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An Efficient Algorithm for Power Flow optimization in PV Inverters Systems

ELECTRIC POWER COMPONENTS AND SYSTEMS 2020年第12-13期48卷 1362-1377页

作者： Alsafasfeh, Qais Saraereh, Omar A. Alsafasfeh, Moath Maqableh, Ayman Khan, Imran Choi, Bong Jun Tafila Tech Univ Dept Elect Power & Mechatron Tafila Jordan Hashemite Univ Dept Elect Engn Zarqa Jordan Al Hussein Bin Talal Univ Comp Engn Dept Maan Jordan Luminus Tech Univ Coll Electromech Engn Dept Amman Jordan Univ Engn & Technol Dept Elect Engn Peshawar Pakistan Soongsil Univ Sch Comp Sci & Engn Seoul South Korea

Aiming at the problem of optimal power flow in photovoltaic (PV) Inverters, this paper proposes a real-time algorithm for optimal power flow (OPF) in distributed PV inverters with sophisticated communication technologies and measurement. A higher accuracy and computation speed are obtained by proposing a linear, time-varying optimization method, and the objective function and constraints were linearized using the Taylor expansion under the premise of small variable changes during the short control period. By comparing with the constant linearization method, the proposed time-varying linear approximation method is more accurate and efficient. The problems faced by the centralized calculation is overcome by proposing a distributed control method which utilizes the alternating direction method of multipliers. The proposed distributed control method enables the distributed PV inverters to optimize their own power setpoints with only a little information required from neighbor nodes. Case study results demonstrate that the proposed algorithm makes the distributed PV Inverters more efficient and economical than the other methods.

关键词： photovoltaic renewable energy optimal power flow power grid optimization PV inverters time-varying optimization alternation direction multiplier distributed generation solar power systems optimization algorithms

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Machine learning and optimization for production rescheduling in Industry 4.0

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2020年第9-10期110卷 2445-2463页

作者： Li, Yuanyuan Carabelli, Stefano Fadda, Edoardo Manerba, Daniele Tadei, Roberto Terzo, Olivier LINKS Fdn Via Pier Carlo Boggio 61 I-10138 Turin Italy Politecn Torino Dept Control & Comp Engn Corso Duca Abruzzi 24 I-10129 Turin Italy Univ Brescia Dept Informat Engn Via Branze 38 I-25123 Brescia Italy Politecn Torino ICT City Logist & Enterprises Lab Corso Duca Abruzzi 24 I-10129 Turin Italy

Along with the fourth industrial revolution, different tools coming from optimization, Internet of Things, data science, and artificial intelligence fields are creating new opportunities in production management. While manufacturing processes are stochastic and rescheduling decisions need to be made under uncertainty, it is still a complicated task to decide whether a rescheduling is worthwhile, which is often addressed in practice on a greedy basis. To find a tradeoff between rescheduling frequency and the growing accumulation of delays, we propose a rescheduling framework, which integrates machine learning (ML) techniques and optimization algorithms. To prove the effectiveness, we first model a flexible job-shop scheduling problem with sequence-dependent setup and limited dual resources (FJSP) inspired by an industrial application. Then, we solve the scheduling problem through a hybrid metaheuristic approach. We train the ML classification model for identifying rescheduling patterns. Finally, we compare its rescheduling performance with periodical rescheduling approaches. Through observing the simulation results, we find the integration of these techniques can provide a good compromise between rescheduling frequency and scheduling delays. The main contributions of the work are the formalization of the FJSP problem, the development of ad hoc solution methods, and the proposal/validation of an innovative ML and optimization-based framework for supporting rescheduling decisions.

关键词： Industry 4 0 Flexible job-shop scheduling Rescheduling Machine learning classification optimization algorithms Real-time data analysis

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Research on Automatic Task Evaluation System of English-Assisted Teaching Based on Text Abstract Extraction

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International Journal of High Speed Electronics and Systems 2025年第0期

作者： Ren, Shiming Department of Foreign Languages Teaching Wuxi Institute of Technology Jiangsu Wuxi214121 China

With the continuous improvement and maturity of keyword extraction technology, its application scope continues to expand and has now penetrated into multiple fields. This study innovatively introduces the concept of word similarity and optimizes the TF-IDF algorithm. By comprehensively considering word similarity and inverse frequency, this study has developed a novel TF-IDF optimization algorithm that significantly improves the accuracy and relevance of keyword extraction, thereby more accurately reflecting students' learning status and needs. On this basis, this study further optimized the construction of the sentence matrix and proposed an improved initial point selection strategy to address the problem of traditional K-means algorithm being prone to local optima, thereby improving the global optimality and stability of clustering results. In the stage of text summarization extraction, this study fully considered the potential impact of Chinese writing habits and keywords on sentence importance, and customized and optimized the TextRank algorithm. With the help of machine learning technology, optimization algorithms can gain a deeper understanding of text structure and semantic associations, thereby more accurately identifying and extracting topic sentences that represent the main ideas of the article, and generating higher-quality text summaries. © 2025 World Scientific Publishing Company.

关键词： optimization algorithms

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High-Dimensional Nonconvex Stochastic optimization by Doubly Stochastic Successive Convex Approximation

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 6287-6302页

作者： Mokhtari, Aryan Koppel, Alec Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA US Army Computat & Informat Sci Directorate Res Lab Adelphi MD 20783 USA

In this paper, we consider supervised learning problems over training sets in which the number of training examples and the dimension of feature vectors are both large. We focus on the case where the loss function defining the quality of the parameter we wish to estimate may be non-convex, but also has a convex regularization. We propose a Doubly Stochastic Successive Convex approximation scheme (DSSC) able to handle non-convex regularized expected risk minimization. The method operates by decomposing the decision variable into blocks and operating on random subsets of blocks at each step (fusing the merits of stochastic approximation with block coordinate methods), and then implements successive convex approximation. In contrast to many stochastic convex methods whose almost sure behavior is not guaranteed in non-convex settings, DSSC attains almost sure convergence to a stationary solution of the problem. Moreover, we show that the proposed DSSC algorithm achieves stationarity at a rate of O((log t)/t(1/4)). Numerical experiments on a non-convex variant of a lasso regression problem show that DSSC performs favorably in this setting. We then apply this method to the task of dictionary learning from high-dimensional visual data collected from a ground robot, and observe reliable convergence behavior for a difficult non-convex stochastic program.

关键词： optimization algorithms stochastic optimization successive convex approximation

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Convex Partition: A Bayesian Regression Tree for Black-box Optimisation

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Pattern Recognition Letters 2025年

作者： de Paz, Erick G.G. Vaquera-Huerta, Humberto Albores-Velasco, Francisco Javier Bauer-Mengelberg, John R. Romero-Padilla, Juan Manuel Colegio de Postgraduados Carretera México-Texcoco Km. 36.5 Texcoco56230 Mexico Universidad Autónoma de Tlaxcala Calzada Apizaquito S/N Tlaxcala Apizaco90341 Mexico

Surrogate-based optimisation is a notable approach for problems, where evaluating the objective function is expensive. These methods construct a model of the objective function to guide the search for optimal solutions. A key challenge lies in efficiently exploring the search space to identify subsets that contain extreme values of the function. This paper introduces Convex Partition (CP), an algorithm based on Classification and Regression Trees (CARTs) and enhanced by a novel Bayesian criterion for selecting partitions during tree construction. This criterion maximises the probability of sampling extreme function values. CP is evaluated on a suite of benchmark functions in dimensions 30, 50 and 100, comparing its performance against black-box optimisation algorithms and existing tree-based surrogates methods such as: Genetic algorithms (GAs), Differential Evolution (DE), Treed Gaussian Processes (TGPs), Bayesian Treed Constants (BTC) and others. Experimental results show that CP achieves a competitive and robust performance. © 2025

关键词： optimization algorithms

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Optimizing the Cost and Reliability of Shared Anchors in an Array of Floating Offshore Wind Turbines

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ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART B-MECHANICAL ENGINEERING 2021年第4期7卷 040905页

作者： Devin, Michael C. DuPont, Bryony L. Hallowell, Spencer T. Arwade, Sanjay R. Oregon State Univ Sch Mech Ind & Mfg Engn Corvallis OR 97331 USA Baker Design Consultants Freeport ME 04032 USA Univ Massachusetts Amherst MA 01003 USA

Commercial floating offshore wind projects are expected to emerge in the U.S. by the end of this decade. Currently, however, high costs for the technology limit its commercial viability, and a lack of data regarding system reliability heightens project risk. This work presents an optimization algorithm to examine the tradeoffs between cost and reliability for a floating offshore wind array that uses shared anchoring. Combining a multivariable genetic algorithm with elements of Bayesian optimization, the optimization algorithm selectively increases anchor strengths to minimize the added costs of failure for a large floating wind farm in the Gulf of Maine under survival load conditions. The algorithm uses an evaluation function that computes the probability of mooring system failure, then calculates the expected maintenance costs of a failure via a Monte Carlo method. A cost sensitivity analysis is also performed to compare results for a range of maintenance cost profiles. The results indicate that virtually all of the farm's anchors are strengthened in the minimum cost solution. Anchor strength is increased between 5 and 35% depending on farm location, with anchor strength nearest the export cable being increased the most. The optimal solutions maintain a failure probability of 1.25%, demonstrating the tradeoff point between cost and reliability. System reliability was found to be particularly sensitive to changes in turbine costs and downtime, suggesting further research into floating offshore wind turbine failure modes in extreme loading conditions could be particularly impactful in reducing project uncertainty.

关键词： Floating wind turbines Ocean engineering optimization Turbines Stress Genetic algorithms Uncertainty Reliability Wind Wind farms Failure optimization algorithms Mooring Maintenance algorithms Cables

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Towards self-organizing railway traffic management: concept and framework

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JOURNAL OF RAIL TRANSPORT PLANNING & MANAGEMENT 2024年 29卷

作者： D'Amato, Leo Naldini, Federico Tibaldo, Valentina Trianni, Vito Pellegrini, Paola Politecn Torino Dipartimento Automat & Informat Corso Duca degli Abruzzi 24 Turin Italy CNR Inst Cognit Sci & Technol Via San Martino della Battaglia 44 I-00185 Rome Italy Univ Gustave Eiffel COSYS ESTAS F-59650 Villeneuve Dascq France

Railway traffic management requires a timely and accurate redefinition of routes and schedules in response to detected perturbations of the original timetable. To date, most of the (automated) solutions to this problem require a central authority to make decisions for all the trains in a given control area. An appealing alternative is to consider trains as intelligent agents able to self -organize and determine the best traffic management strategy. This could lead to more scalable and resilient approaches, that can also take into account the real-time mobility demand. In this paper, we formalize the concept of railway traffic self -organization and we present an original design that enables its real -world deployment. We detail the principles at the basis of the sub -processes brought forth by the trains in a decentralized way, explaining their sequence and interaction. Moreover, we propose a preliminary proof of concept based on a realistic setting representing traffic in a French control area. The results allow conjecturing that self -organizing railway traffic management may be a viable option, and foster further research in this direction.

关键词： Railway traffic management optimization algorithms Self-organization Consensus

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Bridging Evolutionary algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid algorithms

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Li, Pengyi Hao, Jianye Tang, Hongyao Fu, Xian Zhen, Yan Tang, Ke College of Intelligence and Computing Tianjin University Tianjin China Quebec Canada Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary algorithms (EAs) and Reinforcement Learning (RL) for optimization, has demonstrated remarkable performance advancements. By fusing both approaches, ERL has emerged as a promising research direction. This survey offers a comprehensive overview of the diverse research branches in ERL. Specifically, we systematically summarize recent advancements in related algorithms and identify three primary research directions: EA-assisted optimization of RL, RL-assisted optimization of EA, and synergistic optimization of EA and RL. Following that, we conduct an in-depth analysis of each research direction, organizing multiple research branches. We elucidate the problems that each branch aims to tackle and how the integration of EAs and RL addresses these challenges. In conclusion, we discuss potential challenges and prospective future research directions across various research directions. To facilitate researchers in delving into ERL, we organize the algorithms and codes involved on https://***/yeshenpy/Awesome-Evolutionary-Reinforcement-Learning IEEE

关键词： optimization algorithms

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A Randomized Block-Coordinate Adam online learning optimization algorithm

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NEURAL COMPUTING & APPLICATIONS 2020年第16期32卷 12671-12684页

作者： Zhou, Yangfan Zhang, Mingchuan Zhu, Junlong Zheng, Ruijuan Wu, Qingtao Henan Univ Sci & Technol Sch Informat Engn Luoyang 471023 Peoples R China

In recent years, stochastic gradient descent (SGD) becomes one of the most important optimization algorithms in many fields, such as deep learning and reinforcement learning. However, the computation of full gradient in SGD is prohibitive when dealing with high-dimensional vectors. For this reason, we propose a randomized block-coordinate Adam (RBC-Adam) online learning optimization algorithm. At each round, RBC-Adam randomly chooses a variable from a subset of parameters to compute the gradient and updates the parameters along the negative gradient direction. Moreover, this paper analyzes the convergence of RBC-Adam and obtains the regret bound,O(T) whereTis a time horizon. The theoretical results are verified by simulated experiments on four public datasets. Moreover, the simulated experiment results show that the computational cost of RBC-Adam is lower than the variants of Adam.

关键词： Adam Convergence Online learning optimization algorithms Randomized block-coordinate Stochastic gradient descent

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Research on Computer-aided Innovation and Entrepreneurship Education Model and Algorithm

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Wang, Jingru Xinyu University Jiangxi Xinyu338000 China

With the rapid development of science and technology, computer-aided technology has been widely used in the field of education, but in the field of innovation and entrepreneurship education (Hereinafter referred to as IEE), there are still problems such as uneven distribution of resources and the disconnection between teaching content and actual *** paper puts forward a new model framework of computer-aided IEE, which is student-centered and *** clear teaching objectives, rich course content, diverse teaching methods, and a scientific evaluation system, students' innovative consciousness and entrepreneurial ability are comprehensively *** course covers innovative thinking, business models, market research, and other theoretical knowledge, and introduces virtual reality (VR) and augmented reality (AR) technologies to simulate the real entrepreneurial process and provide a practical *** the aspect of algorithm application, the research combines the content recommendation algorithm and collaborative filtering recommendation algorithm, constructs accurate user portraits by analyzing students' learning behavior data, and matches them according to the characteristics of learning resources to recommend personalized learning resources for *** the same time, the learning path optimization algorithm is developed, and the personalized and efficient learning path is planned by using the shortest path algorithm in graph theory to ensure that students can master the required knowledge in the shortest *** experimental part designed a one-semester teaching experiment, and compared the differences between the experimental group and the control group in academic performance, learning participation, and *** results show that the computer-aided IEE model significantly improves students' academic performance, learning participation, and satisfaction, which verifies the effectiveness of the *** research on the

关键词： optimization algorithms

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