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Using optimization algorithms of DEA and Grey system theory in strategic partner selection: An empirical study in Vietnam steel industry

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COGENT BUSINESS & MANAGEMENT 2020年第1期7卷 Article: 1832810页

作者： Nguyen, Phu Nguyen, Nhu Ty Vietnam Natl Univ HoChiMinh Int Univ Sch Business Hochiminh Vietnam

In the current market economy, alliances play a key role in developing strategies across fields. In order to have a good partner, managers have used both qualitative and quantitative methodologies. This paper proposes a mathematical model to figure out the most suitable strategic partners. With input data from published financial reports, the authors use the data envelopment analysis (DEA) to evaluate the business efficiency of the steel companies in the period of 2011-2019. Then, Grey system theory is applied to predict their performance in the future period. The findings recommend the two leading steel manufactures but having ineffective performance, the Hoa Sen Group, and the Pomina Steel Corporation, as the most feasible beneficial partnership. Managers and the government can take advantages of the model in order to implement and have overall plans of steel enterprise in the future.

关键词： strategic alliance optimization algorithms Grey forecasting model Data Envelopment Analysis (DEA) business performance

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optimization of Denoising algorithms 3

Optimization of Denoising Algorithms

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3rd International Conference on Artificial Intelligence and Computer Information Technology, AICIT 2024

作者： Yang, Fan Wang, Shixuan Wuhan Institute of Technology School of Electrical and Information Engineering Wuhan China

ISBN: (纸本)9798350365443

In the field of image processing, the three-dimensional block matching algorithm (BM3D) algorithm combines the relevant characteristics of the spatial domain and the frequency domain, and it is one of the algorithms with the best image denoising effect at present, but its denoising thresholds are always qualitative thresholds for different noisy images, which often results in the loss of details in the process of image processing that requires high accuracy, and the noise of the image is not completely removed. This paper proposes a way to optimize the threshold value based on the sand cat swarm algorithm, through the optimization of the threshold value in BM3D, so that the generated threshold curve is more suitable for the threshold value needed by the actual image. The experimental results show that the optimization algorithm denoises the image with reduced running time and improved image quality compared to the original algorithm denoises the image, so that the peak signal-to-noise ratio of the image at a noise standard deviation of 25 is improved from 19.7256 to 21.7862. © 2024 IEEE.

关键词： optimization algorithms

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Data from multimodal functions based on an array of photovoltaic modules and an approximation with artificial neural networks as a scenario for testing optimization algorithms

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DATA IN BRIEF 2019年 27卷 104669-104669页

作者： Robles-Algarin, Carlos Restrepo-Leal, Diego Ospino Castro, Adalberto Univ Magdalena Fac Ingn Carrera 32 22-08 Santa Marta Colombia Univ Costa Fac Ingn Calle 58 55-66 Barranquilla Colombia

This paper presents the data of multimodal functions that emulate the performance of an array of five photovoltaic modules under partial shading conditions. These functions were obtained through mathematical modeling and represent the P-V curves of a photovoltaic module with several local maximums and a global maximum. In addition, data from a feedforward neural network are shown, which represent an approximation of the multimodal functions that were obtained with mathematical modeling. The modeling of multimodal functions, the architecture of the neural network and the use of the data were discussed in our previous work entitled "Search for Global Maxima in Multimodal Functions by Applying Numerical optimization algorithms: A Comparison Between Golden Section and Simulated Annealing" [1]. Data were obtained through simulations in a C code, which were exported to DAT files and subsequently organized into four Excel tables. Each table shows the voltage and power data for the five modules of the photovoltaic array, for multimodal functions and for the approximation of the multimodal functions implemented by the artificial neural network. In this way, a dataset that can be used to evaluate the performance of optimization algorithms and system identification techniques applied in multimodal functions was obtained. (C) 2019 The Author(s). Published by Elsevier Inc.

关键词： Artificial neural networks Multimodal functions optimization algorithms Partial shading Photovoltaic modules

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Decision-focused predictions via pessimistic bilevel optimization: complexity and algorithms

arXiv

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arXiv 2023年

作者： Bucarey, Víctor Calderón, Sophia Muñoz, Gonzalo Semet, Frédéric Engineering Sciences Institute Universidad de O’Higgins Chile Engineering School Universidad de O’Higgins Chile Industrial Engineering Department Universidad de Chile Chile Chile Univ. Lille CNRS Inria Centrale Lille UMR 9189 CRIStAL LilleF-59000 France

Dealing with uncertainty in optimization parameters is an important and longstanding challenge. Typically, uncertain parameters are predicted accurately, and then a deterministic optimization problem is solved. However, the decisions produced by this so-called predict-then-optimize procedure can be highly sensitive to uncertain parameters. In this work, we contribute to recent efforts in producing decision-focused predictions, i.e., to build predictive models that are constructed with the goal of minimizing a regret measure on the decisions taken with them. We begin by formulating the exact expected regret minimization as a pessimistic bilevel optimization model. Then, we establish NP-completeness of this problem in a heavily restricted case. Using duality arguments, we reformulate it as a non-convex quadratic optimization problem. Finally, leveraging the quadratic reformulation, we show various computational techniques to achieve empirical tractability. We report extensive computational results on shortest-path and bipartite matching instances with uncertain cost vectors. Our results indicate that our approach can improve training performance over the approach of Elmachtoub and Grigas (2022), a state-of-the-art method for decision-focused *** Codes 90C30 © 2023, CC BY-NC-ND.

关键词： optimization algorithms

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Online Mixed Discrete and Continuous optimization: algorithms, Regret Analysis and Applications

arXiv

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arXiv 2023年

作者： Ye, Lintao Chi, Ming Liu, Zhi-Wei Wang, Xiaoling Gupta, Vijay School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China College of Automation College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing210023 China The Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States

We study an online mixed discrete and continuous optimization problem where a decision maker interacts with an unknown environment for a number of T rounds. At each round, the decision maker needs to first jointly choose a discrete and a continuous actions and then receives a reward associated with the chosen actions. The goal for the decision maker is to maximize the accumulative reward after T rounds. We propose algorithms to solve the online mixed discrete and continuous optimization problem and prove that the algorithms yield sublinear regret in T. We show that a wide range of applications in practice fit into the framework of the online mixed discrete and continuous optimization problem, and apply the proposed algorithms to solve these applications with regret guarantees. We validate our theoretical results with numerical experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： optimization algorithms

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Tuning-Free Bilevel optimization: New algorithms and Convergence Analysis

arXiv

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arXiv 2024年

作者： Yang, Yifan Ban, Hao Huang, Minhui Ma, Shiqian Ji, Kaiyi Department of Computer Science and Engineering University at Buffalo United States AI Research for Monetization Meta United States Department of Computational Applied Math and Operations Research Rice University United States

Bilevel optimization has recently attracted considerable attention due to its abundant applications in machine learning problems. However, existing methods rely on prior knowledge of problem parameters to determine stepsizes, resulting in significant effort in tuning stepsizes when these parameters are unknown. In this paper, we propose two novel tuning-free algorithms, D-TFBO and S-TFBO. D-TFBO employs a double-loop structure with stepsizes adaptively adjusted by the "inverse of cumulative gradient norms" strategy. S-TFBO features a simpler fully single-loop structure that updates three variables simultaneously with a theory-motivated joint design of adaptive stepsizes for all variables. We provide a comprehensive convergence analysis for both algorithms and show that D-TFBO and S-TFBO respectively require (Equation presented) and (Equation presented) iterations to find an ϵ-accurate stationary point, (nearly) matching their well-tuned counterparts using the information of problem parameters. Experiments on various problems show that our methods achieve performance comparable to existing well-tuned approaches, while being more robust to the selection of initial stepsizes. To the best of our knowledge, our methods are the first to completely eliminate the need for stepsize tuning, while achieving theoretical guarantees. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

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IMPROVEMENT AND optimization OF MACHINE LEARNING algorithms BASED ON INTELLIGENT COMPUTING

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Scalable Computing 2025年第3期26卷 1403-1411页

作者： Fu, Jindi Liu, Xiaojie Ma, Pengkai Song, Changxin Keyi College Zhejiang Sci-Tech University Shaoxing312369 China Shanghai Urban Construction Vocational College Shanghai201415 China Hefei University of Technology Xuancheng242000 China

In this paper, a sensor cloud data intrusion detection framework is proposed. The framework uses parallel discrete optimization techniques for feature refining and incorporates machine learning principles to improve sensing cloud security. Firstly, a set of optimal feature evaluation criteria is established, and a parallel discrete optimization feature extraction system is built to reduce the data dimension and strengthen the stability of feature processing. Then, a widely used discrete optimization algorithm is developed, proving its global convergence. The optimal feature set is obtained through parallel screening feature subsets. Finally, using these features and distributed fuzzy cluster analysis, the intrusion behavior of the sensing cloud is accurately detected. This method incorporates the concept of intelligent iterative evolution and self-regulating clustering strategy, which not only overcomes the local optimal trap that the conventional fuzzy clustering algorithm may encounter but also realizes the automatic adjustment of the number of clusters. The experimental data show that the intrusion detection algorithm performs excellently in providing accurate intrusion determination results. Compared with other detection algorithms, the accuracy of anomaly detection and the reduction of missing detection rate is significantly improved. In addition, the algorithm shows anti-interference solid ability and can maintain stable performance in noisy environments. © 2025 SCPE.

关键词： optimization algorithms

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Fixed-Time Distributed optimization via Edge-Based Adaptive algorithms

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3436-3448页

作者： Ma, Lanlan Hu, Cheng Wen, Shiping Yu, Zhiyong Jiang, Haijun Xinjiang University College of Mathematics and System Science Ürümqi830017 China Xinjiang University College of Mathematics and System Science Xinjiang Key Laboratory of Applied Mathematics Ürümqi830017 China University of Technology Sydney Centre for Artificial Intelligence UltimoNSW2007 Australia Yili Normal University School of Mathematics and Statistics Yining835000 China

This article presents two fixed-time (FXT) distributed adaptive algorithms to solve a class of convex optimization problems for multiagent systems. First, a distributed adaptive protocol based on edge weights is developed to achieve global FXT optimization, in which the initial states are the local optimal points. Subsequently, an adaptive power-law algorithm is designed to realize local FXT optimization for each agent with arbitrary initial state. In the convergence analysis, unlike previous analysis method based on Lyapunov FXT stability criteria, this study employs the definition of FXT stability with Laplace transformation and a method of contradiction, several sufficient conditions are obtained to ensure that the states of all agents converge to the global optimal value within a fixed time, and the upper bound of convergence time is estimated. Furthermore, these adaptive algorithms on undirected graphs are extended to weight-balanced digraphs. Finally, the validity of the proposed edge-based adaptive distributed algorithms is demonstrated through numerical simulations of two packet-level charge-state balance problems. © 2013 IEEE.

关键词： optimization algorithms

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Prediction of water levels in large reservoirs base on optimization of deep learning algorithms

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EARTH SCIENCE INFORMATICS 2025年第1期18卷 1-15页

作者： Li, Haoran Zhang, Lili Yao, Yunsheng Zhang, Yaowen Inst Disaster Prevent Sanhe 065201 Hebei Peoples R China Hebei Key Lab Resource & Environm Disaster Mech & Sanhe 065201 Peoples R China

Water level forecasting is a critical technique for reservoir water resource management and flood early warning. This study addresses the limitations of the traditional Long Short-Term Memory (LSTM) network in terms of accuracy and generalization when handling complex hydrological data. To improve the precision and stability of LSTM in water level forecasting, four optimization algorithms-African Vulture optimization Algorithm (AVOA), Cuckoo Search (CS), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO)-were introduced to optimize the LSTM model. The study employed a self-developed RIL scoring standard to comprehensively evaluate the models' performance. The results show that all optimized models significantly outperformed the traditional LSTM model. Among them, the GWO-LSTM achieved the best performance in terms of accuracy, with a Mean Absolute Error (MAE) of 0.1043 m, a Root Mean Square Error (RMSE) of 0.1402 m, and the highest RIL score of 2.4364. The study confirms the effectiveness of combining optimization algorithms with LSTM models in water level forecasting, offering a method to significantly improve prediction accuracy. It also provides new directions for enhancing the model's generalization capability and adaptability. Accurate water level forecasting in large reservoirs not only provides a scientific basis for reservoir management but also has significant theoretical and practical implications for flood control, disaster mitigation, ecological protection, and the sustainable use of water resources.

关键词： Deep learning optimization algorithms Three Gorges Reservoir Water level prediction Spatio-temporal series

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algorithms FOR SOLVING optimization PROGRAMS INVOLVING DIFFERENCE OF CONVEX FUNCTIONS

Applied Set-Valued Analysis and Optimization

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Applied Set-Valued Analysis and optimization 2025年第1期7卷 71-91页

作者： Boufi, Karima Fadil, Abdessamad Roubi, Ahmed Institut Supérieur des Sciences de la Santé Laboratoire MISI Hassan First University of Settat Settat Morocco Faculté des Sciences et Techniques Laboratoire MISI Hassan First University of Settat Settat Morocco

We investigate constrained optimization problems involving difference of convex functions in the objective as well as in the constraint functions. We first associate with the considered problem a parametric convex one. Relations between the two problems are analyzed, and necessary optimality conditions are given for such problems. Using the parametric approach, we propose three kinds of methods. The first one, we call the DC method of centers, generates a sequence of convex problems that incorporate the constraints in their objective functions. The second one is a pure proximal point method, based on the DC method of centers and the proximal point algorithm idea. The third one is a family of proximal bundle methods that combine the previous cited parametric approach, the proximal point algorithm idea, and the bundle concept. These methods are globally convergent in the sense that they converge for every starting point, feasible, or infeasible. We show that every cluster point of the sequence of optimal solutions of these problems satisfies necessary optimality conditions of KKT criticality type. Finally, we end with some numerical tests to illustrate the behavior of the algorithm. ©2025 Applied Set-Valued Analysis and optimization.

关键词： optimization algorithms

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