimal combination of parameters. Based on this, this paper applies the improved Hunger Games search algorithm to find the optimal value of neural network parameters adaptively, and proposes an ATHGS-GoogleNet model. Firstly, adaptive weights and chaos mapping were integrated into the hunger search algorithm to construct a new algorithm, ATHGS. Secondly, the improved ATHGS algorithm was used to optimize the parameters of GoogleNet to construct a new model, ATHGS-GoogleNet. Finally, in order to verify the effectiveness of the proposed algorithm ATHGS and the model ATHGS-GoogleNet, a comparative experiment was set up. Experimental results show that the proposed algorithm ATHGS shows the best optimization performance in the three engineering experimental designs, and the accuracy of the proposed model ATHGS-GoogleNet reaches 98.1%, the sensitivity reaches 100%, and the precision reaches 99.5%.https://doi.org/10.1371/journal.pone.0305653 |
| spellingShingle | Yanqiu Li Shizheng Qu Huan Liu Improve the Hunger Games search algorithm to optimize the GoogleNet model. PLoS ONE |
| title | Improve the Hunger Games search algorithm to optimize the GoogleNet model. |
| title_full | Improve the Hunger Games search algorithm to optimize the GoogleNet model. |
| title_fullStr | Improve the Hunger Games search algorithm to optimize the GoogleNet model. |
| title_full_unstemmed | Improve the Hunger Games search algorithm to optimize the GoogleNet model. |
| title_short | Improve the Hunger Games search algorithm to optimize the GoogleNet model. |
| title_sort | improve the hunger games search algorithm to optimize the googlenet model |
| url | https://doi.org/10.1371/journal.pone.0305653 |
| work_keys_str_mv | AT yanqiuli improvethehungergamessearchalgorithmtooptimizethegooglenetmodel AT shizhengqu improvethehungergamessearchalgorithmtooptimizethegooglenetmodel AT huanliu improvethehungergamessearchalgorithmtooptimizethegooglenetmodel |