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Hybrid LSTM-PSO optimization techniques for enhancing wind power bidding efficiency in electricity markets

AIN SHAMS ENGINEERING JOURNAL 2025年第2期16卷

作者： Truong, Viet Anh Dinh, Ngoc Sang Duong, Thanh Long Le, Ngoc Thien Truong, Cong Dinh Nguyen, Linh Tung Hochiminh City Univ Technol & Educ Ho Chi Minh City 71300 Vietnam Ind Univ HoChiMinh City Ho Chi Minh City 71400 Vietnam Univ Architecture Hochiminh City Ho Chi Minh City 72400 Vietnam Elect Power Univ Ha Noi City 11900 Vietnam

Past research has predominantly focused on utilizing meta-heuristic algorithms to optimize neural network structures, while the exploration of deep learning in optimization has remained relatively limited. The proposed hybrid approach seeks to enhance wind power bidding strategies, improving profitability by predicting optimal output power for day-ahead electricity markets. This method integrates Long Short-Term Memory (LSTM) with Particle Swarm Optimization (PSO), leveraging LSTM's ability to predict the active movement tendencies of particles for more efficient and faster optimization. Experiments conducted on the IEEE 30-bus power system show that the LSTM-PSO hybrid outperforms mathematical models and standalone PSO algorithms. It also delivers an optimal wind power bidding strategy, yielding peak annual revenue, while recommending a 16 % reduction in bidding output power variance in models that integrate wind power with thermal power and energy storage systems (ESS). Ultimately, this approach fosters confidence in wind energy investment, contributing to sustainable development.

关键词： optimal algorithm Long short-term memory Particle swarm optimization Wind farm Electricity market

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Analysis of Aerosol Optical Mixing Using the EOMOS Model From Typical AERONET Sites

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GEOPHYSICAL RESEARCH LETTERS 2024年第18期51卷

作者： Yan, Gang Sun, Bingqiang Gao, Chenxu Chen, Yunqian Chen, Jianmin Fudan Univ Inst Atmospher Sci Dept Atmospher & Ocean Sci Shanghai Peoples R China Fudan Univ IRDR Int Ctr Excellence Risk Interconnect & Govern Shanghai Peoples R China Fudan Univ Dept Environm Sci & Engn Shanghai Key Lab Atmospher Particle Pollut & Preve Shanghai Peoples R China

Satellite remote sensing predominantly employs optical properties for aerosol classification, often neglecting aerosol mixing and lacking validation accuracy. This study defines five aerosol types: marine, continental, dust, urban-industrial, and biomass-burning. Proposing the external optical mixing optimization solver (EOMOS) model based on the external mixing assumption, the model's accuracy is enhanced by approximately 95.0% through constraints and optimization. Perturbation experiments on particle size distribution and complex refractive index validate the model's robustness. The EOMOS model analyzes aerosol mixing states, and quantifies contributions to aerosol optical depth (AOD) for each aerosol type, surpassing traditional methods by at least 139.7%. Additionally, the EOMOS model examines trends in aerosol type AOD, revealing a noticeable post-2013 reduction in AOD of urban-industrial aerosols in Beijing, suggesting pollution mitigation. In Brazil, urban-industrial and biomass-burning aerosol AODs were 328.1% and 107.7% higher in 2005, 2007, and 2010, primarily due to fire impact. Plain Language Summary Aerosols are tiny particles suspended in the atmosphere, and have complex impacts on climate, human health, and the environment. This study analyzes the external mixing state of aerosols based on optical properties and provides a practical and feasible method for characterizing them. Five primary aerosol types were predefined and their mixing states were determined by relating them to optical observations. The results revealed that urban-industrial aerosols in Beijing have gradually decreased since 2013, partly due to the implementation of the "Action Plan for the Prevention and Control of Air Pollution (2013-2017)" by the Chinese government. However, they remain the predominant pollutants, with peak concentrations occurring in July. In contrast, the Brazilian region experienced a sudden increase in biomass-burning aerosols in 2005, 2007, and 2010 due to wil

关键词： aerosol classification external optical mixing optimization solver (EOMOS) model optimal algorithm

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Shift scheduling and rostering with same shift-type and weekend-off fairness constraints in call centres

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EUROPEAN JOURNAL OF INDUSTRIAL ENGINEERING 2025年第3期19卷 340-373页

作者： Wang, Ruicheng Xu, Yue Wang, Xiuli Nanjing Univ Sci & Technol Sch Econ & Management Nanjing Peoples R China Yangzhou Univ Sch Architectural Sci & Engn Yangzhou Peoples R China

Based on the actual operational situation of call centres, this paper incorporates the constraints of the same shift-type within a week and the fairness of weekends-off into scheduling. Utilising the progressive decomposition structure of the same shift-type constraint, this paper constructs an integer programming model for multi-week scheduling optimisation problem of call centre agents. We first analyse the maximum lower bound of the problem and prove the optimality of its relaxation problem. Then we propose a two-stage algorithm which combines a constructive heuristic with neighbourhood search incorporating simulated annealing. Experimental results show that the integer programming model is only suitable for achieving optimal solutions for small-scale problems, while our two-stage algorithm can obtain (sub-)optimal solutions for large-scale problems. The impact of employment policy on labour costs is also discussed. [Received: 21 March 2023;Accepted: 12 November 2023]

关键词： call centre shift scheduling rostering integer programming optimal algorithm heuristic algorithm neighbourhood search weekend-off fairness same shift-type operations management

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Research on the fusion of FY4A satellite data and station observation data for heavy fog recognition

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THEORETICAL AND APPLIED CLIMATOLOGY 2025年第1期156卷 1-12页

作者： Yao, Zhenhai Wang, Chuanhui Jiang, Chun Anhui Publ Meteorol Serv Ctr 16 Shihe Rd Hefei 230031 Peoples R China

Satellite observation of fog possesses technical advantages of wide coverage and high spatial-temporal resolution. However, the accuracy of satellite-based fog identification is subject to errors induced by factors such as atmospheric and radiation conditions. This study aims to improve the accuracy of fog identification by integrating ground-based station observations with the Fengyun-4 A (FY-4 A) satellite data. Taking Anhui Province as the study area, we establish a fog identification model using multiple algorithms, namely threshold method (THD), support vector machine (SVM), random forest (RF) and gradient boosting (XGB). In addition, a nearby pixel method is employed to validate identification results, in order to select the optimal algorithm. The results indicate that machine learning algorithms outperform the THD method in fog identification. Among the SVM, RF and XGB algorithms, the RF method exhibits the highest median KSS (0.66) and excellent robustness, and thus it is the optimal algorithm. Case studies demonstrate that the RF-based identification results effectively reflect the spatial distribution of fog regions. Although the differences between the images of identification results before and after correction are not obvious, the identification accuracy is highly susceptible to instability due to factors such as radiation, cloud cover and fog intensity. After correction based on station observations, the model KSS scores are noticeably improved (up to 67.2%) and become more stable. Compared with single-satellite-data-based fog monitoring methods, the integration of the FY-4 A satellite data and station observations offers multi-dimensional observation complementarity and achieves technological advances in the digitization and spatialization of fog observations.

关键词： FY4A optimal algorithm Fog recognition Station-observation-based correction Anhui province

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Quantum annealing-driven branch and bound for the single machine total weighted number of tardy jobs scheduling problem

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 155卷 245-255页

作者： Bozejko, Wojciech Pempera, Jaroslaw Uchronski, Mariusz Wodecki, Mieczyslaw Wroclaw Univ Sci & Technol Dept Control Syst & Mechatron Janiszewskiego 11-17 PL-50372 Wroclaw Poland Ctr Networking & Supercomp Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland Wroclaw Univ Sci & Technol Dept Telecommun & Teleinformat Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland

In the paper we present a new approach to solving NP -hard problems of discrete optimization adapted to the architecture of quantum processors (QPU, Quantum Processor Unit) implementing hardware quantum annealing. This approach is based on the use of the quantum annealing metaheuristic in the exact branch and bound algorithm to compute the lower and upper bounds of the objective function. To determine the lower bound, a new method of defining the Lagrange function for the dual problem (the generalized discrete knapsack problem) was used, the value of which is calculated on the QPU of a quantum machine. In turn, to determine the upper bound, we formulate an appropriate task in the form of binary quadratic programming with constraints. Despite the fact that the results generated by the quantum machine are probabilistic, the hybrid method of algorithm construction proposed in the paper, using alternately a CPU and QPU, guarantees the optimal solution. As a case study we consider the NP -hard single machine scheduling problem with minimizing the weighted number of tardy jobs. The performed computational experiments showed that optimal solutions were already obtained in the root of the solution tree, and the values of the lower and upper bounds differ by only a few percent.

关键词： Optimization Quantum computing Scheduling optimal algorithm Quantum annealing Lagrange relaxation

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Development of new indices and use of CARS-Ridge algorithm for wheat fusarium head blight detection using in-situ hyperspectral data

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BIOSYSTEMS ENGINEERING 2024年 237卷 13-25页

作者： Zhang, Hansu Zhao, Jinling Huang, Linsheng Huang, Wenjiang Dong, Yingying Ma, Huiqin Ruan, Chao Anhui Univ Natl Engn Res Ctr Agroecol Big Data Anal & Applica Hefei 230601 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Peoples R China

Wheat Fusarium head blight (FHB) poses a significant threat to wheat quality and yield. However, accurately identifying the disease in wheat ears remains challenging due to limited data and weak hyperspectral signals. This study aimed to address these issues by obtaining effective wheat canopy hyperspectral data over three years of successive experiments. To reduce band redundancy and improve accuracy, nine different models were constructed, and the optimal algorithm was determined. Additionally, two types of new indices, were developed based on the spectral response mechanism of the wheat disease and published vegetation indices. Our study demonstrated that these newly constructed indices outperformed the published vegetation indices in terms of detection capability. By fusing the optimal algorithm with the new indices, a detection accuracy of 91.4% for the disease was achieved, surpassing the current level of wheat FHB detection. The high-accuracy model developed in this study not only provides methodological support for detecting wheat FHB but also serves as a reference for diagnosing diseases in other crops.

关键词： wheat fusarium head blight Hyperspectral data Detection models optimal algorithm New indices construction

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Simple and optimal Stochastic Gradient Methods for Nonsmooth Nonconvex Optimization

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JOURNAL OF MACHINE LEARNING RESEARCH 2022年第1期23卷 1-61页

作者： Li, Zhize Li, Jian Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Tsinghua Univ Inst Interdisciplinary Informat Sci Beijing 100084 Peoples R China

We propose and analyze several stochastic gradient algorithms for finding stationary points or local minimum in nonconvex, possibly with nonsmooth regularizer, finite-sum and online optimization problems. First, we propose a simple proximal stochastic gradient algorithm based on variance reduction called ProxSVRG+. We provide a clean and tight analysis of ProxSVRG+, which shows that it outperforms the deterministic proximal gradient descent (ProxGD) for a wide range of mini -batch sizes, hence solves an open problem proposed in Reddi et al. (2016b). Also, ProxSVRG+ uses much less proximal oracle calls than ProxSVRG (Reddi et al., 2016b) and extends to the on-line setting by avoiding full gradient computations. Then, we further propose an optimal algorithm, called SSRGD, based on SARAH (Nguyen et al., 2017) and show that SSRGD further improves the gradient complexity of ProxSVRG+ and achieves the the optimal upper bound, matching the known lower bound of (Fang et al., 2018;Li et al., 2021). Moreover, we show that both ProxSVRG+ and SSRGD enjoy automatic adaptation with local structure of the objective function such as the Polyak-Lojasiewicz (PL) condition for nonconvex functions in the finite-sum case, i.e., we prove that both of them can automatically switch to faster global linear convergence without any restart performed in prior work ProxSVRG (Reddi et al., 2016b). Finally, we focus on the more challeng-ing problem of finding an (f, 6)-local minimum instead of just finding an epsilon-approximate (first-order) stationary point (which may be some bad unstable saddle points). We show that SSRGD can find an (f, 6)-local minimum by simply adding some random perturbations. Our algorithm is almost as simple as its counterpart for finding stationary points, and achieves similar optimal rates.

关键词： nonconvex optimization optimal algorithm proximal gradient descent variance re-duction local minimum

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The one-station bike repositioning problem

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DISCRETE APPLIED MATHEMATICS 2024年 357卷 173-196页

作者： Angelelli, E. Mor, A. Speranza, M. G. Univ Brescia Dept Econ & Management Brescia Italy Politecn Milan Dept Management Econ & Ind Engn Milan Italy

In bike sharing systems the quality of the service to the users strongly depends on the strategy adopted to reposition the bikes. The bike repositioning problem is in general very complex as it involves different interrelated decisions: the routing of the repositioning vehicles, the scheduling of their visits to the stations, the number of bikes to load or unload for each station and for each vehicle that visits the station. In this paper we study the problem of optimally loading/unloading vehicles that visit the same station at given time instants of a finite time horizon. The goal is to minimize the total lost demand of bikes and free stands in the station. We model the problem as a mixed integer linear programming problem and present an optimal algorithm that runs in linear time in the size of the time horizon. (c) 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Bike repositioning problem One station Linear complexity optimal algorithm

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algorithms for a two-machine no-wait flow shop scheduling problem with two competing agents

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2024年第1期48卷 6-6页

作者： Yang, Qi-Xia Liu, Long-Cheng Huang, Min Wang, Tian-Run Xiamen Univ Sch Math Sci Xiamen 361005 Peoples R China

In this paper, we consider the following two-machine no-wait flow shop scheduling problem with two competing agents F2 | M-1 -> M-2, M-2, p(ij)(A)=p, no- wait | CmaxA: CmaxB <= Q: Given a set of n jobs J={J(1),J(2), . . . ,J(n)} and two competing agents A and B. Agent A is associated with a set of nA jobs JA={J(1)A,J(2)A, . . . ,J(n)(A)A} to be processed on the machine M-1 first and then on the machine M-2 with no-wait constraint, and agent B is associated with a set of n(B) jobs J(B)={J(1)B,J(2)B, . . . ,J(nB)(B)} to be processed on the machine M-2 only, where the processing times for the jobs of agent A are all the same (i.e., p(ij)A=p), J=J(A)boolean OR J(B) and n=n(A)+n(B). The objective is to build a schedule pi of the n jobs that minimizing the makespan of agent A while maintaining the makespan of agent B not greater than a given value Q. We first show that the problem is polynomial time solvable in some special cases. For the non-solvable case, we present an O(nlog n)-time (1+1/n(A)+1)-approximation algorithm and show that this ratio of (1+1/n(A)+1) is asymptotically tight. Finally, (1+epsilon)-approximation algorithms are provided.

关键词： Scheduling with two agents No-wait flow shop optimal algorithm Approximation algorithm Makespan

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optimal Pattern Determination in Reconfigurable Intelligent Surface aided Communications 34

Optimal Pattern Determination in Reconfigurable Intelligent ...

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IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Prasad, Narayan Yapici, Yavuz Luo, Tao Li, Junyi Gaal, Peter Qualcomm Inc Bridgewater MA 08807 USA Qualcomm Inc San Diego CA USA

ISBN: (纸本)9781665464833

Reconfigurable Intelligent surface (RIS) has emerged as a candidate technology for enhancing coverage in millimeter wave wireless networks at a lower energy and cost footprint. RIS has several antenna elements that can each be configured to reflect impinging electromagnetic waves after imparting a chosen phase shift with possibly some amplitude attenuation (a.k.a. chosen reflection coefficient). Over the canonical RIS-enabled communications scenario, an optimal choice of reflection coefficients (or optimal RIS pattern) can be efficiently determined for an ideal unit-amplitude unconstrained phase alphabet. However, for most practical RIS that entail finite alphabets with amplitude imbalance and per-group-of-elements control, efficiently determining optimal patterns remain open problems. In this paper we resolve two such open problems by designing optimal and efficient pattern determination algorithms for binary and quaternary alphabets. We show that our algorithms can noticeably improve over the state-of-art conventional heuristic, especially in the presence of high amplitude imbalance and more restrictive per-group control. The designed algorithms also yield companion sets, which we show offer very significant advantages in RIS pattern selection under interference limit (leakage suppression) constraints.

关键词： Discrete Optimization Finite alphabet optimal algorithm Reconfigurable Intelligent Surface

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