Black-winged kite algorithm: a nature-inspired meta-heuristic for solving benchmark functions and engineering problems

Wang, Jun; Wang, Wen-chuan; Hu, Xiao-xue; Qiu, Lin; Zang, Hong-fei

doi:10.1007/s10462-024-10723-4

Cited by 49 publications

References 86 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Soft Sensor Modeling of Acrylic Acid Yield Based on Autoencoder Long Short‐Term Memory Neural Network of Savitzky–Golay and ReliefF Algorithm

Liu,

Zhang,

et al. 2024

Journal of Chemometrics

View full text Add to dashboard Cite

Acrylic acid yield (AAY) is a key quality index in production process of acrylic acid. Meanwhile, AAY has been considered as direct characterization of productivity. Aiming at the difficulty of online measurement of AAY in acrylic acid process, a soft sensing model of AAY based on autoencoder long short‐term memory neural network (AE LSTM NN) applying Savitzky–Golay and ReliefF method is presented in this paper. Firstly, Savitzky–Golay method with denoising effect is adopted to remove industrial noise in measurement. Then, ReliefF algorithm is developed to compress characteristic variables from the result of denoising. Finally, AE LSTM is employed to predict the AAY in acrylic acid process. In contrast to LSTM, support vector machine, and artificial neural network, the root mean square error (RMSE) of the provided method is 0.0954, mean absolute error (MAE) is 0.0757, mean absolute percent error (MAPE) is 0.09%, and maximum absolute error (MaxAE) is 0.3236, which shows validity and superiority.

show abstract

Soft Sensor Modeling of Acrylic Acid Yield Based on Autoencoder Long Short‐Term Memory Neural Network of Savitzky–Golay and ReliefF Algorithm

Liu,

Zhang,

et al. 2024

Journal of Chemometrics

View full text Add to dashboard Cite

show abstract

Prediction of total volatile basic nitrogen (TVB‐N) in fish meal using a metal‐oxide semiconductor electronic nose based on the VMD‐SSA‐LSTM algorithm

Li,

et al. 2024

J Sci Food Agric

View full text Add to dashboard Cite

BACKGROUNDThe total volatile basic nitrogen (TVB‐N) is the main indicator for evaluating the freshness of fish meal, and accurate detection and monitoring of TVB‐N is of great significance for the health of animals and humans. Here, to realize fast and accurate identification of TVB‐N, in this article, a self‐developed electronic nose (e‐nose) was used, and the mapping relationship between the gas sensor response characteristic information and TVB‐N value was established to complete the freshness detection.RESULTSThe TVB‐N variation curve was decomposed into seven subsequences with different frequency scales by means of variational mode decomposition (VMD). Each subsequence was modelled using different long short‐term memory (LSTM) models, and finally, the final TVB‐N prediction result was obtained by adding the prediction results based on different frequency components. To improve the performance of the LSTM, the sparrow search algorithm (SSA) was used to optimize the number of hidden units, learning rate and regularization coefficient of LSTM. The prediction results indicated that the high accuracy was obtained by the VMD‐LSTM model optimized by SSA in predicting TVB‐N. The coefficient of determination (R2), the root‐mean‐squared error (RMSE) and relative standard deviation (RSD) between the predicted value and the actual value of TVBN were 0.91, 0.115 and 6.39%, respectively.CONCLUSIONSThis method improves the performance of e‐nose in detecting the freshness of fish meal and provides a reference for the quality detection of e‐nose in other materials. © 2024 Society of Chemical Industry.

show abstract

Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey

Yang,

Gao,

Ding

et al. 2024

WIREs Data Min & Knowl

View full text Add to dashboard Cite

This paper reviews the integration of Q‐learning with meta‐heuristic algorithms (QLMA) over the last 20 years, highlighting its success in solving complex optimization problems. We focus on key aspects of QLMA, including parameter adaptation, operator selection, and balancing global exploration with local exploitation. QLMA has become a leading solution in industries like energy, power systems, and engineering, addressing a range of mathematical challenges. Looking forward, we suggest further exploration of meta‐heuristic integration, transfer learning strategies, and techniques to reduce state space.This article is categorized under: Technologies > Computational Intelligence Technologies > Artificial Intelligence

show abstract

Black-winged kite algorithm: a nature-inspired meta-heuristic for solving benchmark functions and engineering problems

Cited by 49 publications

References 86 publications

Soft Sensor Modeling of Acrylic Acid Yield Based on Autoencoder Long Short‐Term Memory Neural Network of Savitzky–Golay and ReliefF Algorithm

Soft Sensor Modeling of Acrylic Acid Yield Based on Autoencoder Long Short‐Term Memory Neural Network of Savitzky–Golay and ReliefF Algorithm

Prediction of total volatile basic nitrogen (TVB‐N) in fish meal using a metal‐oxide semiconductor electronic nose based on the VMD‐SSA‐LSTM algorithm

Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey

Contact Info

Product

Resources

About