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Private and Hot-Pluggable Distributed Averaging

IEEE CONTROL SYSTEMS LETTERS 2020年第4期4卷 988-993页

作者： Ridgley, Israel L. Donato Freeman, Randy A. Lynch, Kevin M. Northwestern Univ Dept Elect & Comp Engn Evanston IL 60208 USA Northwestern Univ Northwestern Inst Complex Syst Evanston IL 60208 USA Northwestern Univ Ctr Robot & Biosyst Evanston IL 60208 USA Northwestern Univ Dept Mech Engn Evanston IL 60208 USA

Some distributed optimization applications require privacy, meaning that the values of certain parameters local to a node should not be revealed to other nodes in the network during the joint optimization process. A special case is the problem of private distributed averaging, in which a network of nodes computes the global average of individual node reference values in a distributed manner while preserving the privacy of each reference. We present simple iterative methods that guarantee accuracy (i.e., the exact asymptotic computation of the global average) and privacy (i.e., no node can estimate another node's reference value). To achieve this, we require that the digraph modeling the communication between nodes satisfy certain topological conditions. Our method is hot-pluggable (meaning no reinitialization of the averaging process is required when the network changes or a node enters or leaves, when there is a communication or computation fault, or when a node's reference value changes);it does not require an initial scrambling phase;it does not inject noise or other masking signals into the distributed computation;it does not require random switching of edge weights;and it does not rely on homomorphic encryption.

关键词： Privacy optimization Encryption Convergence Standards Protocols Heuristic algorithms optimization algorithms distributed control network analysis and control

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Hardware-in-the-loop testing of grid-tied PMSG-based wind power generation system with optimum PI parameters

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2023年 5卷

作者： Tripathi, S.M. Singh, U.P. Singh, Sonu Rai, N.K. Himani Srivastava, A.K. Power & Energy Research Centre (PERC) Department of Electrical Engineering Kamla Nehru Institute of Technology (U.P.) Sultanpur India Rail Vikas Nigam Limited U.P. Pilibhit India Indian Railways India Department of Electrical Engineering Institute of Engineering and Technology Dr. Rammanohar Lohia Avadh University U.P. Ayodhya India

The regulators based on PI control law continue to be the key elements in many of the industrial systems for their control. Likewise, the wind power generation systems (WPGSs) also make extensive use of PI regulators in their control schemes. The enhanced performance of these WPGS depends on proper selection of the PI regulator parameters. This paper deals with the control of a grid-tied permanent magnet synchronous generator (PMSG)-based WPGS wherein, a new attempt has been depicted to apply the most optimum design of the involved PI regulator parameters for the proposed WPGS based on standard performance indices making use of four popular optimization algorithms namely genetic algorithm (GA), cultural algorithm (CA), particle swarm optimization (PSO) and artificial bee colony (ABC). An informative discussion has also been presented which would be useful for practicing engineers/researchers to select flexibly and reasonably the PI regulators parameters meant for the control of the proposed WPGS. A detailed simulation model developed in MATLAB/Simulink has been used to analyze the performance of the proposed PMSG-based WPGS employed with the most optimum values of PI regulator parameters. The performances of WPGS have been compared while the most optimum PI regulator parameters have been included in the control system, and also when incorporating the PI regulator parameters in WPGS control designed via classical D-partition technique. The results obtained under gradually changing wind speed profile show the improvement in the performance of WPGS in terms of peak overshoot, time response and waveform oscillations. The experimental validation of the control performances have been carried out by way of real-time hardware-in-the-loop (HIL) testing making use of Typhoon HIL402 emulator and TMS320F28335 digital signal controller. The obtained real time HIL results are in close agreement to the results obtained in simulations using MATLAB/Simulink. A deviation of less than

关键词： Hardware-in-the-loop testing optimization algorithms Permanent magnet synchronous generator (PMSG) PI regulator Wind power generation systems (WPGS)

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Optimized neural networks in industrial data analysis

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SN APPLIED SCIENCES 2020年第2期2卷 300页

作者： Caballero, Liesle Jojoa, Mario Percybrooks, Winston S. Univ Norte Barranquilla Colombia Inst Univ ITSA Barranquilla Colombia

The research work presented here tackles the problem of finding the optimum value for hyper-parameters, such as number of layers and number of neurons per layer, for a fully-connected Artificial Neural Network (ANN), particularly in regression problems. A proposed optimization strategy is tested on different datasets related to diverse industrial applications: (1) prediction of the performance of exploration algorithms for mobile robots, (2) prediction of the compressive strength of concrete, (3) prediction of energy output from a power plant;and (4) prediction of wine quality. Different evaluation metrics, such as Pearson correlation coefficient (R), Square Correlation Coefficient (R-2), Absolute Relative Error ( RAE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), are used to determine the best performing prediction model when the hyper-parameter optimization algorithms are used. That result is compared to the performance of the best model previously reported in the literature on the same dataset in order to determine the gains achieved by using the hyper-parameter optimization strategy under test.

关键词： Machine learning Artificial neural network optimization algorithms Hyper-parameters

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A Distributed Algorithm That Finds Almost Best Possible Estimate Under Non-Vanishing and Time-Varying Measurement Noise

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IEEE CONTROL SYSTEMS LETTERS 2020年第1期4卷 229-234页

作者： Lee, Jin Gyu Shim, Hyungbo Seoul Natl Univ Dept Elect & Comp Engn ASRI Seoul 08826 South Korea

In this letter, we review an existing distributed least-squares solver and share some new insights on it. Then, by the observation that an estimation of a constant vector under output noise can be translated into finding the least-squares solution, we present an algorithm for distributed estimation of the state of linear time-invariant systems under measurement noise. The proposed algorithm consists of a network of local observers, where each of them utilizes local measurements and information transmitted from the neighbors. It is proven that even under non-vanishing and time-varying measurement noise, we could obtain an almost best possible estimate with arbitrary precision. Some discussions regarding the plug-and-play operation are also given.

关键词： Estimation sensor networks optimization algorithms large-scale systems observers for linear systems

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Hybrid approach for solving the integrated planning and scheduling production problem

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JOURNAL OF ENGINEERING DESIGN AND TECHNOLOGY 2020年第1期18卷 172-189页

作者： Ibn Majdoub Hassani, Zineb El Barkany, Abdellah Jabri, Abdelouahhab El Abbassi, Ikram Darcherif, Abdel Moumen Fac Sci & Technol Fez Mech Engn Lab Fes Morocco ECAM EPMI LR2E Cergy Pontoise France ECAM EPMI Grad Sch Cergy Pontoise France

Purpose This paper aims to present a new model for solving the integrated production planning and scheduling. Usually, the two decision levels are treated sequentially because of their complexity. Scheduling depends on the lot sizes calculated at the tactical level and ignoring scheduling constraints generates unrealistic and inconsistent decisions. Therefore, integrating more detail scheduling constraint in production planning is important for managing efficiently operations. Therefore, an integrated model was developed, and two evolutionary optimization approaches were suggested for solving it, namely, genetic algorithm (GA) and the hybridization of simulated annealing (SA) with GA HSAGA. The proposed algorithms have some parameters that must be adjusted using Taguchi method. Therefore, to evaluate the proposed algorithm, the authors compared the results given by GA and the hybridization. The SA-based local search is embedded into a GA search mechanism to move the GA away from being closed within local optima. The analysis shows that the combination of simulated annealing with GA gives better solutions and minimizes the total production costs. Design/methodology/approach The paper opted for an approached resolution method particularly GA and simulated annealing. The study represents a comparison between the results found using GA and the hybridization of simulated annealing and GA. A total of 45 instances were studied to evaluate job-shop problems of different sizes. Findings The results illustrate that for 36 instances of 45, the hybridization of simulated annealing and GA HSAGA has provided best production costs. The efficiency demonstrated by HSAGA approach is related to the combination between the exploration ability of GA and the capacity to escape local optimum of simulated annealing. Originality/value This study provides a new resolution approach to the integration of planning and scheduling while considering a new operational constrain. The model suggested

关键词： Planning Operational research Modelling Genetic algorithms optimization algorithms Production Scheduling Mono criteria Integrated approach HSAGA Simulated annealing

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Exact Complexity Certification of a Nonnegative Least-Squares Method for Quadratic Programming

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IEEE CONTROL SYSTEMS LETTERS 2020年第4期4卷 1036-1041页

作者： Arnstrom, Daniel Bemporad, Alberto Axehill, Daniel Linkoping Univ Div Automat Control S-58183 Linkoping Sweden IMT Sch Adv Studies Lucca Dept Comp Sci & Engn I-55100 Lucca Italy

In this letter we propose a method to exactly certify the complexity of an active-set method which is based on reformulating strictly convex quadratic programs to nonnegative least-squares problems. The exact complexity of the method is determined by proving the correspondence between the method and a standard primal active-set method for quadratic programming applied to the dual of the quadratic program to be solved. Once this correspondence has been established, a complexity certification method which has already been established for the primal active-set method is used to also certify the complexity of the nonnegative least-squares method. The usefulness of the proposed method is illustrated on a multi-parametric quadratic program originating from model predictive control of an inverted pendulum.

关键词： optimization algorithms predictive control for linear systems

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Machine learning with bond information for local structure optimizations in surface science

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JOURNAL OF CHEMICAL PHYSICS 2020年第23期153卷 234116页

作者： del Rio, Estefania Garijo Kaappa, Sami Torres, Jose A. Garrido Bligaard, Thomas Jacobsen, Karsten Wedel Tech Univ Denmark Dept Phys Lyngby Denmark Stanford Univ Dept Chem Engn SUNCAT Ctr Interface Sci & Catalysis Stanford CA 94305 USA SLAC Natl Accelerator Lab 2575 Sand Hill Rd Menlo Pk CA 94025 USA Columbia Univ Dept Chem Engn Columbia Electrochem Energy Ctr New York NY 10027 USA Tech Univ Denmark Dept Energy Convers & Storage Lyngby Denmark

Local optimization of adsorption systems inherently involves different scales: within the substrate, within the molecule, and between the molecule and the substrate. In this work, we show how the explicit modeling of different characteristics of the bonds in these systems improves the performance of machine learning methods for optimization. We introduce an anisotropic kernel in the Gaussian process regression framework that guides the search for the local minimum, and we show its overall good performance across different types of atomic systems. The method shows a speed-up of up to a factor of two compared with the fastest standard optimization methods on adsorption systems. Additionally, we show that a limited memory approach is not only beneficial in terms of overall computational resources but can also result in a further reduction of energy and force calculations.

关键词： Editor's Pick Topics Gaussian processes Machine learning Potential energy surfaces Atomic structure optimization algorithms Density functional theory Adsorption Surface science

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Some Insights on Synthesizing Optimal Linear Quadratic Controllers Using Krotov Sufficient Conditions

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IEEE CONTROL SYSTEMS LETTERS 2020年第2期4卷 486-491页

作者： Kumar, Avinash Jain, Tushar Indian Inst Technol Mandi Sch Comp & Elect Engn Mandi 175005 Himachal Prades India

This letter revisits the problem of synthesizing the optimal control laws for linear systems with a quadratic cost. Traditionally, these laws are computed using the Hamilton-Jacobi-Bellman method, where the solution to the original problem is obtained by solving the Riccati equation, which hinges upon a priori information of the optimal cost function. Within the general Krotov global optimal control framework, though being less explored in the literature, that information is no longer needed. However, utilizing this framework, the original optimization problem is translated into a non-convex problem, which is solved by using iterative methods. In this letter, we propose a new method to compute a direct (non-iterative) solution by transforming the resulting non-convex optimization problem into a convex problem. It turns out that the proposed method naturally leads to the Riccati inequality as the crucial intermediate step, of which the origin was not well understood, although it serves as a strong backbone to address linear quadratic problems and other significant linear system theoretic results. Numerical results and future directions, particularly for solving the optimal control problem for bilinear systems, are also provided to demonstrate the usability of the proposed method.

关键词： Optimal control LMIs optimization algorithms linear systems time-varying systems

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Unified Convergence Analysis for Adaptive optimization with Moving Average Estimator

arXiv

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arXiv 2021年

作者： Guo, Zhishuai Xu, Yi Yin, Wotao Jin, Rong Yang, Tianbao Texas A&M University College StationTX77840 United States Dalian University of Technology Liaoning Dalian116024 China Alibaba Group BellevueWA98004 United States

Although adaptive optimization algorithms have been successful in many applications, there are still some mysteries in terms of convergence analysis that have not been unraveled. This paper provides a novel non-convex analysis of adaptive optimization to uncover some of these mysteries. Our contributions are three-fold. First, we show that an increasing or large enough momentum parameter for the first-order moment used in practice is sufficient to ensure the convergence of adaptive algorithms whose adaptive scaling factors of the step size are bounded. Second, our analysis gives insights for practical implementations, e.g., increasing the momentum parameter in a stage-wise manner in accordance with stagewise decreasing step size would help improve the convergence. Third, the modular nature of our analysis allows its extension to solving other optimization problems, e.g., compositional, min-max and bilevel problems. As an interesting yet non-trivial use case, we present algorithms for solving non-convex min-max optimization and bilevel optimization that do not require using large batches of data to estimate gradients or double loops as the literature do. Our empirical studies corroborate our theoretical results. © 2021, CC BY.

关键词： optimization algorithms

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Competition on Dynamic optimization Problems Generated by Generalized Moving Peaks Benchmark (GMPB)

arXiv

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arXiv 2021年

作者： Yazdani, Danial Mavrovouniotis, Michalis Li, Changhe Chen, Guoyu Luo, Wenjian Omidvar, Mohammad Nabi Branke, Jürgen Yang, Shengxiang Yao, Xin The Business Intelligence Team WINC Australia Cyprus University of Technology Lemesos Cyprus ERATOSTHENES Centre of Excellence Cyprus The School of Artificial Intelligence Anhui University of Sciences & Technology Hefei China The Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory Shenzhen518055 China The School of Computing University of Leeds Leeds University Business School LeedsLS2 9JT United Kingdom The Operational Research and Management Sciences Group at Warwick Business school University of Warwick CoventryCV4 7AL United Kingdom School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom The Department of Computing and Decision Sciences Lingnan University Hong Kong The CERCIA School of Computer Science BirminghamB15 2TT United Kingdom

The Generalized Moving Peaks Benchmark (GMPB) [1] is a tool for generating continuous dynamic optimization problem instances with controllable dynamic and morphological characteristics. GMPB has been used in recent Competitions on Dynamic optimization at prestigious conferences, such as the IEEE Congress on Evolutionary Computation (CEC). This dynamic benchmark generator can create a wide variety of landscapes, ranging from simple unimodal to highly complex multimodal configurations, and from symmetric to asymmetric forms. It also supports diverse surface textures, from smooth to highly irregular, and can generate varying levels of variable interaction and conditioning. This document provides an overview of GMPB, emphasizing how its parameters can be adjusted to produce landscapes with customizable characteristics. The MATLAB implementation of GMPB is available on the EDOLAB platform [2]. © 2021, CC BY-NC-ND.

关键词： optimization algorithms

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