Enhancing Differential Evolution on Continuous Optimization Problems by Detecting Promising Leaders

Cai, Yiqiao; Wu, Duanwei; Liu, Shaopeng; Fu, Shunkai; Liu, Peizhong

doi:10.1109/access.2020.3045423

Cited by 1 publication

(2 citation statements)

References 82 publications

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Section: Elite-guided Strategymentioning

confidence: 99%

“…Elite-guided strategies aim to direct individuals towards regions with a superior fitness, accelerating the convergence process [27]. However, this may result in premature convergence, trapping the algorithm in local optima [27]. Therefore, designing an appropriate elite-guided strategy is a crucial aspect of research on intelligent optimization algorithms.…”

Section: Elite-guided Strategymentioning

confidence: 99%

See 1 more Smart Citation

Turbofan Engine Health Prediction Model Based on ESO-BP Neural Network

Zhang,

Xu,

Dai

et al. 2024

Applied Sciences

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Predicting the health index of turbofan engines is critical in reducing downtime and ensuring aircraft safety. This study introduces the elite snake optimizer-back propagation (ESO-BP) model to address the challenges of low accuracy and poor stability in predicting the health index of turbofan engines through neural networks. Firstly, the snake optimizer (SO) was improved into the elite snake optimizer (ESO) through an elite-guided strategy and a reverse learning mechanism. The performance improvement was validated using benchmark functions. Additionally, feature importance was introduced as a feature selection method. Finally, the optimization results of the ESO were employed to set the initial weights and biases of the BP neural network, preventing convergence to local optima. The prediction performance of the ESO-BP model was validated using the C-MAPSS datasets. The ESO-BP model was compared with the CNN, RNN, LSTM, baseline BP, and unimproved SO-BP models. The results demonstrated that the ESO-BP model has a superior accuracy with an impressive R-squared (R2) value of 0.931 and a root mean square error (RMSE) of 0.060 on the FD001 sub-dataset. Furthermore, the ESO-BP model exhibited lower standard deviations of evaluation metrics on 100 trials. According to the study, ESO-BP demonstrated a greater prediction accuracy and stability when compared to commonly used models such as CNN, RNN, LSTM, and BP.

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Section: Elite-guided Strategymentioning

confidence: 99%

Section: Elite-guided Strategymentioning

confidence: 99%