Fractional Gradient Descent (FGD) methods extend classical optimization algorithms by integrating fractional calculus, leading to notable improvements in convergence speed, stability, and accuracy. However, recent stu...
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Fractional Gradient Descent (FGD) methods extend classical optimization algorithms by integrating fractional calculus, leading to notable improvements in convergence speed, stability, and accuracy. However, recent studies indicate that engineering challenges-such as tensor-based differentiation in deep neural networks-remain partially unresolved, prompting further investigation into the scalability and computational feasibility of FGD. This paper provides a comprehensive review of recent advancements in FGD techniques, focusing on their approximation methods and convergence properties. These methods are systematically categorized based on their strategies to overcome convergence challenges inherent in fractional-order calculations, such as nonlocality and long-memory effects. Key techniques examined include modified fractional-order gradients designed to avoid singularities and ensure convergence to the true extremum. Adaptive step-size strategies and variable fractional-order schemes are analyzed, balancing rapid convergence with precise parameter estimation. Additionally, the application of truncation methods is explored to mitigate oscillatory behavior associated with fractional derivatives. By synthesizing convergence analyses from multiple studies, insights are offered into the theoretical foundations of these methods, including proofs of linear convergence. Ultimately, this paper highlights the effectiveness of various FGD approaches in accelerating convergence and enhancing stability. While also acknowledging significant gaps in practical implementations for large-scale engineering tasks, including deep learning. The presented review serves as a resource for researchers and practitioners in the selection of appropriate FGD techniques for different optimization problems.
In this paper, we address the maximin optimization problem and introduce an algorithm to solve it. The core objective is to maximize a given function expressed as a minimum of the values of finite linear functions. Th...
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This study proposes a simplified mathematical formulation for optimizing isolated microgrids, enhancing computational efficiency while preserving solution quality. The research focuses on the influence of Operation an...
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This study proposes a simplified mathematical formulation for optimizing isolated microgrids, enhancing computational efficiency while preserving solution quality. The research focuses on the influence of Operation and Maintenance (O&M) costs for Non-Dispatchable Generators (NDGs) and the relationship between costs and pollutant emissions. The proposed simplification reduces computational requirements, improves result interpretability, and increases the scalability of optimization techniques. The O&M costs of photovoltaic and wind systems were excluded from the initial optimization and calculated afterward. A Student's t-test yielded a p-value of 87.3%, confirming no significant difference between the tested scenarios, ensuring that the simplification does not impact solution quality while reducing computational complexity. For emission-related costs, scenarios with single and multiple pollutant generators were analyzed. When only one generator type is present, modifications are needed to enable effective multi-objective optimization. To address this, two alternative mathematical formulations were tested, offering more suitable approaches for the problem. However, when multiple pollutant sources exist, cost and emission differences naturally define the problem as multi-objective without requiring adjustments. Future work will explore grid-connected microgrids and additional optimization objectives, such as loss minimization, voltage control, and device lifespan extension.
Machine learning and automatized routines for parameter optimization have experienced a surge in development in the past years, mostly caused by the increasing availability of computing capacity. Gradient-free optimiz...
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ISBN:
(纸本)9780791884942
Machine learning and automatized routines for parameter optimization have experienced a surge in development in the past years, mostly caused by the increasing availability of computing capacity. Gradient-free optimization can avoid cumbersome theoretical studies as input parameters are purely adapted based on output data. As no knowledge about the objective function is provided to the algorithms, this approach might reveal unconventional solutions to complex problems that were out of scope of classical solution strategies. In this study, the potential of these optimization methods on thermoacoustic problems is examined. The optimization algorithms are applied to a generic low-order thermoacoustic can-combustor model with several fuel injectors at different locations. We use three optimization algorithms the well established Downhill Simplex Method, the recently proposed Explorative Gradient Method, and an evolutionary algorithm - to find optimal fuel distributions across the fuel lines while maintaining the amount of consumed fuel constant. The objective is to have minimal pulsation amplitudes. We compare the results and efficiency of the gradient-free algorithms. Additionally, we employ model-based linear stability analysis to calculate the growth rates of the dominant thermoacoustic modes. This allows us to highlight general and thermoacoustic-specific features of the optimization methods and results. The findings of this study show the potential of gradient-free optimization methods on combustor design for tackling thermoacoustic problems, and motivate further research in this direction.
The water quality index (WQI) is a critical parameter that must be accurately predicted to ensure the sustainable management of water resources. Thus, our study develops the sine cosine optimization algorithm (SCOA)- ...
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The water quality index (WQI) is a critical parameter that must be accurately predicted to ensure the sustainable management of water resources. Thus, our study develops the sine cosine optimization algorithm (SCOA)- long short-term memory (LSTM) - Extreme gradient boosting (XGBoost), SCOA- LSTM - least square support vector machine (LSSVM), crow optimization algorithm (COA)- LSTM-XGBoost, and COA-LSTM-LSSVM models to predict WQI in Aidoghmoush river, Iran. First, COA and SCOA adjust the parameters of LSTM, LSSVM, and XGBoost. Then, LSTM captures temporal patterns in the time series data, which include water quality parameters. Finally, the LSSVM and XGBoost models use the captured patterns to make final predictions. Our results demonstrate that the SCOA-LSTM-XGBoost model achieves a Willmott's index (WI) of 0.96, an explained variance score (EVS) of 0.95, and a t-statistic (TS) of 0.021. The results of our paper show that SCOA-LSTM-XGBoost is a reliable model for predicting WQI.
Recent advancements in machine learning enable accurate prediction of concrete's mechanical properties, offering efficiency in data analysis and reducing material waste in civil engineering. What sets this study a...
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Recent advancements in machine learning enable accurate prediction of concrete's mechanical properties, offering efficiency in data analysis and reducing material waste in civil engineering. What sets this study apart from the literature is the simultaneous prediction of Compressive, Tensile, and Flexural Strength for reinforced concrete (SFRC). This integrated prediction could streamline the material selection process, enhance the durability of concrete structures, and improve performance in real-world applications such as pavements, bridges, and industrial floors. Unique variables like fiber length and content are among the input variables for predictions, which may lead to more cost-effective structural solutions. In this study, base models of the Ridge Regression and Quantile Regression were cross-validated, and the best folds of the compiled dataset for training and testing models were selected. The Sled Dog Optimizer, as a recently developed strong optimization algorithm, is utilized for fine-tuning hyperparameters of the models and enhancing prediction performance. Also, the Bayesian model combination method was employed to develop a hybrid ensemble model for reliably predicting concrete strengths by combining the capabilities of both hybrid models. Such hybrid and ensemble models are rare in the literature in this field. Also, SHAP-based explainable AI utilized for interpreting importance of variables in predictions. The hybrid ensemble model performed best in predicting Compressive Strength, achieving the lowest RMSE (3.295 MPa) and highest R2 (0.974). For Flexural and Tensile Strength, it attained R2 values of 0.986 and 0.974, respectively, with 90% and 100% of predictions showing errors below 10%. Among hybrid models, RRSD excelled, with R2 values of 0.94 for Compressive Strength and 0.98 for Flexural and Tensile Strength. These results demonstrate the reliability of machine learning for modeling SFRC properties.
The healthcare industry has been suffering from fraud in many facets for decades, resulting in millions of dollars lost to fictitious claims at the expense of other patients who cannot afford appropriate care. As such...
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The healthcare industry has been suffering from fraud in many facets for decades, resulting in millions of dollars lost to fictitious claims at the expense of other patients who cannot afford appropriate care. As such, accurately identifying fraudulent claims is one of the most important factors in a well-functioning healthcare system. However, over time, fraud has become harder to detect because of increasingly complex and sophisticated fraud scheme development, data unpreparedness, as well as data privacy concerns. Moreover, traditional methods are proving increasingly inadequate in addressing this issue. To solve this issue a novel evolutionary dynamic weighted search space approach (DW-WOA-SVM) is presented in the current study. The approach has different levels that work simultaneously, where the optimization algorithm is responsible for tuning the Support Vector Machine (SVM) parameters, applying the weighting procedure for the features, and using a dynamic search space to adjust the range values. Tuning the parameters benefits the performance of SVM, and the weighting technique makes it updated with importance and lets the algorithm focus on data structure in addition to optimization objectives. The dynamic search space enhances the search range during the process. Furthermore, large language models have been applied to generate the dataset to improve the quality of the data and address the lack of good dimensionality, helping to enhance the richness of the data. The experiments highlighted the superior performance of this proposed approach than other algorithms.
The optimal operation of complex networks and critical infrastructures requires solving various large-scale decision-making problems. These problems usually are formulated as optimization problems with several variabl...
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ISBN:
(纸本)9781665458412
The optimal operation of complex networks and critical infrastructures requires solving various large-scale decision-making problems. These problems usually are formulated as optimization problems with several variables and constraints. This leads to the high computational complexity of solving the underlying optimization problem. Hence, we require efficient methods to first model the operational objective function and constraints of the complex networks, and how they can leverage available computational resources to achieve the optimal operation of the entire system. We further need to ensure data security of decision-making entities, e.g., network flow problems, and their impact on the secure operation of the system. The proposed framework and algorithms in this paper include distributed intelligence among heterogeneous agents in a complex network represented by a graph of nodes and edges among them. Our utilized methods act as efficient computational algorithms to solve the underlying optimization problems of these networks in a computationally-efficient fashion. In order to evaluate the introduced distributed algorithm for linear-constrained optimization with a quadratic cost function, we used a random network with different numbers of nodes and edges. We illustrate the run-time and convergence of the distributed method over various networks.
The early detection of faults in power transformers is crucial for ensuring operational reliability and minimizing system disruptions. This study introduces a novel machine learning framework that integrates Particle ...
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The early detection of faults in power transformers is crucial for ensuring operational reliability and minimizing system disruptions. This study introduces a novel machine learning framework that integrates Particle Swarm optimization (PSO) and Dwarf Mongoose optimization (DMO) algorithms for feature selection and hyperparameter tuning, combined with advanced classifiers such as Decision Trees (DT), Random Forests (RF), and Support Vector Machines (SVM). A 5-fold cross-validation approach was employed to ensure a robust performance evaluation. Feature extraction was performed using both Discrete Wavelet Decomposition (DWD) and Matching Pursuit (MP), providing a comprehensive representation of the dataset comprising 2400 samples and 41 extracted features. Experimental validation demonstrated the efficacy of the proposed framework. The PSO-optimized RF model achieved the highest accuracy of 97.71%, with a precision of 98.02% and an F1 score of 98.63%, followed by the PSO-DT model with a 95.00% accuracy. Similarly, the DMO-optimized RF model recorded an accuracy of 98.33%, with a precision of 98.80% and an F1 score of 99.04%, outperforming other DMO-based classifiers. This novel framework demonstrates significant advancements in transformer protection by enabling accurate and early fault detection, thereby enhancing the reliability and safety of power systems.
Photosynthesis plays a pivotal role in vegetable growth. However, its intricate interplay with plant physiology and environmental factors complicates precise prediction of photosynthetic rates (Pn). Current predictive...
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Photosynthesis plays a pivotal role in vegetable growth. However, its intricate interplay with plant physiology and environmental factors complicates precise prediction of photosynthetic rates (Pn). Current predictive models primarily focus on environmental influences on photosynthesis, limiting their applicability to leaves exhibiting different physiological traits. To address the challenge, we introduce a novel approach that incorporates chlorophyll fluorescence (ChlF) parameters into a model for predicting Pn across diverse leaf ontogenies. Eggplant leaves were used as experimental samples. We collected 5280 Pn data of leaves with different ChlF parameters under controlled changes in temperature, [CO2], and light intensity. The Fo (initial fluorescence) and Fv/Fm (Maximum light energy conversion efficiency of PSII system) were selected as key ChlF indicators using the entropy method. Fo and Fv/Fm, along with temperature, [CO2], and light intensity, are key features, while Pn serves as a label, forming a robust modeling dataset. Then, we proposed a Convolutional Neural Network Regression model with Input Encoding and Genetic Algorithm optimization (CNNR-IEGA) to train these environment and fluorescence data and develop the predictive model for eggplant *** results indicate that the model exhibits excellent performance in predicting Pn. On unknown datasets, the root mean square error of the model is only 0.97 mu mol center dot m- 2 center dot s- 1, with a high coefficient of determination reaching 0.99. Compared to models established by other algorithms (including multiple nonlinear regression, support vector regression, and back propagation neural network), the proposed model demonstrates superior performance across training, testing, and validation sets. Furthermore, compared to models without ChlF parameters and those with single ChlF parameters, the proposed model has the highest accuracy. This demonstrates the validity of using fluorescence to characterize
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