Q-learning improved golden jackal optimization algorithm and its application to reliability optimization of hydraulic system
Abstract To endow the prey with intelligent movement behavior and improve the performance of Golden Jackal Optimization (GJO), a Q-learning Improved Gold Jackal Optimization (QIGJO) algorithm is proposed. This paper introduces five update mechanisms and proposes double-population Q-learning collabor...
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| 主要な著者: | , , , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Nature Portfolio
2024-10-01
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| シリーズ: | Scientific Reports |
| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1038/s41598-024-75374-5 |
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