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arXiv 2023年

作者： Birge, John R. The University of Chicago Booth School of Business 5807 South Woodlawn Avenue ChicagoIL60637 United States

optimization software enables the solution of problems with millions of variables and associated parameters. These parameters are, however, often uncertain and represented with an analytical description of the parameter’s distribution or with some form of sample. With large numbers of such parameters, optimization of the resulting model is often driven by misspecifications or extreme sample characteristics, resulting in solutions that are far from a true optimum. This paper describes how asymptotic convergence results may not be useful in large-scale problems and how the optimization of problems based on sub-sample estimates may achieve improved results over models using full-sample solution estimates. A motivating example and numerical results from a portfolio optimization problem demonstrate the potential improvement. A theoretical analysis also provides insight into the structure of problems where sub-sample optimization may be most beneficial. © 2023, CC BY.

关键词： optimization algorithms

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Gauss-Newton Runge-Kutta integration for efficient discretization of optimal control problems with long horizons and least-squares costs

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EUROPEAN JOURNAL OF CONTROL 2024年 80卷

作者： Frey, Jonathan Baumgaertner, Katrin Diehl, Moritz Univ Freiburg Dept Microsyst Engn IMTEK Freiburg Germany Univ Freiburg Dept Math Freiburg Germany

This work proposes an efficient treatment of continuous-time optimal control problems with long horizons and nonlinear least-squares costs. In particular, we present the Gauss-Newton Runge-Kutta (GNRK) integrator which provides a high-order cost integration. Crucially, the Hessian of the cost terms required within SQP-type algorithm is approximated with a Gauss-Newton Hessian. Moreover, L2 penalty formulations constraints are shown to be particularly effective for optimization with GNRK. An efficient implementation of GNRK is provided in the open-source software framework acados. We demonstrate the effectiveness the proposed approach and its implementation on an illustrative example showing a reduction of relative suboptimality by a factor greater than 10 while increasing the runtime by only 10%.

关键词： Optimal control optimization algorithms optimization

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Regularity Properties of optimization-Based Controllers

arXiv

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arXiv 2023年

作者： Mestres, Pol Allibhoy, Ahmed Cortés, Jorge Department of Mechanical and Aerospace Engineering University of California 9500 Gilman Dr La Jolla San DiegoCA92093 United States Department of Mechanical Engineering University of California Riverside 900 University Ave. RiversideCA92521 United States

This paper studies regularity properties of optimization-based controllers, which are obtained by solving optimization problems where the parameter is the system state and the optimization variable is the input to the system. Under a wide range of assumptions on the optimization problem data, we provide an exhaustive collection of results about their regularity, and examine their implications on the existence and uniqueness of solutions and the forward invariance guarantees for the resulting closed-loop systems. We discuss the broad relevance of the results in different areas of systems and controls. Copyright © 2023, The Authors. All rights reserved.

关键词： optimization algorithms

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Optimizing energy Dynamics: A comprehensive analysis of hybrid energy storage systems integrating battery banks and supercapacitors

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ENERGY CONVERSION AND MANAGEMENT 2024年 312卷

作者： Guven, Aykut Fatih Abdelaziz, Almoataz Y. Samy, Mohamed Mahmoud Barakat, Shimaa Yalova Univ Engn Fac Dept Elect & Elect Engn Yalova Turkiye Ain Shams Univ Fac Engn Cairo 11517 Egypt Future Univ Egypt Fac Engn & Technol Cairo 11835 Egypt Beni Suef Univ Dept Elect Engn Bani Suwayf Egypt

This study investigates the optimization of a grid-connected hybrid energy system integrating photovoltaic (PV) and wind turbine (WT) components alongside battery and supercapacitor storage. The research addresses the critical need for efficient energy storage solutions in renewable energy integration. Six optimization algorithms-AGTO, ARO, BOA, CGO, PFA, and TSO-are evaluated for their efficacy in determining optimal system configurations. The system's adaptability to dynamic scenarios is examined through comprehensive sensitivity analyses, shedding light on its robustness. The findings reveal that the CGO algorithm outperforms others by achieving optimal solutions with fewer iterations, highlighting its efficiency. Key conclusions include the identification of an optimal configuration comprising a 589.58 kW PV system, 664 kW WT, a 675-kW supercapacitor, and a 1000 kWh battery bank. This configuration achieves an 80 % renewable energy fraction (REF), reduces the annual system cost (ACS) to $603,537.8522, and maintains a competitive levelized cost of electricity (LCOE) at $0.2380 per kWh. The research underscores the significance of integrated energy storage solutions in optimizing hybrid energy configurations, offering insights crucial for advancing sustainable energy initiatives. The study contributes valuable insights to the scientific community, paving the way for more efficient and resilient renewable energy systems.

关键词： Hybrid renewable energy system Hybrid energy storage system Supercapacitor optimization algorithms Sensitivity Analysis Sustainable energy

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Application of ANN control algorithm for optimizing performance of a hybrid ORC power plant

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ENERGY 2024年 306卷

作者： Podlasek, Szymon Jankowski, Marcin Balazy, Patryk Lalik, Krzysztof Figaj, Rafal AGH Univ Krakow Fac Mech Engn & Robot Al Mickiewicza 30 PL-30059 Krakow Poland AGH Univ Krakow Fac Energy & Fuels Al Mickiewicza 30 PL-30059 Krakow Poland

Hybrid systems for generating electricity from multiple sources are becoming an increasingly popular subject of analysis in science and industry. This paper presents a validated model of a hybrid ORC plant powered by solar and geothermal energy. A key challenge in optimizing the operating parameters over time was the variability of solar conditions, which was the main energy source of the system. The operation of the ORC plant is simulated using a complex model with Multiple Input Multiple Output (MIMO) variables, which is nonlinear. The input variables represent the system's operational parameters, while the output variables describe the plant's performance indicators. The main objective of this paper is to optimize the year-round performance of the ORC installation through different computational techniques. The first approach involves the application of the gradient-based optimization method that is known as sequential quadratic programming (SQP). With the use of SQP, two distinct simulation runs (SQP-N and SQP-Q/N) of the system are performed, each with a specific objective function to be optimized. The second approach is based on reinforcement learning principles and leverages the method known as Deep Deterministic Policy Gradient (DDPG) algorithm. The main advantage of DDPG over SQP is that DDPG does not require knowledge of the model. This improves the algorithm flexibility, making it well-adapted to fluctuating environmental conditions. Overall, three optimization runs (two using SQP, one using DDPG) have been performed, aiming at identifying the optimal year-round control strategy for the installation. The results revealed that under the control of DDPG, the hybrid system has produced the highest amount of electricity (4993.4 MWh), outperforming in this matter SQP-N and SQP-Q/N optimization variants by 16.83 % and 10.49%, respectively.

关键词： Hybrid energy systems Renewable energy sources Neural networks optimization algorithms Computer modeling

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An Agile Approach for Adopting Sustainable Energy Solutions with Advanced Computational Techniques

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ENERGIES 2024年第13期17卷 3150页

作者： Konneh, David Abdul Howlader, Harun Or Rashid Elkholy, M. H. Senjyu, Tomonobu Univ Ryukyus Dept Elect & Elect Engn Nishihara Okinawa 9030213 Japan Univ Hawaii Hawaii Nat Energy Inst Grid START Res Corp Honolulu HI 96822 USA

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policymakers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision-Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale, but the findings also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

关键词： sustainable energy solutions renewable energy integration multi-criteria decision tool advanced computational techniques grid-connected hybrid system optimization algorithms policy decision-making metrics environmental sustainability energy security

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Meta-model structural monitoring with cutting-edge AAE-VMD fusion alongside optimized machine learning methods

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2024年

作者： Hassani, Sahar Univ New South Wales Ctr Infrastruct Engn & Safety Sch Civil & Environm Engn Sydney NSW 2052 Australia

Transfer learning significantly enhances machine learning by leveraging knowledge from one dataset to boost performance on another, which is particularly beneficial when labelled data ares limited or costly. Sensor fusion, an essential technique in structural health monitoring, improves the effectiveness of transfer learning by combining data from multiple sensors to extract more informative features. This article proposes a novel meta-model structural monitoring using a new data fusion method named the adversarial autoencoder (AAE)-variational mode decomposition (VMD) algorithm, which integrates optimization algorithms, transfer learning, and machine learning techniques for damage detection tasks. The proposed meta-model combines transfer learning with an optimized machine learning framework for training and employs pretrained models for testing across diverse datasets. First, the tensors are fused using the AAE approach into 300 data points, reducing noise and outliers, performing dimensionality reduction, and enriching the training and test datasets. Then, a preprocessing step involves decomposing and denoising tensors using the VMD algorithm, followed by selecting the most informative intrinsic mode functions (IMFs) based on their variance. These selected IMFs are concatenated, and 13 statistical features are extracted from them and used as inputs for machine learning models. Various optimization algorithms are employed to fine-tune the hyperparameters of the machine learning models for optimal classification results. Validation of this meta-model utilizes the dataset collected from a steel grandstand structure, available in benchmark dataset format. For decomposed fused tensors, both particle swarm optimization and covariance matrix adaptation evolution strategy emerge as equally effective optimization techniques for K-nearest neighbours (KNN), consistently achieving the highest mean test accuracy and F1 score of 99.5%. Conversely, KNN stands out as a robust an

关键词： Structural health monitoring data fusion noise reduction method machine learning transfer learning optimization algorithms

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SMOOTHING ACCELERATED PROXIMAL GRADIENT METHOD WITH FAST CONVERGENCE RATE FOR NONSMOOTH MULTI-OBJECTIVE optimization

arXiv

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arXiv 2023年

作者： Huang, Chengzhi Chongqing Normal University China

This paper proposes a Smoothing Accelerated Proximal Gradient Method with Extrapolation Term (SAPGM) for nonsmooth multiobjective optimization. By combining the smoothing methods and the accelerated algorithm for multiobjective optimization by Tanabe et al., our method achieve fast convergence rate. Specifically, we establish that the convergence rate of our proposed method can be enhanced to o(lnσ k/k) by incorporating a extrapolation term (Formula Presented) with α > ***, we prove that the iterates sequence is convergent to a Pareto optimal solution of the primal problem. Furthermore, we present an effective strategy for solving the subproblem through its dual representation, validating the efficacy of the proposed method through a series of numerical experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： optimization algorithms

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A Simple Uniformly Optimal Method without Line Search for Convex optimization

arXiv

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arXiv 2023年

作者： Li, Tianjiao Lan, Guanghui School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA30332 United States

Line search (or backtracking) procedures have been widely employed into first-order methods for solving convex optimization problems, especially those with unknown problem parameters (e.g., Lipschitz constant). In this paper, we show that line search is superfluous in attaining the optimal rate of convergence for solving a convex optimization problem whose parameters are not given a priori. In particular, we present a novel accelerated gradient descent type algorithm called auto-conditioned fast gradient method (AC-FGM) that can achieve an optimal O(1/k2) rate of convergence for smooth convex optimization without requiring the estimate of a global Lipschitz constant or the employment of line search procedures. We then extend AC-FGM to solve convex optimization problems with Hölder continuous gradients and show that it automatically achieves the optimal rates of convergence uniformly for all problem classes with the desired accuracy of the solution as the only input. Finally, we report some encouraging numerical results that demonstrate the advantages of AC-FGM over the previously developed parameter-free methods for convex optimization. © 2023, CC BY.

关键词： optimization algorithms

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Advancements in machine learning techniques for coal and gas outburst prediction in underground mines

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INTERNATIONAL JOURNAL OF COAL GEOLOGY 2024年 285卷

作者： Anani, Angelina Adewuyi, Sefiu O. Risso, Nathalie Nyaaba, Wedam Univ Arizona Dept Min & Geol Engn 1235 James E Rogers Way Tucson AZ 85719 USA Univ Illinois Dept Orthopaed 835 S Wolcott Ave Chicago IL 60612 USA

Coal and gas outbursts are a major cause of fatalities in underground coal mines and pose a threat to coal power generation worldwide. Among the current mitigation efforts include monitoring methane gas levels using sen-sors, employing geophysical surveys to identify geological structures and zones prone to outbursts, and using empirical modeling for outburst predictions. However, in the wake of industry 4.0 technologies, several studies have been conducted on applying artificial intelligence methods to predict outbursts. The proposed models and their results vary significantly in the literature. This study reviews the application of machine learning (ML) to predict coal and gas outbursts in underground mines using a mixed-method approach. Most of the available literature, with a focus on ML applications in coal and gas outburst prediction, was investigated in China. Findings indicate that researchers proposed diverse ML models mostly combined with different optimization algorithms, including particle swarm optimization (PSO), genetic algorithm (GA), rough set (RS), and fruit fly optimization algorithm (IFOA) for outburst prediction. The number and type of input parameters used for prediction differed significantly, with initial gas velocity being the most dominant parameter for gas outbursts, and coal seam depth as the dominant parameter for coal outbursts. The datasets for training and testing the proposed ML models in the literature varied significantly but were mostly insufficient - which questions the reliability of some of the applied ML models. Future research should investigate the effect of data size and input parameters on coal and gas outburst prediction.

关键词： Mining hazard Machine learning optimization algorithms Artificial intelligence Coal mining

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