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Intrusion detection systems (IDSs) in wireless sensor networks (WSNs) rely heavily on effective feature selection (FS) for enhanced efficacy. This study proposes a novel approach called Genetic Sacrificial Whale Optimization (GSWO) to address the limitations of conventional methods. GSWO combines a genetic algorithm (GA) and whale optimization algorithms (WOA) modified by applying a new three-population division strategy with a proposed conditional inherited choice (CIC) to overcome premature convergence in WOA. The proposed approach achieves a balance between exploration and exploitation and enhances global search abilities. Additionally, the CatBoost model is employed for classification, effectively handling categorical data with complex patterns. A new technique for fine-tuning CatBoost's hyperparameters is introduced, using effective quantization and the GSWO strategy. Extensive experimentation on various datasets demonstrates the superiority of GSWO-CatBoost, achieving higher accuracy rates on the WSN-DS, WSNBFSF, NSL-KDD, and CICIDS2017 datasets than the existing approaches. The comprehensive evaluations highlight the real-time applicability and accuracy of the proposed method across diverse data sources, including specialized WSN datasets and established benchmarks. Specifically, our GSWO-CatBoost method has an inference time nearly 100 times faster than deep learning methods while achieving high accuracy rates of 99.65%, 99.99%, 99.76%, and 99.74% for WSN-DS, WSNBFSF, NSL-KDD, and CICIDS2017, respectively. [ABSTRACT FROM AUTHOR]

Data categorization is a top concern in medical data to predict and detect illnesses; thus, it is applied in modern healthcare informatics. In modern informatics, machine learning and deep learning models have enjoyed great attention for categorizing medical data and improving illness detection. However, the existing techniques, such as features with high dimensionality, computational complexity, and long-term execution duration, raise fundamental problems. This study presents a novel classification model employing metaheuristic methods to maximize efficient positives on Chronic Kidney Disease diagnosis. The medical data is initially massively pre-processed, where the data is purified with various mechanisms, including missing values resolution, data transformation, and the employment of normalization procedures. The focus of such processes is to leverage the handling of the missing values and prepare the data for deep analysis. We adopt the Binary Grey Wolf Optimization method, a reliable subset selection feature using metaheuristics. This operation is aimed at improving illness prediction accuracy. In the classification step, the model adopts the Extreme Learning Machine with hidden nodes through data optimization to predict the presence of CKD. The complete classifier evaluation employs established measures, including recall, specificity, kappa, F-score, and accuracy, in addition to the feature selection. Data related to the study show that the proposed approach records high levels of accuracy, which is better than the existing models. [ABSTRACT FROM AUTHOR]

Summary: To ensure smooth power exchange in a three‐phase inverter, it is important to accurately detect the faults of insulated gate bipolar transistor (IGBT) switches. However, the fault characteristics of power tubes are complex, the fault diagnosis results are affected by the load, and the recognition accuracy of some fault diagnosis models is low. To tackle this issue, this paper proposes an inverter open circuit fault diagnosis method named SR‐WOA‐ELM, which integrates signal reconstruction (SR), the whale optimization algorithm (WOA), and an extreme learning machine (ELM). First, the three‐phase output current is processed by the signal reconstruction method to eliminate the effect of load fluctuation, the average value of low frequency coefficients and wavelet entropy is extracted from the reconstructed three‐phase currents using the improved wavelet packet transform, and the fault feature vector is constructed by fusing the above two feature parameters. Second, the parameters of the ELM fault diagnosis model are optimized using WOA. Finally, the fault feature vector is added in the SR‐WOA‐ELM model to obtain the fault diagnosis results. Through simulation and experimental verification, the model can accurately identify the single‐tube and double‐tube faults of the inverter and is not affected by load fluctuations. [ABSTRACT FROM AUTHOR]

This paper introduces a novel and comprehensive model for the analysis of dam deformation trends, integrating the variational mode decomposition (VMD) method, fractal theory, and the whale optimization algorithm (WOA) to re6ne the deep extreme learning machine (DELM) model. This integration allows for a meticulous denoising process through VMD, e7ectively isolating pertinent signal characteristics from noise and measurement interference. Following this, fractal theory is utilized to conduct an in-depth qualitative analysis of the denoised data, capturing intricate patterns within the deformation trends. The model further evolves with the application of WOA to optimize the DELM model, thereby facilitating an integrated approach that merges qualitative insights with quantitative analysis. The efficacy of this advanced model is demonstrated through a case study, highlighting its capability to deliver accurate and reliable predictions that are in harmony with practical engineering scenarios. This research not only o7ers a robust framework for analyzing dam deformation trends but also sets a new standard in the 6eld, providing a new solution for assessing structural integrity in hydrological engineering. [ABSTRACT FROM AUTHOR]

Machine learning classifiers have been widely used for crop mapping; however, employing numerous features can reduce classifier accuracy. Feature selection is a widely recognized technique that addresses the issue of high dimensionality by choosing the most relevant subset of features with the highest degree of relevance and practicality. Therefore, the optimal combination of feature selection methods and classifiers for high-precision crop mapping remains an open problem. In this study, we introduce a novel hybrid feature selection approach to obtain the optimal subset of features for effective crop mapping. Initially, spectro-temporal remote sensing features from the dataset were ranked using two distinct feature selection techniques: Mutual Information (MI) and ReliefF filter. The most relevant features from each filter-based approach were combined into a union subset. Subsequently, the Gray Wolf Optimization (GWO), a metaheuristic optimization technique, was applied to enhance the feature set generated in the initial step. In the final stage, a random forest classifier leveraged the optimized feature subset for accurate crop type prediction. The effectiveness of the proposed approach was evaluated in Behiera governorate, Egypt. Comparative analysis against existing crop mapping methods demonstrated the superior performance of the proposed approach, achieving an accuracy rate of 82%. Furthermore, this approach showcased a significant reduction in the feature count, streamlining the set from 308 down to only 26 features. [ABSTRACT FROM AUTHOR]

Summary: This article introduces Crystal Structure Algorithm (CryStAl), a novel metaheuristic optimization algorithm, for fine‐tuning microstrip patch antennas (MPA) in C‐band applications, which utilizes CryStAl with dielectric constant and resonant frequency inputs to optimize MPA dimensions, addressing limited bandwidth and gain issues. Achieves superior antenna performance with 32.71 dB return loss, 5.09 dB gain, and 1.2 VSWR, demonstrating improved convergence speed, radiation pattern, and suitability for diverse C‐band conditions. [ABSTRACT FROM AUTHOR]

This study introduces a new human-based meta-heuristic algorithm, the Learning Cooking Algorithm (LCA), based on the opposition-based learning (OBL) strategy, namely the Opposition-based Learning Cooking Algorithm (OLCA). The proposed OLCA algorithm consists of four stages: the first stage, where the OBL strategy is implemented to improve the initial population; the second stage, where children learn from their respective mothers; the third stage, where children and mothers learn from chefs; and the fourth stage, where OBL is applied again to update the population. The proposed OLCA has been examined over 23 test functions, and the OLCA outcomes are equated with several popular and top-performing optimization algorithms. The statistical outcomes, such as the average (Ave), standard deviation (Std), Wilcoxon rank-sum test, and t -test, reveal that the outcomes of OLCA may effectively address optimization problems by maintaining a proper balance between exploitation and exploration. Furthermore, the proposed OLCA has been employed to solve three real-world engineering problems, such as the tension/compression spring problem, the gear train problem, and the three-bar truss problem. The results demonstrate the OLCA's superiority and capability over other algorithms in solving engineering problems. [ABSTRACT FROM AUTHOR]

One of the significant challenges in scaling agile software development is organizing software development teams to ensure effective communication among members while equipping them with the capabilities to deliver business value independently. A formal approach to address this challenge involves modeling it as an optimization problem: given a professional staff, how can they be organized to optimize the number of communication channels, considering both intra-team and inter-team channels? In this article, we propose applying a set of bio-inspired algorithms to solve this problem. We introduce an enhancement that incorporates ensemble learning into the resolution process to achieve nearly optimal results. Ensemble learning integrates multiple machine-learning strategies with diverse characteristics to boost optimizer performance. Furthermore, the studied metaheuristics offer an excellent opportunity to explore their linear convergence, contingent on the exploration and exploitation phases. The results produce more precise definitions for team sizes, aligning with industry standards. Our approach demonstrates superior performance compared to the traditional versions of these algorithms. [ABSTRACT FROM AUTHOR]

Bathymetry data is indispensable for a variety of aquatic field studies and benthic resource inventories. Determining water depth can be accomplished through an echo sounding system or remote estimation utilizing space-borne and air-borne data across diverse environments, such as lakes, rivers, seas, or lagoons. Despite being a common option for bathymetry mapping, the use of satellite imagery faces challenges due to the complex inherent optical properties of water bodies (e.g., turbid water), satellite spatial resolution limitations, and constraints in the performance of retrieval models. This study focuses on advancing the remote sensing based method by harnessing the non-linear learning capabilities of the machine learning (ML) model, employing advanced feature selection through a meta-heuristic algorithm, and using image extraction techniques (i.e., band ratio, gray scale morphological operation, and morphological multi-scale decomposition). Herein, we validate the predictive capabilities of six ML models: Random Forest (RF), Support Vector Machine (SVM), CatBoost (CB), Extreme Gradient Boost (XGB), Light Gradient Boosting Machine (LGBM), and KTBoost (KTB) models, both with and without the application of meta-heuristic optimization (i.e., Dragon Fly, Particle Swarm Optimization, and Grey Wolf Optimization), to accurately ascertain water depth. This is achieved using a diverse input dataset derived from multi-spectral Landsat 9 imagery captured on a cloud-free day (19 September 2023) in a shallow, turbid lagoon. Our findings indicate the superior performance of LGBM coupled with Particle Swamp Optimization (R2 = 0.908, RMSE = 0.31 m), affirming the consistency and reliability of the feature extraction and selection-based framework, while offering novel insights into the expansion of bathymetric mapping in complex aquatic environments. [ABSTRACT FROM AUTHOR]

Analyzing the stability of footings is a significant step in civil/geotechnical engineering projects. In this work, two novel predictive tools are suggested based on an artificial neural network (ANN) to analyze the bearing capacity of a footing installed on a two-layered soil mass. To this end, backtracking search algorithm (BSA) and equilibrium optimizer (EO) are employed to train the ANN for approximating the stability value (SV) of the system. After executing a set of finite element analyses, the settlement values lower/higher than 5 cm are considered to indicate the stability/failure of the system. The results demonstrated the efficiency of these algorithms in fulfilling the assigned task. In detail, the training error of the ANN (in terms of root mean square error—RMSE)) dropped from 0.3585 to 0.3165 (11.72%) and 0.2959 (17.46%) by applying the BSA and EO, respectively. Moreover, the prediction accuracy of the ANN climbed from 93.7 to 94.3% and 94.1% (in terms of area under the receiving operating characteristics curve—AUROC). A comparison between the elite complexities of these algorithms showed that the EO enjoys a larger accuracy, while BSA is a more time-effective optimizer. Lastly, an explicit mathematical formula is derived from the EO-ANN model to be conveniently used in predicting the SV. [ABSTRACT FROM AUTHOR]

Debris flow is a sudden natural disaster in mountainous areas, which seriously threatens the lives and property of nearby residents. Therefore, it is necessary to predict the volume of debris flow accurately and reliably. However, the predictions of back propagation neural networks are unstable and inaccurate due to the limited dataset. In this study, the Cubic map optimizes the initial population position of the whale optimization algorithm. Meanwhile, the adaptive weight adjustment strategy optimizes the weight value in the shrink-wrapping mechanism of the whale optimization algorithm. Then, the improved whale optimization algorithm optimizes the final weights and thresholds in the back propagation neural network. Finally, to verify the performance of the final model, sixty debris flow gullies caused by earthquakes in Longmenshan area are selected as the research objects. Through correlation analysis, 4 main factors affecting the volume of debris flow are determined and inputted into the model for training and prediction. Four methods (support vector machine regression, XGBoost, back propagation neural network optimized by artificial bee colony algorithm, back propagation neural network optimized by grey wolf optimization algorithm) are used to compare the prediction performance and reliability. The results indicate that loose sediments from co-seismic landslides are the most important factor influencing the flow of debris flows in the earthquake area. The mean absolute percentage error, mean absolute error and R2 of the final model are 0.193, 29.197 × 104 m3 and 0.912, respectively. The final model is more accurate and stable when the dataset is insufficient and under complexity. This is attributed to the optimization of WOA by Cubic map and adaptive weight adjustment. In general, the model of this paper can provide reference for debris flow prevention and machine learning algorithms. [ABSTRACT FROM AUTHOR]

Software development effort estimation (SDEE) is recognized as vital activity for effective project management since under or over estimating can lead to unsuccessful utilization of project resources. Machine learning (ML) algorithms are largely contributing in SDEE domain, particularly ensemble effort estimation (EEE) works well in rectifying bias and subjectivity to solo ML learners. Performance of EEE significantly depends on hyperparameter composition as well as weight assignment mechanism of solo learners. However, in EEE domain, impact of optimization in terms of hyperparameter tunning as well as weight assignment is explored by few researchers. This study aims in improving SDEE performance by incorporating metaheuristic hyperparameter and weight optimization in EEE, which enables accuracy and diversity to the ensemble model. The study proposed Metaheuristic-optimized Multi-dimensional bagging scheme and Weighted Ensemble (MoMdbWE) approach. This is achieved by proposed search space division and hyperparameter optimization method named as Multi-dimensional bagging (Mdb). Metaheuristic algorithm considered for this work is Firefly algorithm (FFA), to get best hyperparameters of three base ML algorithms (Random Forest, Support vector machine and Deep Neural network) since FFA has shown promising results of fitness in terms of MAE. Further enhancement in performance is achieved by incorporating FFA-based weight optimization to construct Metaheuristic-optimized weighted ensemble (MoWE) of individual multi-dimensional bagging schemes. Proposed scheme is implemented on eight frequently utilized effort estimation datasets and results are evaluated by 5 error metrices (MAE, RMSE, MMRE, MdMRE, Pred), standard accuracy and effect size along with Wilcox statistical test. Findings confirmed that the use of FFA optimization for hyperparameter (with search space sub-division) and for ensemble weights, has significantly enhanced performance in comparison with individual base algorithms as well as other homogeneous and heterogenous EEE techniques. [ABSTRACT FROM AUTHOR]

Built by machine‐learning, the surrogate model of metasurfaces reduces the need for a huge number of simulations in the design process, enhancing the efficiency and performance of the designed meta‐devices. However, the surrogate model of metasurfaces is often constructed‐based on specific physical perspectives or experiences, which limits its versatility. In this study, a generalized surrogate meta‐atom model for metasurfaces is introduced. This model can simulate arbitrary meta‐atoms and their corresponding electromagnetic responses at any polarization within the full space of pixelated unit cells. Utilizing a genetic algorithm, the model is employed to design various types of meta‐devices, automatically generating configurations of meta‐atoms with optimal performance for specific application scenarios. Three typical meta‐devices, including the reflective linear‐circular polarization converter, the metasurface‐based absorber, and the asymmetrical transmission meta‐slab, are designed and validated through full‐wave simulations and/or experiments. This work presents an efficient and flexible approach to model arbitrary metasurfaces, opening new possibilities for metasurface design and applications. [ABSTRACT FROM AUTHOR]

Accurately estimating evaporation is essential for water managers to formulate effective rules and policies. The complexity arising from intricate interactions within the soil-atmosphere system makes evaporation a challenging parameter to predict. In the past decade, Machine Learning techniques rooted in soft computing have emerged as potent tools for addressing the intricacies and non-linearities in hydrology. Surprisingly, there has been no prior research exploring the impact of reservoir water temperature on evaporation modeling. Consequently, this study aimed to employ a hybridized Adaptive Neuro-Fuzzy Inference System (ANFIS) integrated with four optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO), and Salp Swarm Algorithm (SSA). The focus was on modeling the monthly evaporation of the Boukourdane Dam in Algeria for the period of September 1996 to August 2016 and examining how the reservoir's water temperature influences model performance based on four performance indicators, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), scatter index (SI) and Correlation Coefficient (R). The findings underscored the significance of incorporating the water temperature parameter. Among the models, ANFIS-HHO demonstrated the highest accuracy with MAE, RMSE and R values of 1.28, 1.21 mm and 0.92, respectively during test period. Moreover, results revealing a notable impact of reservoir water temperature on evaporation forecasting and the addition of this parameter provide an increase in R and decrease in RMSE about 4.54% and 17.98% respectively. [ABSTRACT FROM AUTHOR]

Groundwater resources are required for domestic water supply, agriculture, and industry, and the strategic importance of water resources will only increase in the context of climate change and population growth. For optimal management of this crucial resource, exploration of the potential of groundwater is necessary. To this end, the objective of this study was the development of a new method based on remote sensing, deep neural networks (DNNs), and the optimization algorithms Adam, Flower Pollination Algorithm (FPA), Artificial Ecosystem-based Optimization (AEO), Pathfinder Algorithm (PFA), African Vultures Optimization Algorithm (AVOA), and Whale Optimization Algorithm (WOA) to predict groundwater potential in the North Central region of Vietnam. 95 springs or wells with 13 conditioning factors were used as input data to the machine learning model to find the statistical relationships between the presence and nonpresence of groundwater and the conditioning factors. Statistical indices, namely root mean square error (RMSE), area under curve (AUC), accuracy, kappa (K) and coefficient of determination (R2), were used to validate the models. The results indicated that all the proposed models were effective in predicting groundwater potential, with AUC values of more than 0.95. Among the proposed models, the DNN-AVOA model was more effective than the other models, with an AUC value of 0.97 and an RMSE of 0.22. This was followed by DNN-PFA (AUC=0.97, RMSE=0.22), DNN-FPA (AUC=0.97, RMSE=0.24), DNN-AEO (AUC=0.96, RMSE=0.25), DNN-Adam (AUC=0.97, RMSE=0.28), and DNN-WOA (AUC=0.95, RMSE=0.3). In addition, according to the groundwater potential map, about 25–30% of the region was in the high and very high potential groundwater zone; 5–10% was in the moderate zone, and 60–70% was low or very low. The results of this study can be used in the management of water resources in general and the location of appropriate wells in particular. [ABSTRACT FROM AUTHOR]

This study investigated the compressibility of clay (Cc) for soft ground improvement and developed six optimized metaheuristic-based extreme learning machine (ELM) models (particle swarm optimization (PSO)-ELM, moth search optimization (MSO)-ELM, firefly optimization (FO)-ELM, cuckoo search optimization (CSO)-ELM, bees optimization (BO)-ELM, and ant colony optimization (ACO)-ELM) to predict Cc. A total of 739 laboratory tests were conducted to develop the models, and 517 datasets were used for training, while the remaining 222 samples were used for testing. The results showed that the accuracy of the developed models was improved by 3–5% compared to the original ELM model. The BO-ELM and MSO-ELM models were identified as the most effective models for predicting Cc, with accuracies ranging from 86.5% to 87%. The study suggests that the MSO-ELM model should be used if training time is critical. The developed models provide useful tools for predicting Cc, an essential parameter for soft ground improvement design, and can assist in the improvement of soft ground. [ABSTRACT FROM AUTHOR]

To solve the problems of high computational cost and the long time required by the simulation and calculation of aeroengines' exhaust systems, a method of predicting the characteristics of infrared radiation based on the hybrid kernel extreme learning machine (HKELM) optimized by the improved dung beetle optimizer (IDBO) was proposed. Firstly, the Levy flight strategy and variable spiral strategy were introduced to improve the optimization performance of the dung beetle optimizer (DBO) algorithm. Secondly, the superiority of IDBO algorithm was verified by using 23 benchmark functions. In addition, the Wilcoxon signed-rank test was applied to evaluate the experimental results, which proved the superiority of the IDBO algorithm over other current prominent metaheuristic algorithms. Finally, the hyperparameters of HKELM were optimized by the IDBO algorithm, and the IDBO-HKELM model was applied to the prediction of characteristics of infrared radiation of a typical axisymmetric nozzle. The results showed that the RMSE and MAE of the IDBO-HKELM model were 20.64 and 8.83, respectively, which verified the high accuracy and feasibility of the proposed method for predictions of aeroengines' infrared radiation characteristics. [ABSTRACT FROM AUTHOR]

To address the issue of diagnosing hard and soft faults in active power factor correction (APFC) power supply, this study analyzes failure modes resulting from aging and malfunction of various sensitive components. The power fault waveform patterns are initially analyzed based on the circuit's THD, current ripple value, and RMS value. The inductor current signals in different fault modes are then utilized to extract and construct time–frequency fusion fault features of the APFC power supply. Finally, these feature quantities are downscaled and optimized using the RF algorithm. The SOA‐KELM model of the APFC converter is proposed, and the feature vectors under different fault modes are used to classify and diagnose faults, achieving hard and soft fault detection of the converter. The experiments show that the method achieves 100% accuracy for hard fault diagnosis and 96.36% accuracy for soft fault diagnosis of the converter, demonstrating high diagnostic accuracy. [ABSTRACT FROM AUTHOR]

In numerous scientific disciplines and practical applications, addressing optimization challenges is a common imperative. Nature-inspired optimization algorithms represent a highly valuable and pragmatic approach to tackling these complexities. This paper introduces Dendritic Growth Optimization (DGO), a novel algorithm inspired by natural branching patterns. DGO offers a novel solution for intricate optimization problems and demonstrates its efficiency in exploring diverse solution spaces. The algorithm has been extensively tested with a suite of machine learning algorithms, deep learning algorithms, and metaheuristic algorithms, and the results, both before and after optimization, unequivocally support the proposed algorithm's feasibility, effectiveness, and generalizability. Through empirical validation using established datasets like diabetes and breast cancer, the algorithm consistently enhances model performance across various domains. Beyond its working and experimental analysis, DGO's wide-ranging applications in machine learning, logistics, and engineering for solving real-world problems have been highlighted. The study also considers the challenges and practical implications of implementing DGO in multiple scenarios. As optimization remains crucial in research and industry, DGO emerges as a promising avenue for innovation and problem solving. [ABSTRACT FROM AUTHOR]

Document classification is becoming increasingly essential for the vast number of documents available in digital libraries, emails, the Internet, etc. Textual records frequently contain non-discriminative (noisy and irrelevant) terms that are also high-dimensional, resulting in higher computing costs and poorer learning performance in Text Classification (TC). Feature selection (FS), which tries to discover discriminate terms or features from the textual data, is one of the most effective tasks for this issue. This paper introduces a novel multi-stage term-weighting scheme-based FS model designed for the single-label TC system to obtain the optimal set of features. We have also developed a hybrid deep learning fine-tuning network based on Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) for the classification stage. The FS approach is worked on two-stage criteria. The filter model is used in the first stage, and the multi-objective wrapper model, an upgraded version of the Whale Optimization Algorithm (WOA) with Particle Swarm Optimization (PSO), is used in the second stage. The objective function in the above wrapper model is based on a tri-objective principle. It uses the Pareto front technique to discover the optimal set of features. Here in the wrapper model, a novel selection strategy has been introduced to select the whale instead of the random whale. The proposed work is evaluated on four popular benchmark text corpora, of which two are binary class, and two are multi-class. The suggested FS technique is compared against classic Machine Learning (ML) and deep learning classifiers. The results of the experiments reveal that the recommended FS technique is more effective in obtaining better results than the other results. [ABSTRACT FROM AUTHOR]

In order to further improve the accuracy of photovoltaic (PV) power prediction and the stability of power system, a short-term PV power prediction model based on hierarchical clustering of K-means++ algorithm and deep learning hybrid model is proposed in this paper. First, hierarchical clustering of the K-means++ algorithm is used to cluster historical data into different weather scenes according to different seasons. Second, a hybrid model combining convolutional neural network (CNN), squeeze-and-excitation attention mechanism (SEAM), and bidirectional long short-term memory (BILSTM) neural network is constructed to capture long-term dependencies in time series, and the improved pelican optimization algorithm (IPOA) is used to optimize the hyperparameters in the prediction model. Finally, an example for modeling analysis is conducted by using the actual output and meteorological data of a PV power station in the Ili region of Xinjiang, China. The effectiveness and accuracy of the proposed model are verified by comparing with LSTM, BILSTM, CNN-BILSTM, and POA-CNN-SEAM-BILSTM models, and the superiority of IPOA is verified by comparing with particle swarm optimization and whale optimization algorithm. The results show that the proposed model can obtain better results under different weather scenes in different seasons, and the prediction accuracy of the model optimized by IPOA is further improved. [ABSTRACT FROM AUTHOR]

The primary aim of this study was to optimize 3D-printed sandwich beams incorporating auxetic cores. Four key design variables were considered: core thickness, width, and two specific geometric parameters related to the auxetic core–the cell's wall thickness-to-length ratio and the cell angle. Initially, sixteen sandwich samples, each varying in geometric parameters, were printed following Taguchi's experimental design. These samples underwent a three-point bending test, and the resulting data were used to train a deep neural network (DNN). The trained DNN proficiently predicted the bending response of the structures. Another network, using a similar architecture, was trained to precisely predict the structure's mass. These trained networks were then integrated into the imperialist competitive algorithm (ICA) to optimize the sandwich beams design for minimal mass. A contour plot was subsequently generated to visually illustrate the minimum required mass distribution for sandwich beams under various conditions and constraints, considering applied bending forces and maximum allowable deflection. The optimization results underscored the importance of fabricating and testing an adequate number of sandwich samples, aligning with the number of design parameters. The results provide a valuable reference for future research on designing advanced 3D-printed lightweight structures, including integrating machine learning techniques and metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

This study addresses the critical challenge of accurately classifying brain tumors using artificial intelligence. Early detection is crucial, as untreated tumors can be fatal. Despite advances in AI, accurately classifying tumors remains a challenging task. To address this challenge, we propose a novel optimization approach called PSCS combined with deep learning for brain tumor classification. PSCS optimizes the classification process by improving Particle Swarm Optimization (PSO) exploitation using Cuckoo search (CS) algorithm. Next, classified gene expression data of brain tumor using Deep Learning (DL) to identify different groups or classes related to a particular tumor along with the PSCS optimization technique. The proposed optimization technique with DL achieves much better classification accuracy than other existing DL and Machine learning models with the different evaluation matrices such as Recall, Precision, F1‐Score, and confusion matrix. This research contributes to AI‐driven brain tumor diagnosis and classification, offering a promising solution for improved patient outcomes. [ABSTRACT FROM AUTHOR]

The precise evaluation of predicting software development effort is a serious process in software engineering. The underestimates require more time, which seems to be the negotiation of complete efficient design and complete software testing. Thus, this paper devises a novel model for inspecting the effort taken for the design of software. The Mayfly Lion Optimization (MFLion) algorithm is devised, which ensembles the Mayfly Algorithm (MA) with the Lion Optimization Algorithm (LOA) for precise estimation. The deep maxout network (DMN) is adapted wherein the different weights are produced out of which the most suitable is infused considering the MFLion during model training. The optimal features are selected using recursive feature elimination (RFE) wherein the best features are selected and the remaining irrelevant features are eliminated from the list. The proposed MFLion obtained better performance with the smallest mean magnitude of relative error (MMRE) of 5.909 and the smallest root mean square error (RMSE) of 75.505, respectively. Each technique is produced using a separate database generated using the Promise software engineering repository. The outcomes produced from the assessment of the accuracies of models suggested that the MFLion‐DMN is a substitute to forecast software effort, which is extensively devised in engineering platforms. [ABSTRACT FROM AUTHOR]

The purpose of the present study is to predict and draw up non-grain cultivated land (NCL) susceptibility map based on optimized Extreme Gradient Boosting (XGBoost) model using the Particle Swarm Optimization (PSO) metaheuristic algorithm. In order to, a total of 184 NCL areas were identified based on historical records, and a total of 16 NCL susceptibility conditioning factors (NCLSCFs) were considered, based on both a systematic literature survey and local environmental conditions. The results showed that the XGBoost model optimized by PSO performed well in comparison to other machine learning algorithms; the values of sensitivity, specificity, PPV, NPV, and AUC are 0.93, 0.89, 0.88, 0.93, and 0.96, respectively. Slope, rainfall, fault density, distance from fault and drainage density are most important variables. According to the results of this study, the use of meta-innovative algorithms such as PSO can greatly enhance the ability of machine learning models. [ABSTRACT FROM AUTHOR]

This study provides a comprehensive analysis of the combination of Genetic Algorithms (GA) and XGBoost, a well-known machine-learning model. The primary emphasis lies in hyperparameter optimization for fraud detection in smart grid applications. The empirical findings demonstrate a noteworthy enhancement in the model's performance metrics following optimization, particularly emphasizing a substantial increase in accuracy from 0.82 to 0.978. The precision, recall, and AUROC metrics demonstrate a clear improvement, indicating the effectiveness of optimizing the XGBoost model for fraud detection. The findings from our study significantly contribute to the expanding field of smart grid fraud detection. These results emphasize the potential uses of advanced metaheuristic algorithms to optimize complex machine-learning models. This work showcases significant progress in enhancing the accuracy and efficiency of fraud detection systems in smart grids. [ABSTRACT FROM AUTHOR]

Accurate fault detection in high-voltage direct current (HVDC) transmission lines plays a pivotal role in enhancing operational efficiency, reducing costs, and ensuring grid reliability. This research aims to develop a cost-effective and high-performance fault detection solution for HVDC systems. The primary objective is to accurately identify and localize faults within the power system. In pursuit of this goal, the paper presents a comparative analysis of current and voltage characteristics between the rectifier and inverter sides of the HVDC transmission system and their associated alternating current (AC) counterparts under various fault conditions. Voltage and current features are extracted and optimized using a metaheuristic approach, specifically Harris Hawk's optimization method. Leveraging machine learning (ML) and artificial neural networks (ANN), this technique demonstrates its effectiveness in generating a fault locator with exceptional accuracy. With a substantial volume of data employed for learning and training, the Harris Hawks optimization method exhibits faster convergence compared to other metaheuristic methods examined in this study. The research findings are applied to simulate diverse fault types and unknown fault locations at multiple system points. Evaluating the fault detection system's effectiveness, quantified through metrics such as specificity, accuracy, F1 score, and sensitivity, yields remarkable results, with percentages of 99.01%, 98.69%, 98.64%, and 98.67%, respectively. This research underscores the critical role of accurate fault detection in HVDC systems, offering valuable insights into optimizing grid performance and reliability. [ABSTRACT FROM AUTHOR]

Population-based metaheuristics can be seen as a set of agents that smartly explore the space of solutions of a given optimization problem. These agents are commonly governed by movement operators that decide how the exploration is driven. Although metaheuristics have successfully been used for more than 20 years, performing rapid and high-quality parameter control is still a main concern. For instance, deciding the proper population size yielding a good balance between quality of results and computing time is constantly a hard task, even more so in the presence of an unexplored optimization problem. In this paper, we propose a self-adaptive strategy based on the on-line population balance, which aims for improvements in the performance and search process on population-based algorithms. The design behind the proposed approach relies on three different components. Firstly, an optimization-based component which defines all metaheuristic tasks related to carry out the resolution of the optimization problems. Secondly, a learning-based component focused on transforming dynamic data into knowledge in order to influence the search in the solution space. Thirdly, a probabilistic-based selector component is designed to dynamically adjust the population. We illustrate an extensive experimental process on large instance sets from three well-known discrete optimization problems: Manufacturing Cell Design Problem, Set covering Problem, and Multidimensional Knapsack Problem. The proposed approach is able to compete against classic, autonomous, as well as IRace-tuned metaheuristics, yielding interesting results and potential future work regarding dynamically adjusting the number of solutions interacting on different times within the search process. [ABSTRACT FROM AUTHOR]

This paper presents the Multiscale Medalist Learning Algorithm (MMLA) as a novel heuristic approach for complex engineering optimization problems. By extending the Medalist Learning Algorithm, MMLA offers enhanced solution efficiency. The algorithm divides the learning process into successive periods with reduced search spaces, enabling focused search efforts in promising areas. Within each period, predefined learning stages are implemented. Top performers, known as medalists, engage in self-improvement operations through neighborhood searches, while common learners either learn from medalists or adapt based on the current state using neighborhood fluctuation. The MMLA balances exploration and exploitation capabilities through a natural growth curve that determines the learning efficiency. The MMLA's effectiveness and robustness are illustrated through the solution of a two-dimensional benchmark optimization problem and the successful resolution of ten well-known engineering design optimization problems. Comparative analysis demonstrates that the MMLA consistently outperforms other algorithms, providing competitive solutions with strict feasibility and minimal variation. [ABSTRACT FROM AUTHOR]

Energy consumption forecasting is a hot field of research; despite the number of developed models, projecting electric consumption in residential buildings remains problematic owing to the significant unpredictability of occupant energy use behavior. Discovering the electricity consumption knowledge from the multi-dimensional data streams (MDDS) of electricity logs is a challenging research problem. We propose a novel electricity knowledge discovery model proposed from the MDDS using clustering and machine learning. Context-aware clustering with whale optimization algorithm (CAC-WOA) is proposed to discover the predictive features from the electricity MDDS and perform the predictions using WOA. The CAC-WOA consists of two phases context-aware group formation and a WOA-based machine learning predictive model. In the CAC algorithm, group formation using electricity contextual information to estimate the robust predictive features are proposed. Using such predictive features, the predictive model using the WOA-based artificial neural network (ANN) is built. The modified ANN technique using the WOA algorithm is used to reduce the error rates and improve the prediction accuracy. The experimental outcomes using publicly available electricity consumption datasets prove the efficiency of the CAC-WOA model. Overall prediction accuracy is improved by 3.27% and prediction time is reduced by 11.31% using CAC-WOA compared state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

Feature selection is an important component of the machine learning domain, which selects the ideal subset of characteristics relative to the target data by omitting irrelevant data. For a given number of features, there are 2n possible feature subsets, making it challenging to select the optimal set of features from a dataset via conventional feature selection approaches. We opted to investigate glaucoma infection since the number of individuals with this disease is rising quickly around the world. The goal of this study is to use the feature set (features derived from fundus images of benchmark datasets) to classify images into two classes (infected and normal) and to select the fewest features (feature selection) to achieve the best performance on various efficiency measuring metrics. In light of this, the paper implements and recommends a metaheuristics-based technique for feature selection based on emperor penguin optimization, bacterial foraging optimization, and proposes their hybrid algorithm. From the retinal fundus benchmark images, a total of 36 features were extracted. The proposed technique for selecting features minimizes the number of features while improving classification accuracy. Six machine learning classifiers classify on the basis of a smaller subset of features provided by these three optimization techniques. In addition to the execution time, eight statistically based performance metrics are calculated. The hybrid optimization technique combined with random forest achieves the highest accuracy, up to 0.95410. Because the proposed medical decision support system is effective and ensures trustworthy decision-making for glaucoma screening, it might be utilized by medical practitioners as a second opinion tool, as well as assist overworked expert ophthalmologists and prevent individuals from losing their eyesight. [ABSTRACT FROM AUTHOR]

Feature selection is becoming a relevant problem within the field of machine learning. The feature selection problem focuses on the selection of the small, necessary, and sufficient subset of features that represent the general set of features, eliminating redundant and irrelevant information. Given the importance of the topic, in recent years there has been a boom in the study of the problem, generating a large number of related investigations. Given this, this work analyzes 161 articles published between 2019 and 2023 (20 April 2023), emphasizing the formulation of the problem and performance measures, and proposing classifications for the objective functions and evaluation metrics. Furthermore, an in-depth description and analysis of metaheuristics, benchmark datasets, and practical real-world applications are presented. Finally, in light of recent advances, this review paper provides future research opportunities. [ABSTRACT FROM AUTHOR]

In the optimization field, the ability to efficiently tackle complex and high-dimensional problems remains a persistent challenge. Metaheuristic algorithms, with a particular emphasis on their autonomous variants, are emerging as promising tools to overcome this challenge. The term "autonomous" refers to these variants' ability to dynamically adjust certain parameters based on their own outcomes, without external intervention. The objective is to leverage the advantages and characteristics of an unsupervised machine learning clustering technique to configure the population parameter with autonomous behavior, and emphasize how we incorporate the characteristics of search space clustering to enhance the intensification and diversification of the metaheuristic. This allows dynamic adjustments based on its own outcomes, whether by increasing or decreasing the population in response to the need for diversification or intensification of solutions. In this manner, it aims to imbue the metaheuristic with features for a broader search of solutions that can yield superior results. This study provides an in-depth examination of autonomous metaheuristic algorithms, including Autonomous Particle Swarm Optimization, Autonomous Cuckoo Search Algorithm, and Autonomous Bat Algorithm. We submit these algorithms to a thorough evaluation against their original counterparts using high-density functions from the well-known CEC LSGO benchmark suite. Quantitative results revealed performance enhancements in the autonomous versions, with Autonomous Particle Swarm Optimization consistently outperforming its peers in achieving optimal minimum values. Autonomous Cuckoo Search Algorithm and Autonomous Bat Algorithm also demonstrated noteworthy advancements over their traditional counterparts. A salient feature of these algorithms is the continuous nature of their population, which significantly bolsters their capability to navigate complex and high-dimensional search spaces. However, like all methodologies, there were challenges in ensuring consistent performance across all test scenarios. The intrinsic adaptability and autonomous decision making embedded within these algorithms herald a new era of optimization tools suited for complex real-world challenges. In sum, this research accentuates the potential of autonomous metaheuristics in the optimization arena, laying the groundwork for their expanded application across diverse challenges and domains. We recommend further explorations and adaptations of these autonomous algorithms to fully harness their potential. [ABSTRACT FROM AUTHOR]

Accurately estimating concrete mechanical parameters using artificial intelligence-based methods can save time and energy. Existing nonlinear relationships between concrete components have entered uncertainty in the estimation of hardness properties of the slump and compressive strength as one of the most important parameters in concrete design. Employing regular approaches to use AI models individually in estimating dependent variables has been adopted in many studies. Therefore, the current study has aimed to develop predictive models in two categories of ensemble and hybrid frameworks to predict the hardness properties of high-performance concrete (HPC). In this regard, models based on Support Vector Regression, Decision Tree, and AdaBoost Machine learning were coupled with a metaheuristic optimization algorithm Chaos game optimizer (CGO). Linking three predictive models as well as tuning their internal settings via optimization algorithm could generate various types of hybrid and ensemble models. By assessing the results of the proposed models for compressive strength, the performance of ADA-CGO hybrid models was calculated higher than the ensemble model of SVR-ADA-DT, with 1.22% and 166% percent difference in terms of R2 and RMSE, respectively. Also, for predicting Slump, other hybrid models appeared with weaker performance than the ensemble model, with an average difference of 40.66% in terms of the MAE index. Generally, using advanced types of individual models, including ensemble and hybrid, indicated boosted performance accompanied by low-cost modeling processes. [ABSTRACT FROM AUTHOR]

Ransomware is a serious security concern to mobile devices, as it prevents the use of the device and its contents until a ransom is paid, resulting in considerable financial losses for both people and corporations. The existing anti‐malware measures have shown to be inadequate in combatting new malware variants that utilize advanced evasion strategies like Polymorphic, Metamorphic, Dynamic Code Loading, Time‐based evasion, and Reflection. Furthermore, these primary defences have also suffered from low detection rates, significant false positives, high processing times, and excessive processing and power consumption that is inappropriate for smartphones. This paper offers the binary JAYA (BJAYA) for ransomware detection in Android mobile devices using the BJAYA optimization‐based algorithm. The developed algorithm's effectiveness has been assessed against two datasets, the 0–1 knapsack, and real ransomware dataset. The proposed BJAYA method surpassed the other algorithms on 85% of the 0–1 knapsack datasets. The suggested BJAYA method was also tested on a ransomware dataset in two phases. In the first stage of testing, BJAYA outperformed other standard classifiers with sensitivity and Gmean values of 97% and 98.2%, respectively. In the second stage of testing, BJAYA outperformed other GA, FPA, and PSO metaheuristic algorithms in terms of specificity, sensitivity, and Gmean. These findings indicate the applicability of the suggested BJAYA algorithm for ransomware detection. [ABSTRACT FROM AUTHOR]

This paper aims to address uncertainty in practical applications that the experimental observations and measurements have nature of inhomogeneity, randomness, and imprecision. This study employs a new method for regression model prediction in an uncertain environment and presents fuzzy parameter estimation of fuzzy regression models using triangular fuzzy numbers. These estimation methods are obtained by new learning algorithms in which linear programming is used. In this study, the new algorithm is a combination of a fuzzy rule-based system, on the basis of particle swarm optimization (PSO) and ant colony optimization ACOR. In the adaptive neuro-fuzzy inference system method, the output is crisp. In this research, a hybrid algorithm has been proposed to get the fuzzy output based on linear programming. Meta-heuristic algorithms have been used to reduce the error in the proposed methods based on PSO and ACOR. In addition, two simulation and two practical examples in the field of machining process are applied to indicate the performance of the proposed methods in dealing with crisp input and fuzzy output (CIFO) problems where the observed output variables have the nature of inhomogeneity, randomness, and imprecision. Finally, the results of the proposed algorithms are evaluated. Based on examples, the proposed method is more accurate than the LP and FWLP methods but is not more complicated than the FWLP in computations. Using paired t test, a significant difference was shown between the proposed methods and previous methods, such as LP and FWLP, but there was no significant difference between the two proposed methods ACOR and PSO. [ABSTRACT FROM AUTHOR]

This document is a discussion of a research paper titled "Predicting Solid-Particle Erosion Rate of Pipelines Using Support Vector Machine with Improved Sparrow Search Algorithm." The authors of the discussion express their appreciation for the authors of the original paper for enhancing the sparrow search algorithm (SSA) through adaptive t-distribution (AT) and evaluating its performance in optimizing the support vector machine (SVM) for predicting erosion rates in pipelines. The authors compared the results of the AT-SSA-SVM with other algorithms and found that the performance of SSA was improved by incorporating AT, resulting in better predictions. The discussion also highlights some discrepancies and issues with overfitting and optimization parameters in the original paper. The data availability statement and acknowledgments are also included. [Extracted from the article]

Carbon trading is one of the pivotal means of carbon emission reduction. Accurate prediction of carbon prices can stabilize the carbon market, mitigate investment risks, and promote green development. In this study, firstly, the IVMD and ICEEMDAN are used to decompose carbon price quadratically; secondly, the Dispersion entropy is used to identify the sequence frequency, and then the SOA-LSSVM model and TCN model are used to predict the high-frequency and low-frequency sequences, respectively; finally, the prediction results are integrated by SOA-GRU. As a result, the hybrid IVMD-ICEEMDAN-SOALSSVM/TCN-SOAGRU model was constructed. This framework consistently performs best under two carbon markets, the CEEX Guangzhou and the EU ETS, compared with 21 comparative models, with MAPEs of 0.42% and 0.83%, respectively. The main contributions are as follows: (1) A novel IVMD-ICEEMDAN secondary decomposition method is proposed, which improves the problem of poorly determining the value of the decomposition modal number K in the traditional VMD method and improves the efficiency of the carbon price sequence decomposition. (2) A hybrid forecasting model of LSSVM and TCN is proposed, effectively capturing the features of different sequences. (3) Optimization for LSSVM and GRU using SOA improves the stability and adaptability of the model. The article provides governments, enterprises, and investors with novel and effective carbon price forecasting tool. • Propose a novel secondary decomposition-integration framework. • Compare 18 different hybrid frameworks and single models. • Validate that the secondary decomposition method is superior to the primary decomposition method. • Validate hybrid methods in frameworks are better than single methods. • Validate the effectiveness of the nonlinear integration and optimization method. [ABSTRACT FROM AUTHOR]

Groundwater (GW) salinization of coastal aquifers has become a serious problem for attaining sustainable water resource management in Saudi Arabia and other parts of the world. Therefore, it is crucial to assess the extent of this salinization to protect and manage our water resources effectively. This research proposed real fieldwork GW samples at several locations supported with experimental based on chromatography (IC) and inductively coupled plasma mass spectrometry (ICP-MS) to analyze several GW physical, chemical, and hydro-geochemical elements. In this study, we model GW salinization with machine learning algorithms such as support vector regression, gaussian process regression, artificial neural networks, and least squares ensemble boosting regression tree. The performance of the standalone models was optimized with metaheuristic optimization-based algorithms such as fuzzy hybridized genetic algorithm (ANFIS-GA) and particle swarm optimization (ANFIS-PSO). The outcomes based on three variable input combinations were validated using several performance indicators and graphical methods. The quantitative analysis indicated that GPR-Combo1(MAE = 0.006 mg/L), Ensm- Combo2 (MAE = 0.025 mg/L), and GPR- Combo3 (MAE = 0.078 mg/L) proved merit among the standalone combinations. Where combo 1, 2, and 3 stand for model combinations derived from feature selection. The cumulative probability function (CPF) demonstrated that heuristic optimization ANFIS-GA (MAE = 0.0025 mg/L, MAPE = 0.19183) and ANFIS-PSO (MAE = 0.0018 mg/L, MAPE = 0.0723) outperformed the standalone error accuracy and served reliable approach. Both the standalone models and heuristic algorithms used for GW salinization modeling have demonstrated promising results in accurately predicting salinity. This approach could aid in effectively managing the GW resources for sustainable development. • GW salinization addressed by field, experiment, and Al-based modelling. • Standalone hybrid techniques and heuristic algorithms were developed. • The nature-inspired optimization techniques proved reliable. • Na, Cl, Br, and Ca are the most important parameters for GW salinization modelling. • Reliable results and possible research directions were summarized for decision-makers. [ABSTRACT FROM AUTHOR]

Traditional machine learning algorithms face challenges in assessing flood susceptibility reliably due to their low robustness and the inherent 'black-box' nature. This paper utilizes five hyperparameter optimization algoirthms (HPO), namely grid search (GS), random search (RS), gauss process (GP), tree-structured parzen estimator (TPE) and simulated annealing (SA), to tune the traditional random forest's (RF) hyperparameters to improve the robustness of flood hazard mapping (FHM) models at Ningxiang City Hunan Province, China. Additionally, SHapley Additive exPlanations (SHAP) method were used to interpret the decision-mechanisms of these flood hazard models. This study considers 19 pluvial flood influencing factors and 2064 flood locations to create a geospatial database. The performance of each hybrid model was evaluated by area under the receiver operating characteristic (ROC) curve (AUC) and several validation methods. The results demonstrate that the developed hybrid models demonstrated good performance, with RF-TPE achieving the highest AUC (0.9660), followed by RF-GP (0.9648), RF-SA (0.9624), RF-GS (0.9612), RF-RS (0.9600), and RF (0.9539). The RF-TPE model exhibits superior robustness than other models, and the FHM constructed using it is more reliable. HPO is an effective approach to improve the predictive accuracy and robustness of FHM models. When considering limited computational resources, Bayesian optimization (TPE) should be prioritized for optimizing FHM models, followed by metaheuristic algorithms and model-free algorithms. Moreover, the study revealed that distance from river, peak rainfall intensity, continuous rainfall, antecedent effective rainfall, and terrain relief, are the most significant for pluvial FHM modeling in this region. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative method for achieving optimal tracking control in nonlinear continuous-time systems with input constraints. The method combines reinforcement learning and hybrid metaheuristics to enhance the controller's performance. Specifically, a hybrid metaheuristic algorithm is employed to optimize the hyperparameters of a critic neural network, which serves as the system's controller. The proposed approach is evaluated through extensive simulation studies on a nonlinear system with input constraints. Results demonstrate its superiority over traditional control techniques in terms of accuracy, timeliness, and stability. Notably, the approach effectively eliminates overshoot and steady-state error while providing precise and prompt tracking and showcasing remarkable robustness against model uncertainties. By synergistically integrating reinforcement learning and hybrid metaheuristics, this approach represents a significant advancement in enhancing the control performance of complex nonlinear systems. The simulation studies confirm superiority of the proposed approach over existing techniques, offering a promising solution for achieving optimal tracking control in nonlinear systems with input constraints. This approach holds potential for a wide range of applications, including robotics, aerospace, and manufacturing, where precise and prompt tracking control is critical. [ABSTRACT FROM AUTHOR]

Flue gas water-alternating gas (flue gas-WAG) is a promising technique for enhancing oil production and reducing greenhouse gas emissions. The effective utilization of this technique relies on identifying the optimal factors, often determined through numerous numerical simulations. This paper introduces a cost-effective optimization framework that integrates machine learning models and diverse optimization algorithms to identify the best parameters for injecting flue gas-WAG into the "Gullfaks" reservoir. Robust Machine learning models, including Multilayer Perceptron, Cascade Forward Neural Network (CFNN), Radial Basis Function, and Generalized Regression Neural Network, were employed as proxy models. The CFNN model, with satisfactory agreement with actual data (average absolute relative error of 0.4543 % for oil recovery factor (RF) and 1.9366 % for CO 2 storage), is selected for optimization through metaheuristic algorithms, including Non-dominated sorting genetic algorithm version II (NSGA-II), Pareto envelope-based selection Algorithm version-II (PESA_II), Multi-objective particle swarm optimization (MOPSO), and Multi-objective gray wolf optimization (MOGWO(. Among the optimization algorithms, MOGWO outperforms others in terms of speed and accuracy, yielding Pareto-optimal solutions with an RF of 0.8284 and a CO 2 storage of 1.5314 × 107 (kg-mol). The proposed approach uses Pareto dominance for insightful field development planning, enabling decision-makers to choose flue gas-WAG parameters based on future circumstances. [Display omitted] • A robust optimization framework is proposed for flue gas storage and enhanced oil recovery. • NSGA-II, MOPSO, MOGWO and PESA-II algorithms are utilized to optimize the water-alternating flue gas process. • CFNN, MLP, RBFNN, and GRNN are developed as proxy models that imitate the numerical simulator. • CFNN outperforms all developed models with AAPRE of 0.4543 % and 1.9633 % to estimate oil recovery factor and CO 2 storage, respectively. • The created Pareto fronts allow flexible applicability conditions to examine the design factors of the process. [ABSTRACT FROM AUTHOR]

[Display omitted] • Detection by terahertz time-domain spectroscopy combined with chemometrics. • Whale Optimization Algorithm was used to improve RF and ELM model. • Different concentrations of PDE5 Inhibitor can be detected. Combating the illicit use of PDE5 inhibitor drugs is a focal point in forensic science research. In order to achieve rapid identification of such drugs, this study applies terahertz time-domain spectroscopy combined with chemometrics to establish a fast and accurate detection method for PDE5 inhibitors. The optimal detection method is determined by comparing the spectral performance of three optical parameters, namely absorption coefficient, refractive index, and dielectric constant. Linear discriminant models based on different spectral parameters, whale optimization algorithm optimized extreme learning machine models, and whale optimization algorithm optimized random forest models are established. The effectiveness and performance of principal component analysis and competitive adaptive reweighted sampling algorithm for spectral feature data selection are also investigated. The PDE5 inhibitor identification model based on the competitive adaptive reweighted sampling - whale optimization algorithm - random forest (CARS-WOA-RF) model achieves an accuracy of 98.61%, and the identification model for two concentrations of Sildenafil achieves 100% accuracy. The results demonstrate that terahertz time-domain spectroscopy combined with chemometrics can effectively detect various common types of PDE5 inhibitor drugs and different concentrations. [ABSTRACT FROM AUTHOR]

• Data-driven metamodel developed for ZnO varistor sintering. • ML algorithms used for non-normal distribution of permittivity values. • HMA used for process parameter optimization. • Integration of Hybrid-EDT into HMA saves time and resources. • Successful validation experiment conducted under optimal conditions. A ZnO varistor functions as a circuit protection device against surge voltage because of its nonlinear current/voltage characteristics. Achieving the desired electrical properties in a ZnO varistor necessitates meticulous control over its microstructure, which is achieved by regulating parameters during the sintering process, such as the sintering temperature, sintering time, heating rate, and cooling rate. In this study, a metamodel was developed through machine learning using a dataset obtained from a laboratory-scale furnace by employing the design of experiment approach and incorporating the aforementioned four process variables and permittivity. A hybrid metaheuristic optimization algorithm was then applied to determine the optimal process conditions, maximizing permittivity through the metamodel. To adapt the derived optimal conditions to a scale-up sintering furnace, temperature data were collected from laboratory-scale and scale-up sintering furnaces through numerical simulation. The permittivity of the sintered ZnO varistor, which was predicted by inputting the corrected process variables into the metamodel, exhibited a 4.6% difference from the experimental value, representing the average permittivity of ZnO varistors sintered in the scale-up furnace. The application of this simulation-based virtual scale-up to ceramic processing can significantly reduce the development time required for scale-up processes. [ABSTRACT FROM AUTHOR]

This comprehensive review focuses on the Autonomous Driving System (ADS), which aims to reduce human errors that are the reason for about 95% of car accidents. The ADS consists of six stages: sensors, perception, localization, assessment, path planning, and control. We explain the main state-of-the-art techniques used in each stage, analyzing 275 papers, with 162 specifically on path planning due to its complexity, NP-hard optimization nature, and pivotal role in ADS. This paper categorizes path planning techniques into three primary groups: traditional (graph-based, sampling-based, gradient-based, optimization-based, interpolation curve algorithms), machine and deep learning, and meta-heuristic optimization, detailing their advantages and drawbacks. Findings show that meta-heuristic optimization methods, representing 23% of our study, are preferred for being general problem solvers capable of handling complex problems. In addition, they have faster convergence and reduced risk of local minima. Machine and deep learning techniques, accounting for 25%, are favored for their learning capabilities and fast responses to known scenarios. The trend towards hybrid algorithms (27%) combines various methods, merging each algorithm's benefits and overcoming the other's drawbacks. Moreover, adaptive parameter tuning is crucial to enhance efficiency, applicability, and balancing the search capability. This review sheds light on the future of path planning in autonomous driving systems, helping to tackle current challenges and unlock the full capabilities of autonomous vehicles. [ABSTRACT FROM AUTHOR]

• A feature selection method based on binary dandelion algorithm is proposed. • The algorithm applies a sine-cosine operator and a restart strategy. • Mutual information and quick bit mutation improve the performance of the algorithm. • The algorithm performs well on datasets with larger dimensions. Feature selection (FS) is an important data preprocessing technology for machine learning and data mining. Metaheuristic algorithm (MH) has been widely used in feature selection because of its powerful search function. This paper presents an improved Binary Dandelion Algorithm using Sine Cosine operator and Restart strategy (SCRBDA) for feature selection. First, the sine cosine operator is used in the radius formula of the core dandelions (CD), which significantly enhances the ability of algorithm development and exploration. Secondly, the algorithm uses a restart strategy to increase its ability to get rid of local optimum. Thirdly, mutual information is used to guide the generation of some dandelions, which pays more attention to the correlation between the selected features and categories. Finally, quick bit mutation is used as the mutation strategy to improve the diversity of the population. The SCRBDA proposed in this paper was tested on 18 datasets of different sizes from UCI machine learning database. The SCRBDA was compared with 8 other classical feature selection algorithms, and the performance of the proposed algorithm was evaluated through feature subset size, classification accuracy, fitness value, and F1-score. The experimental results show that SCRBDA achieves the best performance, which has stronger feature reduction ability and achieves better overall performance on most datasets. Especially on large-scale datasets, SCRBDA can obtain extremely smaller feature subsets while maintaining much higher classification accuracy, and satisfactory F1-score. [ABSTRACT FROM AUTHOR]

The proactive prediction and systematic classification of students' academic performance empower educational administrators with the invaluable capability to not only pinpoint potential challenges but also to craft targeted strategies and interventions that are tailored to the unique needs of students. This multifaceted approach enables educational institutions to proactively address issues within the education system, fostering a more equitable and effective learning environment for all, while simultaneously fostering a culture of continuous improvement and accountability in the pursuit of educational excellence. Hence, the current investigation aims to classify and predict the students' performance by examining and comparing the machine learning and artificial neural network assessments. Five methods of the Random Forest Classifier, the Decision Tree Classifier, the K Neighbors Classifier, the MLP Classifier, and the XG-Boost Classifier are used. These methods' performances are compared through the accuracy, precision, recall, and F1-score indicators. This comparison is applied to the base data and balanced data, which is carried out by the SVM-SMOTE technique. Finally, five metaheuristic algorithms are applied to the selected method to evaluate the performance indicators of the hybrid models. The results indicate that applying the SVM-SMOTE technique improves the methods' performance, in which the XG-Boost represented the best performance. As a result, the metaheuristic algorithms are applied to the XG-Boost, yielding to 9.33%, 8.44%, 9.33%, and 9.27% enhancement of the Accuracy, Precision, Recall and F1-Score values. Subsequently, the Enhanced Artificial Ecosystem-Based Optimization + XG-Boost hybrid method provides the accuracy and F1-score values of 0.9417 and 0.9413. These results underscore the potential of combining machine learning techniques with metaheuristic algorithms to enhance the accuracy and effectiveness of predicting and classifying students' performance, thus providing valuable insights for educational administrators to address issues and improve the education system. [ABSTRACT FROM AUTHOR]

[Display omitted] Feature selection (FS) is a crucial process that aims to remove unnecessary features from datasets. It plays a role in data mining and machine learning (ML) by reducing the risk associated with high-dimensional datasets. FS is considered a challenging problem that is difficult to solve efficiently due to its combinatorial nature. As the size of the problem increases, the computation time also grows. Recently, researchers have focused on metaheuristic FS algorithms specifically designed for high-dimensional datasets. Therefore, this article proposes a powerful metaheuristic algorithm called Binary Enhanced Golden Jackal Optimization (BEGJO), which is an improved version of the recently published Golden Jackal Optimization (GJO) algorithm. The original GJO algorithm faces challenges when dealing with high-dimensional FS problems, as it tends to get trapped in local optima. To address this issue, various enhancement strategies are employed to improve the efficiency of GJO. The proposed BEGJO algorithm utilizes Copula Entropy (CE) to reduce the dimensionality of high-dimensional FS problems while maintaining high classification accuracy using the K-Nearest Neighbour (K-NN) classifier. Additionally, four enhancement strategies are incorporated to enhance the exploration and exploitation capabilities of the fundamental GJO algorithm. The BEGJO algorithm is transformed into its binary form using the sigmoid transfer function, aligning it with the nature of the FS problem. It is then tested on various high-dimensional benchmark datasets. The effectiveness of BEGJO is evaluated by comparing it with well-known algorithms in terms of classification accuracy, feature dimension, and processing time. BEGJO outperforms other algorithms in terms of classification accuracy and feature dimension and ranks up to fourth in terms of processing time. Furthermore, the advantageous use of CE is demonstrated by comparing the performance of the proposed algorithm with traditional FS algorithms. Statistical evaluations are conducted to further validate the effectiveness and superiority of the proposed algorithm. The results confirm that BEGJO is an effective solution for high-dimensional FS problems. [ABSTRACT FROM AUTHOR]