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An optimization Model for Electric Vehicle Battery Charging at a Battery Swapping Station

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2018年第2期67卷 881-895页

作者： Wu, Hao Pang, Grantham Kwok Hung Choy, King Lun Lam, Hoi Yan Univ Hong Kong Dept Elect & Elect Engn Hong Kong Hong Kong Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Hong Kong Peoples R China

A new model for a viable battery swapping station is proposed to minimize its cost by determining the optimized charging schedule for swapped electric vehicle (EV) batteries. The aim is to minimize an objective function considering three factors: the number of batteries taken from stock to serve all the swapping orders from incoming EVs, potential charging damage with the use of high-rate chargers, and electricity cost for different time period of the day. A mathematical model is formulated for the charging process following the constant-current/constant-voltage charging strategy. An integrated algorithm is proposed to determine an optimal charging schedule, which is inspired by genetic algorithm, differential evolution, and particle swarm optimization. A series of simulation studies are executed to assess the feasibility of the proposed model and compare the performance between the proposed algorithm and the typical evolutionary algorithms.

关键词： Battery swapping stations battery charging electric vehicles optimization algorithms

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optimization of the separation of ionizable compounds in micellar electrokinetic chromatography by simultaneous change of pH and SDS concentration

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ELECTROPHORESIS 2007年第20期28卷 3712-3721页

作者： Tellez, Adolfo Fuguet, Elisabet Roses, Marti Univ Barcelona Dept Quim Anal E-08028 Barcelona Spain

A method to optimize the separation in micellar EKC (MEKC) of mixtures of acidic compounds as a function of two parameters, pH and concentration of sodium dodecyl sulfate, has been developed. The method considers the prediction of the retention time and the shape of the peaks. The retention time is predicted from the retention factor model and the peak shape by a polynomically modified Gaussian function that considers peak width, asymmetry factor, and height. An algorithm to calculate the global resolution of the separation at any experimental pH and [SDS] has been applied. This algorithm provides a 3-D resolution map to easily detect the areas in which resolution for the separation of the compounds is maximum. Initial experiments to fit the models have been performed with a set of ten phenolic compounds with different hydrophobicities and pK(a) values, and therefore, expected to behave in a different way with changes of pH and surfactant concentration. The experiments encompassed a pH range from 6.7 to 11.1, and a sodium dodecyl sulfate concentration range from 40 to 80 mM. Through the proposed methodology, chromatograms have been simulated at different pH and [SDS] very accurately. Furthermore, the resolution at any experimental point within the studied ranges have been also calculated, giving an optimum resolution value at pH 6.7 and [SDS] = 72 mM.

关键词： ionizable compounds MEKC optimization algorithms resolution simulation of chromatograms

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GTAdam: Gradient Tracking With Adaptive Momentum for Distributed Online optimization

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2023年第3期10卷 1436-1448页

作者： Carnevale, Guido Farina, Francesco Notarnicola, Ivano Notarstefano, Giuseppe Univ Bologna Dept Elect Elect & Informat Engn I-40126 Bologna Italy

This article deals with a network of computing agents aiming to solve an online optimization problem in a distributed fashion, i.e., by means of local computation and communication, without any central coordinator. We propose the gradient tracking with an adaptive momentum estimation (GTAdam) distributed algorithm, which combines a gradient tracking mechanism with first- and second-order momentum estimates of the gradient. The algorithm is analyzed in the online setting for strongly convex cost functions with Lipschitz continuous gradients. We provide an upper bound for the dynamic regret given by a term related to the initial conditions and another term related to the temporal variations of the objective functions. Moreover, a linear convergence rate is guaranteed in the static setup. The algorithm is tested on a time-varying classification problem, on a (moving) target localization problem, and in a stochastic optimization setup from image classification. In these numerical experiments from multiagent learning, GTAdam outperforms state-of-the-art distributed optimization methods.

关键词： Distributed algorithms Distributed optimization multiagent systems networked systems optimization algorithms

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optimization of a Printed UWB Antenna Application of the invasive weed optimization algorithm in antenna design

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IEEE ANTENNAS AND PROPAGATION MAGAZINE 2017年第1期59卷 48-57页

作者： Dastranj, Aliakbar Univ Yasuj Yasuj Iran

This article presents a general approach to design and optimize printed ultrawideband (UWB) antennas by using invasive weed optimization (IWO), a well-known global optimization algorithm. To achieve the required radiation parameters over a wide bandwidth, a frequencyrelated cost function with optimal weighting coefficients is suggested. Two prototypes of the optimized antenna have been manufactured and examined. The experimental outcomes show good agreement with the simulated ones that validate the proposed optimization approach. The optimized antenna has a compact size of 50 mm × 50 mm. The operational bandwidth of the antenna for S₁₁ < -10 dB, from both measurement and simulation, is 150%, based on the center frequency of 6.4 GHz (1.6-11.2 GHz), which also covers the UWB (3.1-10.6 GHz) applications. The time-domain response of the antenna was also investigated by measurement of the group delay. Finally, the efficiency of the optimized antenna in terms of bandwidth, size, and gain are compared with a number of previously proposed designs. Comparison results show that the optimized antenna outperforms other designs cited. The experimental outcomes in frequency as well as the time domain show that the antenna is suitable for use in UWB or other communication systems.

关键词： Invasive Weeds Ultra wideband antennas antenna design optimization algorithms

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Determination of minimum-time maneuvers for a robotic manipulator using numerical optimization methods

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MECHANICS OF STRUCTURES AND MACHINES 1999年第2期27卷 185-201页

作者： Heilig, J McPhee, J Univ Waterloo Waterloo ON N2L 3G1 Canada

The productivity of automated production lines depends on the velocities of operating robot manipulators. Hence, time-optimal control for working cycles of robot manipulators are of decisive importance. In this paper, the minimum-time retraction of a robot arm, subject to gravity and suspended on a prismatic-rotational joint, is investigated. Iterative integration methods using a B-spline representation for the actuator force and torque, a Runge-Kutta integration method, and a sequential quadratic programming optimization algorithm are used to calculate the time-optimal control and trajectories. Results show that nonlinear gravity and centrifugal effects are exploited very effectively, to obtain minimum-time maneuvers from an initial to a final state. These states also determine the switching structures of the control. It is demonstrated that even the simple retraction of a robot arm produces unexpectedly complex time-optimal solutions.

关键词： time optimal control Integration Methods Robot Manipulator optimization methods Gravity robotic arm splines (mathematics) optimization algorithms Robot porduction automation optimization method

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Code optimization for direct sequence spread spectrum and SAW-matched filter implementation

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IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 2000年第4期47卷 974-983页

作者： Hikino, O Belkerdid, MA Malocha, DC Hitachi Media Elect Yokohama Kanagawa 244 Japan Terion Inc Melbourne FL 32935 USA Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

This paper introduces a new optimization algorithm for the minimization of the time sidelobes of the correlation function of a pseudonoise (PN) sequence by applying dynamic weighting to the sequence. The resulting optimized time sidelobe level sequences are to be used ill direct sequence spread spectrum (DS-SS) systems with digital modulations such as BPSK, DPSK, QPSK, etc. The new optimization algorithm starts with a PN sequence. It first optimizes the correlation time sidelobes for the case where the consecutive data bits are identical (11 or 00), It then optimizes the correlation time sidelobes for the case of alternating consecutive data bits (10 or 01), The suppressed time sidelobe level sequences are derived by iterating these algorithms alternately starting from the initial PN sequence. The derived suppressed time sidelobe sequences show excellent correlation characteristics when compared to conventional PN sequences such as maximal length sequences, Gold sequences and Barker codes. Surface acoustic wave (SAW) devices were used to implement the optimized time sidelobe level sequences in a matched filter pair. The design of the apodized SAW-matched filters and their predicted second order effects are presented. The experimental results for the SAW-matched filters for the optimized time sidelobe level sequences derived from a Barker code were found to be in good agreement with the theoretical predictions from this new algorithm.

关键词： correlation methods differential phase shift keying matched filters minimisation pseudonoise codes quadrature phase shift keying sequences spread spectrum communication surface acoustic wave filters BPSK Barker code DPSK DS-SS systems PN sequence QPSK SA optimization algorithms Quadrature phase shift keying matched filters correlation methods differential phase shift keying Pseudonoise sequences pseudonoise codes surface acoustic wave filters Binary phase shift keying Spread spectrum communication

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A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting

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ALEXANDRIA ENGINEERING JOURNAL 2022年第1期61卷 279-303页

作者： Ibrahim, Karim Sherif Mostafa Hassan Huang, Yuk Feng Ahmed, Ali Najah Koo, Chai Hoon El-Shafie, Ahmed Univ Tunku Abdul Rahman UTAR Lee Kong Chian Fac Engn & Sci Sungai Long Campus Kajang 43000 Selangor Malaysia Univ Tenaga Natl UNITEN Inst Energy Infrastruct IEI Kajang 43000 Selangor Malaysia Univ Malaya Dept Civil Engn Fac Engn Kuala Lumpur Malaysia United Arab Emirates Univ Natl Water & Energy Ctr Al Ain POB 15551 Al Ain 15551 U Arab Emirates

Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has further generated immense interest in researching aspects for further improvements to hydrology. This can be seen in the rising number of related works published. This culminated further with the combination of pioneering optimization techniques. Who would have thought that the birds and the bees can offer advances in the mathematical sciences and so have the ants too? The ingenuity of humans is spelled out in the algorithms that mimic many natural activities, like pack hunting by the wolves! This review paper serves to broadcast more of the intriguing interest in newfound procedures in optimal forecasting. Reservoirs are the main and most efficient water storage facilities for managing uneven water distribution. However, due to the major global climate changes which affect rainfall trend and weather, it has been a necessity to find an alternative solution for effective conventional water balance. A multifunctional reservoir operation appears to require the operator to make wise decisions to achieve an optimal reservoir operation. One of the most important aspects of all this is the forecasting of streamflows. For this, Artificial Intelligence (AI) seems to be the best alternative solution;as in the past three decades, there has been a drastic increase in building and developing AI models for forecasting and modelling unstable patterns in various hydrological fields. Nevertheless, AI models are also required to be optimized in tandem to achieve the best result, leading thus to the desirous forming of hybrid models between a standalone AI model and optimization techniques. This comprehensive study categorizes machine learning into three main categories, together with the optimization techniques, and will next explore the various AI model used for different hydrology fields along with the most common optimization techniques. Summarization of findings under every section is

关键词： Artificial intelligence Artificial Neural Network (ANN) Support Vector Machine (SVM) Adaptive Neuro-Fuzzy Inference System (ANFIS) optimization algorithms Genetic algorithms (GA) Particle Swarm optimization (PSO) Artificial Bee Colony (ABC) Reservoir Inflows Streamflow forecasting

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New optimization algorithm for the synthesis of rugate optical coatings

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APPLIED OPTICS 2006年第7期45卷 1515-1524页

作者： Tikhonravov, AV Trubetskov, MK Amotchkina, TV Kokarev, MA Kaiser, N Stenzel, O Wilbrandt, S Gäbler, D Moscow MV Lomonosov State Univ Ctr Res Comp Moscow 119992 Russia Fraunhofer Inst Angew Opt & Feinmachan Dept Opt Coatings D-07745 Jena Germany

A model of a rugate coating that takes into account production potentialities of the Leybold Syrus Pro 1100 deposition system is presented. An efficient algorithm for the synthesis of rugate coatings is proposed. Nume... 详细信息

关键词： efficient algorithm optimization algorithms Proline Economic Resources synthesis Optical coatings

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A modified second-order SPSA optimization algorithm for finite samples

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2002年第5期16卷 397-409页

作者： Zhu, X Spall, JC Johns Hopkins Univ Appl Phys Lab Laurel MD 20723 USA

We propose a modification to the simultaneous perturbation stochastic approximation (SPSA) methods based on the comparisons made between the first- and second-order SPSA (I SPSA and 2SPSA) algorithms from the perspective of loss function Hessian. At finite iterations, the accuracy of the algorithm depends on the matrix conditioning of the loss function Hessian. The error of 2SPSA algorithm for a loss function with an ill-conditioned Hessian is greater than the one with a well-conditioned Hessian. On the other hand, the I SPSA algorithm is less sensitive to the matrix conditioning of loss function Hessians. The modified 2SPSA (M2SPSA) eliminates the error amplification caused by the inversion of an ill-conditioned Hessian. This leads to significant improvements in its algorithm efficiency in problems with an ill-conditioned Hessian matrix. Asymptotically, the efficiency analysis shows that M2SPSA is also superior to 2SPSA in a large parameter domain. It is shown that the ratio of the mean square errors for M2SPSA to 2SPSA is always less than one except for a perfectly conditioned Hessian or for an asymptotically optimal setting of the gain sequence. Copyright (C) 2002 John Wiley Sons, Ltd.

关键词： Hessian Finite Conditioning algorithms LOSS FUNCTION Perspective optimization algorithms

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Framework for field optimization to maximize asset value

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SPE RESERVOIR EVALUATION & ENGINEERING 2005年第1期8卷 7-21页

作者： Bailey, WJ Couët, B Wilkinson, D Schlumberger Doll Res Ctr Risk & Optimizat Program Ridgefield CT USA

In this paper, we propose an optimization framework for maximizing asset value, both with and without uncertainty. We first present the methodology to treat a general control optimization in the presence of uncertainty, followed by a brief section on the optimization algorithms used. We then describe the field model example used to illustrate the application of the methodology. Through a systematic analysis of various deterministic and stochastic cases, we address the various objectives sought. Using net present value (NPV) as a measure, we also explore the valuation of advanced completions along with the returns gained from expanding surface gas-handling facilities. The method also generates an efficient frontier that can be used for risk and decision analysis. The results clearly demonstrate the value of such a framework for value maximization in planning both near- and long-term time horizons as well as providing the necessary foundation for maximizing asset value.

关键词： cost accounting Frameworks deterministic Maximize Completion optimization algorithms System analyses Uncertainty Gas surfacing control optimization Decision analysis

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