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Determining the optimum installation of energy storage systems in railway electrical infrastructures by means of swarm and evolutionary optimization algorithms

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 124卷 106295-106295页

作者： Roch-Dupre, David Gonsalves, Tad Cucala, Asuncion P. Pecharroman, Ramon R. Lopez-Lopez, Alvaro J. Fernandez-Cardador, Antonio Comillas Pontifical Univ Madrid Spain Sophia Univ Tokyo Japan

The installation of wayside Energy Storage Systems (ESSs) in DC-electrified railway systems is one of the main measures to improve their energy efficiency. They store the excess of regenerated energy produced by the trains during the braking phases and give it back to the system when necessary. Nevertheless, the big cost of the associated installation can make railway operators hesitate about the convenience of the investment. Additionally, the decisions about the configuration of the installation (locations and sizes for the ESSs) are usually based on the previous experience of the railway operators or, at best, in assessments made with simulation tools with low accuracy. This paper proposes a model to optimize the profitability of the investment. nature-inspired optimization algorithms are applied in combination with a very realistic railway simulator. The flexibility of the natureinspired optimization algorithms, together with their ability to successfully deal with the computationally-intensive and highly non-linear and non-convex problem posed by the realistic railway simulator, makes them the perfect choice. Three different nature-inspired optimization algorithms have been selected and compared: the Genetic Algorithm (GA) as the main exponent of the evolutionary algorithms, the Particle Swarm optimization algorithm (PSO) as the main exponent of the swarm algorithms and the Fireworks Algorithm (FA) as another variant of the swarm algorithms. The algorithms have shown an excellent behavior, providing solutions that combine the increase of energy efficiency with a very good profitability of the installation required to obtain that increase.

关键词： nature-inspired optimization algorithms optimization of energy storage systems Railway power systems Railway simulation Energy efficiency

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A conceptual and practical comparison of PSO-style optimization algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 167卷 114430-114430页

作者： Tharwat, Alaa Schenck, Wolfram Univ Appl Sci FH Bielefeld Ctr Appl Data Sci Gutersloh CfADS Bielefeld Germany

optimization algorithms are widely employed for finding optimal solutions in many applications. Stochastic optimization algorithms including nature-inspired optimization algorithms are simple and easy to implement, and this is the reason why there is a growing interest in this research area. Recently, many nature-inspired optimization algorithms have been proposed for solving many optimization problems. Moreover, with the aim of improving the performance of optimization algorithms, some modifications were applied such as combining different algorithms and employing some sampling techniques for replacing critical parameters in the optimization algorithms. This paper compares five different widely used PSO-style optimization algorithms to investigate if there is a significant difference between them or not. Theoretically, we explain different PSO-style algorithms and discuss the similarities and differences between them. Practically, a number of experiments were conducted to compare these algorithms. Theoretical analysis and practical results prove that there is not any significant difference between the PSO-style algorithms regarding their performance.

关键词： Particle swarm optimization (PSO) nature-inspired optimization algorithms Swarm optimization algorithms Gray wolf optimization (GWO) Whale optimization algorithm (WOA) Sine-cosine algorithm (SCA) Bat algorithm

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Optimizing Finite-Difference Operator in Seismic Wave Numerical Modeling

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algorithms 2022年第4期15卷 132-132页

作者： Li, Hui Fang, Yuan Huang, Zhiguo Zhang, Mengyao Wei, Qing Beijing Univ Chem Technol Dept Comp Sci & Technol Beijing 100029 Peoples R China

The finite-difference method is widely used in seismic wave numerical simulation, imaging, and waveform inversion. In the finite-difference method, the finite difference operator is used to replace the differential operator approximately, which can be obtained by truncating the spatial convolution series. The properties of the truncated window function, such as the main and side lobes of the window function's amplitude response, determine the accuracy of finite-difference, which subsequently affects the seismic imaging and inversion results significantly. Although numerical dispersion is inevitable in this process, it can be suppressed more effectively by using higher precision finite-difference operators. In this paper, we use the krill herd algorithm, in contrast with the standard PSO and CDPSO (a variant of PSO), to optimize the finite-difference operator. Numerical simulation results verify that the krill herd algorithm has good performance in improving the precision of the differential operator.

关键词： seismic numerical modeling finite-difference scheme nature-inspired optimization algorithms particle swarm optimization center-decenter particle swarm optimization krill herd algorithm

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Image Segmentation Using Multilevel Thresholding: A Research Review

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2020年第1期44卷 1-29页

作者： Pare, S. Kumar, A. Singh, G. K. Bajaj, V. Indian Inst Informat Technol Design & Mfg Jabalpur 482005 MP India Indian Inst Technol Roorkee Dept Elect Engn Roorkee 247667 Uttrakhand India

Image segmentation is a basic problem in computer vision and various image processing applications. Over the years, commonly used image segmentation has become quite challenging because of its utilization in many applications. Image thresholding is one of the most exploited techniques to accomplish image segmentation. Multilevel thresholding is found to be most appropriate and well known among all the image segmentation techniques. The segmented image quality is based on the techniques incorporated to choose the threshold value. In this paper, an exhaustive survey has been carried out, considering both the general purpose and satellite images to cover the performance comparison of various image segmentation approaches based on meta-heuristics optimization algorithms, present in the literature for multilevel image thresholding. In addition, this paper also focuses on information theoretic approach-based objective criterion using different statistical properties such as between-class variance, entropy, moment and maximum likelihood for selecting multilevel thresholds. A list of 157 publications on the subject is also appended for quick reference.

关键词： Image segmentation Multilevel thresholding nature-inspired optimization algorithms Kapur's entropy Between-class variance Tsalli's entropy

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A Novel Social Insect optimization Algorithm for the Optimal Design of an Interior Permanent Magnet Synchronous Machine

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IEEE TRANSACTIONS ON MAGNETICS 2018年第12期54卷 1-6页

作者： Kwon, Hyuk-Sung Ro, Jong-Suk Jung, Hyun-Kyo Seoul Natl Univ Dept Elect & Comp Engn Seoul 08826 South Korea Chung Ang Univ Sch Elect & Elect Engn Seoul 06974 South Korea

optimization of an electric machine is a nonlinear multi-variable problem. For optimization of the nonlinear multi-variable problem, many function evaluations are required, which in turn requires much time. To address this problem, we propose a novel optimization algorithm of which the convergence speed, accuracy, and reliability are superior compared to those of widely used conventional algorithms. The performance of the proposed algorithm is verified through mathematical test functions and applied to a practical optimization scenario of cogging torque minimization for an interior permanent magnet synchronous machine.

关键词： Electric machine interior permanent magnet synchronous machine (IPMSM) multivariable problem nature-inspired optimization algorithms optimization algorithm

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Efficient leader exchange for Migrating Birds optimization when solving Machine-Part Cell Formation Problems 11

Efficient leader exchange for Migrating Birds Optimization w...

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11th Iberian Conference on Information Systems and Technologies (CISTI)

作者： Soto, Ricardo Crawford, Broderick Almonacid, Boris Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Autonoma Chile Santiago Chile Univ Cient Sur Lima Peru Univ San Sebastian Santiago Chile Univ Cent Chile Santiago Chile

ISBN: (纸本)9789899843462

The machine-part cell formation (MPCF) problem is to organize an assembly as a set of cells, where each cell contains certain machines that process a sub-set of parts. In recent years, different types of metaheuristics have been used to solve the problem of MPCF. This publication focuses on solving the MPCF problem using a metaheuristic inspired on birds, called Migrating Birds optimization (MBO) algorithm. Experiments have been conducted to 180 test instances using 2 types of leader exchange in the flock of birds. The results obtained using MBO are equal to or better than other algorithms reported in the literature.

关键词： Cell formation problem nature-inspired optimization algorithms Migrating Birds optimization Metaheuristics

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Experiment-Based Comparison of nature-inspired algorithms for Optimal Tuning of PI-Fuzzy Controlled Nonlinear DC Servo Systems

Experiment-Based Comparison of Nature-Inspired Algorithms fo...

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International Symposium on Power Electronics, Electrical Drives, Automation, and Motion (SPEEDAM)

作者： Precup, Radu-Emil David, Radu-Codrut Radac, Mircea-Bogdan Voisan, Emil-Ioan Petriu, Emil M. Politehn Univ Timisoara Dept Automat and Appl Informat Timisoara Romania Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada

ISBN: (纸本)9781509020676

This paper proposes the comparison of seven nature-inspired optimization algorithms (NIOAs) applied to the tuning of proportional-integral (PI)-fuzzy controllers for a class of nonlinear direct current (DC) servo systems. The servo systems are modeled by second order dynamics with a saturation and dead zone static nonlinearity specific to the actuator. Seven NIOAs are considered, namely Simulated Annealing, Particle Swarm optimization (PSO), Gravitational Search Algorithm (GSA), hybrid PSOGSA, Charged System Search (CSS), adaptive GSA and adaptive CSS, to solve optimization problem. The objective function is the weighted sum of time multiplied by squared control error plus squared output sensitivity function. The output sensitivity function is obtained from the state sensitivity models of the fuzzy control systems with respect to the modification of the process gain leading to a reduced process gain sensitivity. Three parameters of the PI-fuzzy controllers are tuned as variables of the objective function. Simulations and experimental results related to the angular position control of a laboratory nonlinear DC servo system are included.

关键词： nature-inspired optimization algorithms nonlinearity PI-fuzzy controllers servo systems

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Optimal Design of Deformation Monitoring Networks Using the Global optimization Methods 1

Optimal Design of Deformation Monitoring Networks Using the ...

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1st IAG International Workshop on the Quality of Geodetic Observation and Monitoring Systems (QuGOMS)

作者： Yetkin, M. Inal, C. Izmir Katip Celebi Univ Dept Geomat Engn TR-35620 Izmir Turkey Selcuk Univ Dept Geomat Engn TR-42250 Konya Turkey

ISBN: (纸本)9783319108285;9783319108278

Geodetic networks are very important tools that can be used to monitor crustal movements or the deformation of structures. However, a geodetic network must be designed to sufficiently meet some network quality requirements such as accuracy, reliability, sensitivity and economy. This is the subject of geodetic network optimization. Traditional methods have been used for solving geodetic optimization problems. On the other hand, some evolutionary algorithms such as the particle swarm optimization algorithm have been started to be recently used. These methods are inspired by optimization and adaptation processes that are encountered in the nature. They are iterative procedures for quickly and efficiently solving complex optimization problems. They may provide global optimum solution or at least near-optimum solutions to problems. In this paper, the use of the shuffled frog-leaping algorithm for the optimal design of a deformation monitoring network is studied. The aim is to design and optimize a geodetic network in terms of high reliability.

关键词： First-order design Geodetic network optimization nature-inspired optimization algorithms Network reliability Stochastic search methods

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An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

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COMPUTERS IN INDUSTRY 2015年 74卷 75-94页

作者： Precup, Radu-Emil Angelov, Plamen Jales Costa, Bruno Sielly Sayed-Mouchaweh, Moamar Politehn Univ Timisoara Dept Automat & Appl Informat Timisoara 300223 Romania Univ Lancaster Data Sci Grp Sch Comp & Commun Lancaster LA1 4WA England Univ Carlos III Madrid Chair Excellence Madrid Spain Fed Inst Rio Grande Norte IFRN Natal RN Brazil Ecole Mines Douai F-59500 Douai France

Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Data-driven control Data mining Evolving soft computing techniques Fault diagnosis nature-inspired optimization algorithms Wind turbines

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Experiment-Based Comparison of nature-inspired algorithms for Optimal Tuning of PI-Fuzzy Controlled Nonlinear DC Servo Systems

Experiment-Based Comparison of Nature-Inspired Algorithms fo...

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International Symposium on Power Electronics, Electrical Drives, Automation and Motion

作者： Radu-Emil Precup Radu-Codrut David Mircea-Bogdan Radac Emil-Ioan Voisan Emil M. Petriu Department of Automation and Applied Informatics Politehnica University of Timisoara School of Electrical Engineering and Computer Science University of Ottawa

ISBN: (纸本)9781509020683

关键词： nature-inspired optimization algorithms Nonlinearity PI-fuzzy controllers Servo systems

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