An Improved Reptile Search Algorithm Based on Lévy Flight and Interactive Crossover Strategy to Engineering Application

Huang, Liqiong; Wang, Yuanyuan; Guo, Yuxuan; Hu, Gang

doi:10.3390/math10132329

Cited by 15 publications

(9 citation statements)

References 74 publications

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“…The important goal of this work is to optimize BILSTM’s hyperparameters using the LICRSA ( Huang et al, 2022 ) and obtain improved performance in the classification. In general, the RSA replicates the predation plan and social activities of crocodiles.…”

Section: Bilstm–licrsa Methodsmentioning

confidence: 99%

Hyperparameter tuning using Lévy flight and interactive crossover-based reptile search algorithm for eye movement event classification

Pradeep,

Jayachandra,

Askar

et al. 2024

Front. Physiol.

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Introduction: Eye movement is one of the cues used in human–machine interface technologies for predicting the intention of users. The developing application in eye movement event detection is the creation of assistive technologies for paralyzed patients. However, developing an effective classifier is one of the main issues in eye movement event detection.Methods: In this paper, bidirectional long short-term memory (BILSTM) is proposed along with hyperparameter tuning for achieving effective eye movement event classification. The Lévy flight and interactive crossover-based reptile search algorithm (LICRSA) is used for optimizing the hyperparameters of BILSTM. The issues related to overfitting are avoided by using fuzzy data augmentation (FDA), and a deep neural network, namely, VGG-19, is used for extracting features from eye movements. Therefore, the optimization of hyperparameters using LICRSA enhances the classification of eye movement events using BILSTM.Results and Discussion: The proposed BILSTM–LICRSA is evaluated by using accuracy, precision, sensitivity, F1-score, area under the receiver operating characteristic (AUROC) curve measure, and area under the precision–recall curve (AUPRC) measure for four datasets, namely, Lund2013, collected dataset, GazeBaseR, and UTMultiView. The gazeNet, human manual classification (HMC), and multi-source information-embedded approach (MSIEA) are used for comparison with the BILSTM–LICRSA. The F1-score of BILSTM–LICRSA for the GazeBaseR dataset is 98.99%, which is higher than that of the MSIEA.

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Section: Bilstm–licrsa Methodsmentioning

confidence: 99%

Hyperparameter tuning using Lévy flight and interactive crossover-based reptile search algorithm for eye movement event classification

Pradeep,

Jayachandra,

Askar

et al. 2024

Front. Physiol.

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“…A comparison of FMSSA with some improved algorithms (CSOAOA [25], LHHO [37], LICRSA [38], G-QPSO [39]) and the latest proposed algorithms (HBA [40], WSO [41], GJO [42]) is given in Table 7. e optimal mean and standard deviation are given in bold.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Equations ( 36) to (38) are optimization models based on the shortest arc length, the smallest energy, and the smallest curvature change rate of the dual curve of the generalized developable Bézier-like surface, respectively. e concrete numerical examples are given in the next section.…”

Section: Optimization Modelmentioning

confidence: 99%

Enhanced Salp Swarm Algorithm for Optimizing the Shape of Developable Bézier-Like Surfaces

Zhong

et al. 2022

Mathematical Problems in Engineering

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The shape optimization of developable surfaces is a key and difficult technology in CAD/CAM. This paper studies the shape optimization of generalized developable Bézier-like surfaces using the improved salp swarm algorithm. First, aiming at the problems of slow convergence speed and low calculation accuracy, quickly falling into the optimal local solution for the salp swarm algorithm, and by adjusting the control parameters, the added Morlet wavelet mutation strategy to the individual position updates operation. An improved salp swarm algorithm is proposed, called FMSSA for short. Numerical results show that FMSSA performed the best among 18 (78%) comparisons with other improved SSAs in the CEC2005 test set. In addition, FMSSA serves the best among 16 (69%) comparisons with other algorithms. Therefore, the convergence speed and computational accuracy of FMSSA are improved, and the computational results are significantly better than other intelligent algorithms. Second, according to the duality principle of point and plane, the shape optimization problem of generalized developable Bézier-like surface is transformed into the minimization problem of arc length, energy, and curvature change rate of the dual curve, and three shape optimization models of the developable surface are established. Finally, shape optimization of expandable surfaces is mathematically an optimization problem that the swarm intelligence algorithm can efficiently handle, so FMSSA is used to solve these three optimization models. Numerical experiments show that FMSSA obtains optimal generalized developable Bézier-like surfaces in solving these three optimization models, demonstrating the superiority of the proposed FMSSA in effectively solving the shape optimization models in terms of precision and robustness.

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“…Zhang [ 19 ] used the Whale Optimization Algorithm in order to find the global optimal solution, realize hyperparameter optimization of the BiLSTM network, improve the prediction accuracy of wind power generation, and save a significant amount of time on debugging. Huang [ 20 ] proposed a crawling animal search algorithm based on the interactive cross strategy of Levy flight, and they verified its effectiveness in practical engineering via innovations such as the welding beam design.…”

Section: Introductionmentioning

confidence: 99%

Research on Wind Turbine Fault Detection Based on the Fusion of ASL-CatBoost and TtRSA

Kong

Liang

Liu

et al. 2023

Sensors

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The internal structure of wind turbines is intricate and precise, although the challenging working conditions often give rise to various operational faults. This study aims to address the limitations of traditional machine learning algorithms in wind turbine fault detection and the imbalance of positive and negative samples in the fault detection dataset. To achieve the real-time detection of wind turbine group faults and to capture wind turbine fault state information, an enhanced ASL-CatBoost algorithm is proposed. Additionally, a crawling animal search algorithm that incorporates the Tent chaotic mapping and t-distribution mutation strategy is introduced to assess the sensitivity of the ASL-CatBoost algorithm toward hyperparameters and the difficulty of manual hyperparameter setting. The effectiveness of the proposed hyperparameter optimization strategy, termed the TtRSA algorithm, is demonstrated through a comparison of traditional intelligent optimization algorithms using 11 benchmark test functions. When applied to the hyperparameter optimization of the ASL-CatBoost algorithm, the TtRSA-ASL-CatBoost algorithm exhibits notable enhancements in accuracy, recall, and other performance measures compared with the ASL-CatBoost algorithm and other ensemble learning algorithms. The experimental results affirm that the proposed algorithm model improvement strategy effectively enhances the wind turbine fault detection classification recognition rate.

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An Improved Reptile Search Algorithm Based on Lévy Flight and Interactive Crossover Strategy to Engineering Application

Cited by 15 publications

References 74 publications

Hyperparameter tuning using Lévy flight and interactive crossover-based reptile search algorithm for eye movement event classification

Hyperparameter tuning using Lévy flight and interactive crossover-based reptile search algorithm for eye movement event classification

Enhanced Salp Swarm Algorithm for Optimizing the Shape of Developable Bézier-Like Surfaces

Research on Wind Turbine Fault Detection Based on the Fusion of ASL-CatBoost and TtRSA

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