Algorithm Hyperparameter Optimization Research Articles

Multi-strategy modified sparrow search algorithm for hyperparameter optimization in arbitrage prediction models.

Deep learning models struggle to effectively capture data features and make accurate predictions because of the strong non-linear characteristics of arbitrage data. Therefore, to fully exploit the model performance, researchers have focused on network structure and hyperparameter selection using various swarm intelligence algorithms for optimization. Sparrow Search Algorithm (SSA), a classic heuristic method that simulates the sparrows' foraging and anti-predatory behavior, has demonstrated excellent performance in various optimization problems. Hence, in this study, the Multi-Strategy Modified Sparrow Search Algorithm (MSMSSA) is applied to the Long Short-Term Memory (LSTM) network to construct an arbitrage spread prediction model (MSMSSA-LSTM). In the modified algorithm, the good point set theory, the proportion-adaptive strategy, and the improved location update method are introduced to further enhance the spatial exploration capability of the sparrow. The proposed model was evaluated using the real spread data of rebar and hot coil futures in the Chinese futures market. The obtained results showed that the mean absolute percentage error, root mean square error, and mean absolute error of the proposed model had decreased by a maximum of 58.5%, 65.2%, and 67.6% compared to several classical models. The model has high accuracy in predicting arbitrage spreads, which can provide some reference for investors.

A data-driven approach to urban charging facility expansion based on bi-level optimization: A case study in a Chinese city

This paper addresses the optimization of urban charging facilities expansion in response to the growing charging demand and proposes a data-driven bi-level optimization framework. The proposed framework combines a mixed-integer programming model for siting and capacity planning, an improved sparrow search algorithm (ISSA) for hyperparameter optimization of the CNN-BiLSTM model, and the application of Benders decomposition with Lagrangian relaxation using Gurobi for solving the model. The proposed approach is empirically evaluated through a case study conducted in an administrative area of a Chinese city, using comprehensive charging records and other relevant information. The results demonstrate that the ISSA consistently outperforms other algorithms, showing an average improvement of 23.97 % in root mean square error, 19.25 % in average absolute percentage error, and 1.50 % in the determination coefficient. The modified Diebold-Mariano test confirms the significant advantage of ISSA. Based on the model analysis, the study recommends constructing 20 new charging stations in the case city, with more than half equipped with fast chargers. This strategy ensures that 95 % of the area can find a charging station within 5 min while also ensuring the operator's profitability. The research findings provide effective support for investment decisions related to charging facilities.

Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project Health

When data is scarce, software analytics can make many mistakes. For example, consider learning predictors for open source project health (e.g., the number of closed pull requests in 12 months time). The training data for this task may be very small (e.g., 5 years of data, collected every month means just 60 rows of training data). The models generated from such tiny datasets can make many prediction errors. Those errors can be tamed by a landscape analysis that selects better learner control parameters. Our niSNEAK tool (a) clusters the data to find the general landscape of the hyperparameters, then (b) explores a few representatives from each part of that landscape. niSNEAK is both faster and more effective than prior state-of-the-art hyperparameter optimization algorithms (e.g., FLASH, HYPEROPT, OPTUNA). The configurations found by niSNEAK have far less error than other methods. For example, for project health indicators such as C = number of commits, I = number of closed issues, and R = number of closed pull requests, niSNEAK ’s 12-month prediction errors are {I=0%, R=33% C=47%}, whereas other methods have far larger errors of {I=61%,R=119% C=149%}. We conjecture that niSNEAK works so well since it finds the most informative regions of the hyperparameters, then jumps to those regions. Other methods (that do not reflect over the landscape) can waste time exploring less informative options. Based on the preceding, we recommend landscape analytics (e.g., niSNEAK ) especially when learning from very small datasets. This article only explores the application of niSNEAK to project health. That said, we see nothing in principle that prevents the application of this technique to a wider range of problems. To assist other researchers in repeating, improving, or even refuting our results, all our scripts and data are available on GitHub at https://github.com/zxcv123456qwe/niSneak.

Daily Runoff Prediction with a Seasonal Decomposition-Based Deep GRU Method

Accurately predicting hydrological runoff is crucial for water resource allocation and power station scheduling. However, there is no perfect model that can accurately predict future runoff. In this paper, a daily runoff prediction method with a seasonal decomposition-based-deep gated-recurrent-unit (GRU) method (SD-GRU) is proposed. The raw data is preprocessed and then decomposed into trend, seasonal, and residual components using the seasonal decomposition algorithm. The deep GRU model is then used to predict each subcomponent, which is then integrated into the final prediction results. In particular, the hyperparameter optimization algorithm of tree-structured parzen estimators (TPE) is used to optimize the model. Moreover, this paper introduces the single machine learning model (including multiple linear regression (MLR), back propagation (BP), long short-term memory neural network (LSTM) and gate recurrent unit (GRU)) and a combination model (including seasonal decomposition–back propagation (SD-BP), seasonal decomposition–multiple linear regression (SD-MLR), along with seasonal decomposition–long-and-short-term-memory neural network (SD-LSTM), which are used as comparison models to verify the excellent prediction performance of the proposed model. Finally, a case study of the Qingjiang Shuibuya test set, which considers the period 1 January 2019 to 31 December 2019, is conducted. Case studies of the Qingjiang River show the proposed model outperformed the other models in prediction performance. The model achieved a mean absolute error (MAE) index of 38.5, a Nash-Sutcliffe efficiency (NSE) index of 0.93, and a coefficient of determination (R2) index of 0.7. In addition, compared to the comparison model, the NSE index of the proposed model increased by 19.2%, 19.2%, 16.3%, 16.3%, 2.2%, 2.2%, and 1.1%, when compared to BP, MLR, LSTM, GRU, SD-BP, SD-MLR, SD-LSTM, and SD-GRU, respectively. This research can provide an essential reference for the study of daily runoff prediction models.

Landslide susceptibility mapping (LSM) based on different boosting and hyperparameter optimization algorithms: A case of Wanzhou District, China

Boosting algorithms have been widely utilized in the development of landslide susceptibility mapping (LSM) studies. However, these algorithms possess distinct computational strategies and hyperparameters, making it challenging to propose an ideal LSM model. To investigate the impact of different boosting algorithms and hyperparameter optimization algorithms on LSM, this study constructed a geospatial database comprising 12 conditioning factors, such as elevation, stratum, and annual average rainfall. The XGBoost (XGB), LightGBM (LGBM), and CatBoost (CB) algorithms were employed to construct the LSM model. Furthermore, the Bayesian optimization (BO), particle swarm optimization (PSO), and Hyperband optimization (HO) algorithms were applied to optimizing the LSM model. The boosting algorithms exhibited varying performances, with CB demonstrating the highest precision, followed by LGBM, and XGB showing poorer precision. Additionally, the hyperparameter optimization algorithms displayed different performances, with HO outperforming PSO and BO showing poorer performance. The HO-CB model achieved the highest precision, boasting an accuracy of 0.764, an F1-score of 0.777, an area under the curve (AUC) value of 0.837 for the training set, and an AUC value of 0.863 for the test set. The model was interpreted using SHapley Additive exPlanations (SHAP), revealing that slope, curvature, topographic wetness index (TWI), degree of relief, and elevation significantly influenced landslides in the study area. This study offers a scientific reference for LSM and disaster prevention research. This study examines the utilization of various boosting algorithms and hyperparameter optimization algorithms in Wanzhou District. It proposes the HO-CB-SHAP framework as an effective approach to accurately forecast landslide disasters and interpret LSM models. However, limitations exist concerning the generalizability of the model and the data processing, which require further exploration in subsequent studies.

Enhanced Sea Horse Optimization Algorithm for Hyperparameter Optimization of Agricultural Image Recognition

Deep learning technology has made significant progress in agricultural image recognition tasks, but the parameter adjustment of deep models usually requires a lot of manual intervention, which is time-consuming and inefficient. To solve this challenge, this paper proposes an adaptive parameter tuning strategy that combines sine–cosine algorithm with Tent chaotic mapping to enhance sea horse optimization, which improves the search ability and convergence stability of standard sea horse optimization algorithm (SHO). Through adaptive optimization, this paper determines the best parameter configuration in ResNet-50 neural network and optimizes the model performance. The improved ESHO algorithm shows superior optimization effects than other algorithms in various performance indicators. The improved model achieves 96.7% accuracy in the corn disease image recognition task, and 96.4% accuracy in the jade fungus image recognition task. These results show that ESHO can not only effectively improve the accuracy of agricultural image recognition, but also reduce the need for manual parameter adjustment.

Developmental Prediction of Poststroke Patients in Activities of Daily Living by Using Tree-Structured Parzen Estimator-Optimized Stacking Ensemble Approaches.

Poststroke injuries limit the daily activities of patients and cause considerable inconvenience. Therefore, predicting the activities of daily living (ADL) results of patients with stroke before hospital discharge can assist clinical workers in formulating more personalized and effective strategies for therapeutic intervention, and prepare hospital discharge plans that suit the patients needs. This study used the leave-one-out cross-validation procedure to evaluate the performance of the machine learning models. In addition, testing methods were used to identify the optimal weak learners, which were then combined to form a stacking model. Subsequently, a hyperparameter optimization algorithm was used to optimize the model hyperparameters. Finally, optimization algorithms were used to analyze each feature, and features of high importance were identified by limiting the number of features to be included in the machine learning models. After various features were fed into the learning models to predict the Barthel index (BI) at discharge, the results indicated that random forest (RF), adaptive boosting (AdaBoost), and multilayer perceptron (MLP) produced suitable results. The most critical prediction factor of this study was the BI at admission. Machine learning models can be used to assist clinical workers in predicting the ADL of patients with stroke at hospital discharge.

Improved Design for Hardware Implementation of Graph-Based Large Margin Classifiers for Embedded Edge Computing.

The number of connected embedded edge computing Internet of Things (IoT) devices has been increasing over the years, contributing to the significant growth of available data in different scenarios. Thereby, machine learning algorithms arise to enable task automation and process optimization based on those data. However, due to some learning methods' computational complexity implementing geometric classifiers, it is a challenge to map these on embedded systems or devices with limited resources in size, processing, memory, and power, to accomplish the desired requirements. This hampers the applicability of these methods to complex industrial embedded edge applications. This work evaluates strategies to reduce classifiers' implementation costs based on the CHIP-clas model, independent of hyperparameter tuning and optimization algorithms. The proposal aims to evaluate the tradeoff between numerical precision and model performance and analyze the hardware implementations of a distance-based classifier. Two 16 -b floating-point formats were compared to the 32 -b floating-point precision implementation. Also, a new hardware architecture was developed and then compared to the state-of-the-art reference. The results indicate that the model is robust to low precision computation, providing statistically equivalent results compared to the baseline model, also pointing out statistically equivalent performance and a global speed-up factor of approx 4.39 in processing time.

Neural Network Predictive Models for Alkali-Activated Concrete Carbon Emission Using Metaheuristic Optimization Algorithms

Due to environmental impacts and the need for energy efficiency, the cement industry aims to make more durable and sustainable materials with less energy requirements without compromising mechanical properties based on UN Sustainable Development Goals 9 and 11. Carbon dioxide (CO2) emission into the atmosphere is mostly the result of human-induced activities and causes dangerous environmental impacts by increasing the average temperature of the earth. Since the production of ordinary Portland cement (PC) is a major contributor to CO2 emissions, this study proposes alkali-activated binders as an alternative to reduce the environmental impact of ordinary Portland cement production. The dataset required for the training processes of these algorithms was created using Mendeley as a data-gathering instrument. Some of the most efficient state-of-the-art meta-heuristic optimization algorithms were applied to obtain the optimal neural network architecture with the highest performance. These neural network models were applied in the prediction of carbon emissions. The accuracy of these models was measured using statistical measures such as the mean squared error (MSE) and coefficient of determination (R2). The results show that carbon emissions associated with the production of alkali-activated concrete can be predicted with high accuracy using state-of-the-art machine learning techniques. In this study, in which the binders produced by the alkali activation method were evaluated for their usability as a binder material to replace Portland cement, it is concluded that the most successful hyperparameter optimization algorithm for this study is the genetic algorithm (GA) with accurate mean squared error (MSE = 161.17) and coefficient of determination (R2 = 0.90) values in the datasets.

Enhancing Internet of Things Network Security Using Hybrid CNN and XGBoost Model Tuned via Modified Reptile Search Algorithm

This paper addresses the critical security challenges in the internet of things (IoT) landscape by implementing an innovative solution that combines convolutional neural networks (CNNs) for feature extraction and the XGBoost model for intrusion detection. By customizing the reptile search algorithm for hyperparameter optimization, the methodology provides a resilient defense against emerging threats in IoT security. By applying the introduced algorithm to hyperparameter optimization, better-performing models are constructed capable of efficiently handling intrusion detection. Two experiments are carried out to evaluate the introduced technique. The first experiment tackles detection through binary classification. The second experiment handles the task by specifically identifying the type of intrusion through multi-class classification. A publicly accessible real-world dataset has been utilized for experimentation and several contemporary algorithms have been subjected to a comparative analysis. The introduced algorithm constructed models with the best performance in both cases. The outcomes have been meticulously statistically evaluated and the best-performing model has been analyzed using Shapley additive explanations to determine feature importance for model decisions.

Lung Cancer Detection Refined: A Study on SVM Hyperparameter Tuning Using BayesOptimization

This research focuses on the application and enhancement of machine learning algorithms for the detection and differentiation of various types of cancers, with a primary emphasis on lung cancer. Central to this study is the integration of the Bayes Optimization algorithm for hyperparameter optimization and the XGBoost algorithm for predictive modelling. A significant aspect of this work involves the strategic reduction of hyper-features, aimed at refining the XGBoost model's performance. This process not only ensures a more efficient model but also contributes to a higher accuracy in cancer-type prediction. Additionally, a comparative analysis is conducted with other ensemble models to evaluate the relative performance improvements. The findings of this study are pivotal, as they demonstrate the optimized model's enhanced capability in accurately detecting different cancer types, particularly lung cancer, and show marked advancements over other contemporary models. The research highlights the potential of combining advanced machine learning techniques for significant improvements in oncological diagnostics and treatment planning.

Advanced soft computing techniques for predicting punching shear strength

Accurate prediction of punching shear strength is crucial for ensuring the structural integrity and safety of reinforced concrete (RC) flat slab-column connections. However, estimating punching shear strength in complex configurations, such as RC flat slabs, remains a challenging task due to intricate interactions between various parameters. To address this challenge, this study introduces an innovative approach that combines machine learning techniques with hyperparameter optimization strategies to enhance the precision and efficiency of predicting punching shear strength. Specifically, a comprehensive investigation is conducted to assess the performance of XGBoost optimized by different hyperparameter optimization algorithms such as the forensic-based investigation (FBI), Bayesian optimization (BO), grid search (GS), random search (RS), and default setting (DS). The experimental results of 40 runs on three different datasets confirmed the FBI-XGBoost model as the most appropriate for predicting punching shear strength in terms of efficiency and computation time by achieving the highest RMSE, MAPE, MAE, and R2 values. In addition, the statistical results exposed the infeasibility of using RS for the XGBoost model. While the GS method was able to configure the XGBoost model well, doing so required extensive computational time, while the BO-FBI model achieved acceptable prediction accuracy in a reasonable training time. The FBI-XGBoost model was further compared with empirical methods, including ACI-318-08, BS8110-97, and CEB-FIP-90, based on the predictive-to-actual ratio. FBI-XGBoost was identified as the best model by attaining the ratio value closest to 1 and the lowest standard deviation (1.011 ± 0.108). Overall, the statistical experimental results and hypothesis test values firmly support the FBI-XGBoost model as the most reliable free-formulae method for predicting punching shear strength in RC flat slab-column systems.

A surrogate-assisted highly cooperative coevolutionary algorithm for hyperparameter optimization in deep convolutional neural networks

Hyperparameter optimization in convolutional neural networks (CNNs) plays a vital role in ensuring the effectiveness of the models. However, with the depth of the existing CNN expanding, this task becomes very challenging due to the high-dimensional and computationally expensive characteristics. Given these difficulties, this study proposes a surrogate-assisted highly cooperative hyperparameter optimization (SHCHO) algorithm for large-scale chain-styled CNNs. SHCHO tackles the original hyperparameter optimization problem by cooperatively optimizing its subproblems. Specifically, it first decomposes the whole CNN into several overlapping sub-CNNs, which conforms to the inherent overlapping interaction structure among hyperparameters and significantly reduces the search space. The resulting hyperparameter optimization subproblems on these sub-CNNs are then coevolved collaboratively and competitively, facilitating a proper hyperparameter configuration for the whole CNN. Besides, SHCHO also employs the computationally efficient surrogate technique to assist in each subproblem optimization. As a result, the expensive computational cost can be greatly reduced. Extensive experimental results on three widely-used image classification datasets indicate that SHCHO shows competitive performance when compared with several state-of-the-art algorithms in terms of both hyperparameter superiority and computational cost.

Modeling and structural optimization design of switched reluctance motor based on fusing attention mechanism and CNN-BiLSTM

Six-degrees of freedom (6-DOF) parallel mechanisms driven by switched reluctance motors (SRMs) can realize flexible control with high precision. Efficiency is an important indicator to measure the speed control system of SRMs. There are many characteristic factors affecting efficiency and strong nonlinear relationships between different characteristic parameters, which makes it difficult for analytical models and traditional neural network models to express their spatial correlation. For this reason, a convolutional neural network (CNN)-bidirectional long short-term memory network (BiLSTM) efficiency regression prediction model (CNN-BiLSTM-SENet) that integrates the attention mechanism (SENet) is proposed. Firstly, customize a formula for sensitivity analysis to screen characteristic parameters of efficiency. Secondly, build the CNN-BiLSTM-SENet model and use sparrow search algorithm (SSA) for hyperparameter optimization, input data to CNN to extract high-dimensional feature vectors that reflect complex changing relationships between features and efficiency while establishing feature channels. Embed the SENet to adaptively perceive and assign different weights to the feature channels, enhancing the influence of key features. Input the feature vectors outputted by the front-end network to BiLSTM to bidirectionally learn coupling relationships between sequences and complete regression prediction. Finally, propose improved northern goshawk optimization (MNGO) to solve the regression model to obtain the maximum efficiency and corresponding characteristic parameters. The results proved that the SSA-optimized CNN-BiLSTM-SENet model has higher fitting exactness and better prediction effect for efficiency regression prediction, and the MNGO also has stronger search ability and faster convergence.

Towards spatio-temporal crime events prediction

The importance of early prediction in reducing the impact of crime cannot be overstated. Machine learning algorithms have proven to be effective in this regard, but their inability to capture key features automatically can be a hindrance. To overcome this challenge, we propose a deep neural network model that is capable of extracting salient features automatically for predicting crime categories using real-world crime data sourced from the Chicago open data portal. To ensure the robustness of our proposed model, we carried out an extensive exploratory data analysis to determine the impact of socioeconomic indicators on crime occurrences. Additionally, we implemented a data upsampling technique to handle class imbalance issues, and we leveraged hyperparameter optimization algorithms to fine-tune the model. The results of our study were impressive. Our proposed model outperformed the baseline model and other algorithms, with an average improvement of 6% in macro F1 score. This suggests that our model is highly effective, if not superior, in predicting crime categories accurately. Overall, our study provides a solid framework for using deep neural network models in crime prediction, while highlighting the importance of automatic feature extraction in enhancing the accuracy of predictions. By reducing the impact of crime through early prediction, we can help to create a safer and more secure society.

Biophysical Variable Retrieval of Silage Maize with Gaussian Process Regression and Hyperparameter Optimization Algorithms

Quantification of vegetation biophysical variables such as leaf area index (LAI), fractional vegetation cover (fCover), and biomass are among the key factors across hydrological, agricultural, and irrigation management studies. The present study proposes a kernel-based machine learning algorithm capable of performing adaptive and nonlinear data fitting so as to generate a suitable, accurate, and robust algorithm for spatio-temporal estimation of the three mentioned variables using Sentinel-2 images. To this aim, Gaussian process regression (GPR)–particle swarm optimization (PSO), GPR–genetic algorithm (GA), GPR–tabu search (TS), and GPR–simulated annealing (SA) hyperparameter-optimized algorithms were developed and compared against kernel-based machine learning regression algorithms and artificial neural network (ANN) and random forest (RF) algorithms. The accuracy of the proposed algorithms was assessed using digital hemispherical photography (DHP) data and destructive measurements performed during the growing season of silage maize in agricultural fields of Ghale-Nou, southern Tehran, Iran, in the summer of 2019. The results on biophysical variables against validation data showed that the developed GPR-PSO algorithm outperformed other algorithms under study in terms of robustness and accuracy (0.917, 0.931, 0.882 using R2 and 0.627, 0.078, and 1.99 using RMSE in LAI, fCover, and biomass of Sentinel-2 20 m, respectively). GPR-PSO also possesses the unique ability to generate pixel-based uncertainty maps (confidence level) for prediction purposes (i.e., estimated uncertainty level <0.7 in LAI, fCover, and biomass, for 96%, 98%, and 71% of the total study area, respectively). Altogether, GPR-PSO appears to be the most suitable option for mapping biophysical variables at the local scale using Sentinel-2 images.

A novel hybrid PSO- and GS-based hyperparameter optimization algorithm for support vector regression

A novel hybrid PSO- and GS-based hyperparameter optimization algorithm for support vector regression

SoftStep relaxation for mining optimal convolution kernel

An efficient convolution kernel could transform images into more expressive representations, which usually determines the quality of image classification models. Algorithms in the field of hyper-parameter optimization and neural architecture search could be applied to mining optimal kernels, while hyper-parameter optimization algorithms always require multiple training sessions to evaluate hyper-parameter configurations, resulting in a high time consumption. Recent gradient-based architecture search algorithms could deal with such problems within a single training session. However, there are currently no gradient-based NAS algorithms that could be used to efficiently optimize convolution kernels. This paper proposes a novel gradient-based algorithm, SoftStep, which could precisely and efficiently fine-tune the hyper-parameters of kernels. The proposed SoftStep is applied for the optimization of the convolution kernel’s key hyper-parameters and generalized to more hyper-parameters in deep learning. In experiments, architecture search tasks on multiple challenging datasets are compared to various state-of-art hyper-parameter optimization and architecture search algorithms. The reported results show that SoftStep could mine optimal kernels far more efficiently than other SOTA methods.

Temporal-spatial convolutional residual network for decoding attempted movement related EEG signals of subjects with spinal cord injury

Brain Computer Interface (BCI) offers a promising approach to restoring hand functionality for people with cervical spinal cord injury (SCI). A reliable classification of brain activities based on appropriate flexibility in feature extraction could enhance BCI systems performance. In the present study, based on convolutional layers with temporal-spatial, Separable and Depthwise structures, we develop Temporal-Spatial Convolutional Residual Network)TSCR-Net(and Temporal-Spatial Convolutional Iterative Residual Network)TSCIR-Net(structures to classify electroencephalogram (EEG) signals. Using EEG signals in five different hand movement classes of SCI people, we compare the effectiveness of TSCIR-Net and TSCR-Net models with some competitive methods. We use the bayesian hyperparameter optimization algorithm to tune the hyperparameters of compact convolutional neural networks. In order to show the high generalizability of the proposed models, we compare the results of the models in different frequency ranges. Our proposed models decoded distinctive characteristics of different movement efforts and obtained higher classification accuracy than previous deep neural networks. Our findings indicate that TSCIR-Net and TSCR-Net models fulfills a better classification accuracy of 71.11%, and 64.55% for EEG_All and 57.74%, and 67.87% for EEG_Low frequency data sets than the compared methods in the literature.

Raman spectral pattern recognition of breast cancer: A machine learning strategy based on feature fusion and adaptive hyperparameter optimization

Raman spectroscopy, as a kind of molecular vibration spectroscopy, provides abundant information for measuring components and molecular structure in the early detection and diagnosis of breast cancer. Currently, portable Raman spectrometers have simplified and made equipment application more affordable, albeit at the cost of sacrificing the signal-to-noise ratio (SNR). Consequently, this necessitates a higher recognition rate from pattern recognition algorithms. Our study employs a feature fusion strategy to reduce the dimensionality of high-dimensional Raman spectra and enhance the discriminative information between normal tissues and tumors. In the conducted random experiment, the classifier achieved a performance of over 96% for all three average metrics: accuracy, sensitivity, and specificity. Additionally, we propose a multi-parameter serial encoding evolutionary algorithm (MSEA) and integrate it into the Adaptive Local Hyperplane K-nearest Neighbor classification algorithm (ALHK) for adaptive hyperparameter optimization. The implementation of serial encoding tackles the predicament of parallel optimization in multi-hyperparameter vector problems. To bolster the convergence of the optimization algorithm towards a global optimal solution, an exponential viability function is devised for nonlinear processing. Moreover, an improved elitist strategy is employed for individual selection, effectively eliminating the influence of probability factors on the robustness of the optimization algorithm. This study further optimizes the hyperparameter space through sensitivity analysis of hyperparameters and cross-validation experiments, leading to superior performance compared to the ALHK algorithm with manual hyperparameter configuration.