In wireless sensor networks (WSN), data collection is a crucial and complex issue. Especially when the objective function is unknown, designing an effective data collection optimization algorithm has become a huge cha...
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The Long Short-Term Memory (LSTM) network is a commonly used model for time series data. However, its model performance and generalization ability heavily depend on the parameter configuration. Therefore, it is necess...
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To achieve 'carbon peaking' and 'carbon neutrality', transitioning to electric vehicles (EVs) is crucial. However, unregulated EV charging significantly impacts the power grid's peak-valley load di...
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Bilevel optimization reveals the inner structure of otherwise oblique optimization problems, such as hyperparameter tuning, neural architecture search, and meta-learning. A common goal in bilevel optimization is to mi...
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.The present study aims to calibrate a novel multi-class non-lane-based continuum traffic flow model by applying advanced global search optimization algorithms such as Hybrid Search (Combination of genetic algorithm a...
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.The present study aims to calibrate a novel multi-class non-lane-based continuum traffic flow model by applying advanced global search optimization algorithms such as Hybrid Search (Combination of genetic algorithm and Nelder Mead) and Generalized Pattern Search. To represent vehicular behavior in non-lane-based mixed traffic environment, the new continuum model is deduced from two-sided lateral gap car following theory along with few empirical observations. The newness of the selected model is that it mimics complex overtaking behavior of vehicles, effect of slow-moving vehicles on traffic stream and driver's anticipation behavior. The search algorithms considered in this study have been tested and compared using the real-world data. From the analysis, it is observed that the proposed algorithms are more accurate in minimizing the cost function whereas convergence speed is also found to be better. The calibrated parameters are validated using platoon dispersion characteristics of vehicles and alternative transport policy measures.
In open-pit mines, the blast-induced flyrock is one of the most fundamental problems, therefore, a precision prediction of flyrock can be useful to design a proper blast pattern and reduce the undesirable effects of f...
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In open-pit mines, the blast-induced flyrock is one of the most fundamental problems, therefore, a precision prediction of flyrock can be useful to design a proper blast pattern and reduce the undesirable effects of flyrock. The aim of this study is to develop a new integrated intelligent model to approximate flyrock based on an adaptive neuro-fuzzy inference system (ANFIS) in combination with a grasshopper optimization algorithm (GOA). In addition, a cultural algorithm (CA) is combined with ANFIS to predict flyrock. In the proposed models, the hyperparameters of ANFIS were tuned using CA and GOA. To achieve the objective of this study, a comprehensive database collected from three quarry sites, located in Malaysia, was used. The performance of both ANFIS-CA and ANFIS-GOA models was evaluated by calculation of the statistical functions such as the correlation of determination (R-2). The comparison between the proposed models indicated the higher accuracy of using ANFIS-GOA (R-2 = 0.974) as an efficient model to predict flyrock compared to the ANFIS-CA (R-2 = 0.953).
We study the convergence behavior of the celebrated temporal-difference (TD) learning algorithm. By looking at the algorithm through the lens of optimization, we first argue that TD can be viewed as an iterative optim...
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ISBN:
(纸本)9781713899921
We study the convergence behavior of the celebrated temporal-difference (TD) learning algorithm. By looking at the algorithm through the lens of optimization, we first argue that TD can be viewed as an iterative optimization algorithm where the function to be minimized changes per iteration. By carefully investigating the divergence displayed by TD on a classical counter example, we identify two forces that determine the convergent or divergent behavior of the algorithm. We next formalize our discovery in the linear TD setting with quadratic loss and prove that convergence of TD hinges on the interplay between these two forces. We extend this optimization perspective to prove convergence of TD in a much broader setting than just linear approximation and squared loss. Our results provide a theoretical explanation for the successful application of TD in reinforcement learning.
In order to adapt to the complex battlefield environment and various types of weapon systems in modern war, we built a detailed air defense simulation scenario and transformed it into mathematical and programming mode...
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Accurately estimating the Energy Dissipation Rate (EDR) in Hydrofoil-Crested Stepped Spillways (HCSSs) is crucial for ensuring the safety and optimizing the performance of these hydraulic structures. This study invest...
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Accurately estimating the Energy Dissipation Rate (EDR) in Hydrofoil-Crested Stepped Spillways (HCSSs) is crucial for ensuring the safety and optimizing the performance of these hydraulic structures. This study investigates the prediction of EDR using advanced hybrid Machine Learning (ML) models, including the Tabular Neural Network with Moth Flame optimization (TabNet-MFO), Long Short-Term Memory with Ant Lion Optimizer (LSTM-ALO), Extreme Learning Machine with Jaya and Firefly optimization (ELM-JFO), and Support Vector Regression with Improved Whale optimization (SVR-IWOA). Notably, two novel models-TabNet-MFO and SVR-IWOA-are introduced for the first time, providing dynamic hyperparameter optimization to enhance prediction accuracy in complex hydraulic conditions. To develop the models, a dataset comprising 462 laboratory data points from HCSS experiments was used, with 75 % allocated for the training stage and 25 % for the testing stage. The Isolation Forest (IF) algorithm was employed to detect and remove outliers, resulting in the exclusion of 5 % of the original dataset. Dimensional analysis was conducted to identify key factors influencing EDR, including step number (NS), chute angle (theta), hydrofoil formation index (t), and the ratio of critical depth to total chute height (yC / PS). ANOVA and SHAP analyses confirmed the significant impact of the yC / PS ratio on EDR. Model performance was evaluated using metrics such as the coefficient of determination (R2), Root Mean Squared Error (RMSE), Scatter Index (SI), Weighted Mean Absolute Percentage Error (WMAPE), and symmetric Mean Absolute Percentage Error (sMAPE). Performance was further compared using Taylor diagrams, residual error curves (REC), and the Performance Index (PI). During the training stage, TabNet-MFO outperformed the other models with a PI of 0.784 and a normalized Root Mean Squared Error (E') of 1.231, followed by ELM-JFO with a PI of 0.605 and E' of 1.125. In the testing stage, TabNet-MFO m
The abundance of data has led to the emergence of a variety of optimization techniques that attempt to leverage available side information to provide more anticipative decisions. The wide range of methods and contexts...
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The abundance of data has led to the emergence of a variety of optimization techniques that attempt to leverage available side information to provide more anticipative decisions. The wide range of methods and contexts of application have motivated the design of a universal unitless measure of performance known as the coefficient of prescriptiveness. This coefficient was designed to quantify both the quality of contextual decisions compared to a reference one and the prescriptive power of side information. To identify policies that maximize the former in a data-driven context, this paper introduces a distributionally robust contextual optimization model where the coefficient of prescriptiveness substitutes for the classical empirical risk minimization objective. We present a bisection algorithm to solve this model, which relies on solving a series of linear programs when the distributional ambiguity set has an appropriate nested form and polyhedral structure. Studying a contextual shortest path problem, we evaluate the robustness of the resulting policies against alternative methods when the out-of-sample dataset is subject to varying amounts of distribution shift. Copyright 2024 by the author(s)
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