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- W4293702562 abstract "Artificial hummingbird algorithm is a new MH algorithm with a simple mechanism and is easy to implement. It shows a strong competitive edge. However, when solving complex multimodal and engineering design problems, the AHA has disadvantages such as being easy to fall into local optimum and slow convergence speed. Therefore, an effective AHA (JOS-MrAHA) based on mirror reflection learning and joint opposite strategy is proposed in this paper. First, a mirror reflection learning based on the reflection principle is introduced to expand the search area and enhance the search capability. In addition, by adding the joint opposite strategy, the ability of JOS-MrAHA to jump out of the locally optimal solution is enhanced, and the convergence speed is accelerated. In addition, JOS-MrAHA and three latest MH algorithms are used to solve the CEC2019 test suit. The evaluation metrics obtained by JOS-MrAHA are generally better than the other three MH algorithms. Finally, JOS-MrAHA is applied to the rolling element bearing problem, and the numerical results verify the applicability and high efficiency of JOS-MrAHA in solving rolling element bearing mechanical problems." @default.
- W4293702562 created "2022-08-31" @default.
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- W4293702562 date "2022-08-30" @default.
- W4293702562 modified "2023-09-28" @default.
- W4293702562 title "JOS-MrAHA: an effective artificial hummingbird algorithm for rolling element bearing problems" @default.
- W4293702562 doi "https://doi.org/10.1117/12.2645651" @default.
- W4293702562 hasPublicationYear "2022" @default.
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