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Optimum transmission distance for relay-assisted free-space optical communication systems

OPTIK 2016年第16期127卷 6490-6497页

作者： Basgumus, Arif Hicdurmaz, Bahadir Temurtas, Hasan Namdar, Mustafa Altuncu, Ahmet Yilmaz, Gunes Dumlupinar Univ Dept Elect & Elect Engn TR-43100 Kutahya Turkey Uludag Univ Dept Elect & Elect Engn TR-16059 Bursa Turkey Dumlupinar Univ Dept Comp Engn TR-43100 Kutahya Turkey

In this paper, optimum transmission distances are obtained for different relay nodes in the serial and parallel decode-and-forward relaying schemes by using differential evolution algorithm in free-space optical communication systems. The transmission distances are investigated by optimizing the place of the relay nodes at a target outage probability of 10(-6). In this study, the numerical results reveal that the optimum transmission distances are increased for both the whole power margins and different number of relay nodes with the help of the proposed optimization technique. (C) 2016 Elsevier GmbH. All rights reserved.

关键词： Free-space optical communication Relay-assisted transmission Atmospheric turbulence fading Outage probability Optimum transmission distance Decode and forward relaying differential evolution algorithm

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Environmental/economic power dispatch problem using multi-objective differential evolution algorithm

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ELECTRIC POWER SYSTEMS RESEARCH 2010年第9期80卷 1171-1181页

作者： Wu, L. H. Wang, Y. N. Yuan, X. F. Zhou, S. W. Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Hunan Univ Sci & Technol Coll Informat & Elect Engn Xiangtan 411201 Hunan Peoples R China

This paper presents a multi-objective differential evolution (MODE) algorithm for environmental/economic power dispatch (EED) problem. The EED problem is formulated as a nonlinear constrained multi-objective problem with competing and non-commensurable objectives of fuel cost, emission and system loss. The proposed MODE approach adopts an external elitist archive to retain non-dominated solutions found during the evolutionary process. In order to preserve the diversity of Pareto optimality, a crowding entropy diversity measure tactic is proposed. The crowding entropy strategy is able to measure the crowding degree of the solutions more accurately. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed approach have been carried out on the IEEE 30- and 118-bus test system. The results demonstrate the capability of the proposed MODE approach to generate well-distributed Pareto optimal non-dominated solutions of multi-objective EED problem. The comparison with reported results of other MOEAs reveals the superiority of the proposed MODE approach and confirms its potential for solving other power systems multi-objective optimization problems. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Environmental/economic power dispatch evolutionary algorithms differential evolution algorithm Multi-objective optimization

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Comparative Analysis of evolutionary algorithms for the Problem of Parametric Optimization of PID Controllers

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Procedia Computer Science 2017年 103卷 100-107页

作者： S.V. Konstantinov A.A. Baryshnikov RUDN University 6 Mikluho-Maklaya str. Moscow 117198 Russia

Six modern and promising evolutionary algorithms are described: genetic algorithm, differential evolution method, variational genetic algorithm, particle swarm optimization algorithm, bat-inspired method and firefly algorithm. For all algorithms brief description and main steps of receiving solution are given. In the experimental part all algorithms are compared by the effectiveness of solving the parametric optimization problem for PID controllers.

关键词： evolutionary algorithms global optimization problem genetic algorithm differential evolution algorithm variational genetic algorithm particle swarm optimization bat-inspired algorithm fireflies algorithm

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A bi-level model for GHG emission charge based on a continuous distribution of travelers' value of time (VOT)

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TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 2016年 47卷 371-382页

作者： Zhao, Laijun Xu, Xiang Gao, H. Oliver Wang, Jiajia Xie, Yujing Shanghai Jiao Tong Univ SinoUS Global Logist Inst Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai Peoples R China Cornell Univ Sch Civil & Environm Engn Ithaca NY 14853 USA Shanghai Univ Sch Management Shanghai 200444 Peoples R China

Transportation is an important source of greenhouse gas (GHG) emissions. In this paper, we develop a bi-level model for GHG emission charge based on continuous distribution of the value of time (VOT) for travelers. In the bi-level model framework, a policy maker (as the leader) seeks an optimal emission charge scheme, with tolls differentiated across travel modes (e.g., bus, motorcycles, and cars), to achieve a given GHG reduction target by shifting the proportions of travelers taking different modes. In response, travelers (as followers) will adjust their travel modes to minimize their total travel cost. The resulting mode shift, hence the outcome of the emission charge policy, depends on travelers' VOT distribution. For the solution of the bi-level model, we integrate a differential evolution algorithm for the upper level and the "all or nothing" traffic assignment for the lower level. Numerical results from our analysis suggest important policy implications: (1) in setting the optimal GHG emission charge scheme for the design of transportation GHG emission reduction targets, policy makers need to be equipped with rigorous understanding of travelers' VOT distribution and the tradeoffs between emission reduction and system efficiency;and (2) the optimal emission charge scheme in a city depends significantly on the average value of travelers' VOT distribution the optimal emission charge can be designed and implemented in consistency with rational travel flows. Further sensitivity analysis considering various GHG reduction targets and different VOT distributions indicate that plausible emission toll schemes that encourage travelers to choose greener transportation modes can be explored as an efficient policy instrument for both transportation network performance improvement and GHG reduction. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Greenhouse gas Value of time Emission charge differential evolution algorithm

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A method for the allocation of sequencing resources in genotyped livestock populations

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GENETICS SELECTION evolution 2017年第20170522期49卷 47-47页

作者： Gonen, Serap Ros-Freixedes, Roger Battagin, Mara Gorjanc, Gregor Hickey, John M. Univ Edinburgh Roslin Inst Easter Bush Midlothian Scotland Univ Edinburgh Royal Dick Sch Vet Studies Easter Bush Midlothian Scotland

Background: This paper describes a method, called AlphaSeqOpt, for the allocation of sequencing resources in livestock populations with existing phased genomic data to maximise the ability to phase and impute sequenced haplotypes into the whole population. Methods: We present two algorithms. The first selects focal individuals that collectively represent the maximum possible portion of the haplotype diversity in the population. The second allocates a fixed sequencing budget among the families of focal individuals to enable phasing of their haplotypes at the sequence level. We tested the performance of the two algorithms in simulated pedigrees. For each pedigree, we evaluated the proportion of population haplotypes that are carried by the focal individuals and compared our results to a variant of the widely-used key ancestors approach and to two haplotype-based approaches. We calculated the expected phasing accuracy of the haplotypes of a focal individual at the sequence level given the proportion of the fixed sequencing budget allocated to its family. Results: AlphaSeqOpt maximises the ability to capture and phase the most frequent haplotypes in a population in three ways. First, it selects focal individuals that collectively represent a larger portion of the population haplotype diversity than existing methods. Second, it selects focal individuals from across the pedigree whose haplotypes can be easily phased using family-based phasing and imputation algorithms, thus maximises the ability to impute sequence into the rest of the population. Third, it allocates more of the fixed sequencing budget to focal individuals whose haplotypes are more frequent in the population than to focal individuals whose haplotypes are less frequent. Unlike existing methods, we additionally present an algorithm to allocate part of the sequencing budget to the families (i.e. immediate ancestors) of focal individuals to ensure that their haplotypes can be phased at the sequence level, whic

关键词： Sequence Level differential evolution algorithm Focal Individual Livestock Population Quantitative Trait Nucleotide

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Classification of Gene Expression Data Using Multiobjective differential evolution

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ENERGIES 2016年第12期9卷 1061页

作者： Ma, Shijing Li, Xiangtao Wang, Yunhe Northeast Normal Univ Sch Comp Sci & Informat Technol Changchun 130117 Peoples R China

Gene expression data are usually redundant, and only a subset of them presents distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in bioinformatics. In this paper, a multiobjective binary differential evolution method (MOBDE) is proposed to select a small subset of informative genes relevant to the classification. In the proposed method, firstly, the Fisher-Markov selector is used to choose top features of gene expression data. Secondly, to make differential evolution suitable for the binary problem, a novel binary mutation method is proposed to balance the exploration and exploitation ability. Thirdly, the multiobjective binary differential evolution is proposed by integrating the summation of normalized objectives and diversity selection into the binary differential evolution algorithm. Finally, the MOBDE algorithm is used for feature selection, and support vector machine (SVM) is used as the classifier with the leave-one-out cross-validation method (LOOCV). In order to show the effectiveness and efficiency of the algorithm, the proposed method is tested on ten gene expression datasets. Experimental results demonstrate that the proposed method is very effective.

关键词： multiobjective method differential evolution algorithm binary differential evolution binary optimization

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Optimal allocation of battery energy storage systems in distribution networks with high wind power penetration

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IET RENEWABLE POWER GENERATION 2016年第8期10卷 1105-1113页

作者： Zhang, Yongxi Dong, Zhao Yang Luo, Fengji Zheng, Yu Meng, Ke Wong, Kit Po Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Univ Newcastle Ctr Intelligent Elect Networks Newcastle NSW 2384 Australia China Southern Power Grid Co Elect Power Res Inst Guangzhou 510080 Guangdong Peoples R China Univ Western Australia Sch Elect Elect & Comp Engn Perth WA Australia

In recent years, the battery energy storage system (BESS) has been considered as a promising solution for mitigating renewable power generation intermittencies. This study proposes a stochastic planning framework for the BESS in distribution networks with high wind power penetrations, aiming to maximise wind power utilisation while minimise the investment and operation costs. In the proposed framework, the uncertainties in wind power output and system load are modelled by the Monte-Carlo simulation, and a chance-constrained stochastic optimisation model is formulated to optimally determine the location and capacity of BESS while ensuring wind power utilisation level. Then, the Monte-Carlo simulation embedded differential evolution algorithm is used to solve the problem. Simulation studies performed on a 15-bus radial distribution system prove the efficiency of the proposed method.

关键词： battery storage plants wind power plants power distribution planning Monte Carlo methods stochastic programming evolutionary computation battery energy storage systems BESS distribution networks wind power penetration renewable power generation intermittencies stochastic planning wind power utilisation wind power uncertainties Monte Carlo simulation stochastic optimisation model differential evolution algorithm radial distribution system

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Physics-Based Design by Optimization of Unconventional Supercavitating Hydrofoils

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JOURNAL OF SHIP RESEARCH 2016年第4期60卷 187-202页

作者： Vernengo, Giuliano Bonfiglio, Luca Gaggero, Stefano Brizzolara, Stefano MIT Dept Mech Engn MIT iShip Design Lab Cambridge MA 02139 USA Univ Genoa DITEN Naval Architecture & Marine Technol Unit Genoa Italy

A computational framework to design a new family of unconventional supercavitating (SC) hydrofoils with optimized hydrodynamic performance is developed. A low-order boundary element method is used to solve for the steady potential flow over the hydrofoil predicting its hydrodynamic characteristics, including the vapor cavity interface. Shape variations are obtained by an ad hoc parametrization scheme by composite B-spline curves whose control points represent the design variables to the hydrodynamic optimization problem. The accuracy of the Computational Fluid Dynamics (CFD) tools is also preventively validated on the experimental characteristics of a conventional SC hydrofoil. A computational test case is performed to maximize the efficiency of a SC hydrofoil accounting for both shape and angle of attack variations. The new hydrofoil leads to 40% improvement on the lift over drag ratio compared to the initial reference shape. This result is confirmed by high-fidelity unsteady multiphase viscous solver.

关键词： supercavitating hydrofoils design by optimization boundary element method multiphase Reynold-averaged Navier Stokes differential evolution algorithm

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Diagnosis of Power Transformer Faults Based on Multi-layer Support Vector Machine Hybridized with Optimization Methods

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2016年第19期44卷 2172-2184页

作者： Koroglu, Selim Demircali, Akif Pamukkale Univ Dept Elect & Elect Engn TR-20070 Kinikli Denizli Turkey

This article presents an intelligent diagnosis and classification method for power transformer fault classification based on dissolved gas analysis: the support vector machine. It is a powerful algorithm for classification of faults that needs a limited set of small sampling data, a case of applications with non-linear behavior, and a high number of parameters;however, appropriate model parameters must be determined carefully. The selection of parameters has a direct effect on the machine's classification accuracy. In this study, a multi-layer support vector machine classifier is optimized by a grid search method and three heuristic approaches: (1) genetic, (2) differential evolution, and (3) particle swarm optimization algorithms. The performance analysis of the support vector machine hybridized with these optimization methods is demonstrated using the same classification set. The employed structure has five support vector machine layers, each of which uses a Gaussian kernel function due to its advantages of needing one parameter for optimization and providing excellent classification ability for non-linear data. The proposed approach gives highly accurate performance for diagnosis of power transformers. The support vector machine optimized with the particle swarm optimization algorithm has the best accuracy and requires less computational time compared to the other methods.

关键词： transformer fault diagnosis dissolved gas analysis support vector machine grid search genetic algorithm differential evolution algorithm particle swarm optimization

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Optimal strategy for distribution system with PV based on voltage violation regulation

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The Journal of Engineering 2017年第13期2017卷 1124-1128页

作者： Shanhong Jin Weiguo He Jingwei Zhang Xiang Zhu Haoming Liu State Grid Jiaxing Power Supply Company Jiaxing People's Republic of China China Electric Power Research Institute Nanjing People's Republic of China College of Energy and Electrical Engineering Hohai University Nanjing People's Republic of China

The high penetration of distributed photovoltaic (PV) can bring about huge economic and environmental benefits in the distribution network. While at the same time, it can result in the voltage violation in power system. Due to the two-quadrant power output characteristics of PV inverter, it is possible to regulate voltage violation via utilising the reactive power output of PV inverter instead of traditional reactive power compensation devices. In this study, a global voltage regulation strategy has been addressed by taking the indices of voltage violation regulation and active power loss of distribution network into consideration, and voltage limit violation is processed based on the concept of hard and soft voltage boundary constraints. differential evolution algorithm is introduced to solve the optimisation model. Finally, it was simulated in IEEE-33 node distribution system, results of example analysis confirm that the proposed strategy can optimise the reactive and active power of PV, which can not only regulate the voltage violation but also realise the economic operation of the distribution system.

关键词： photovoltaic power systems voltage control power distribution control power generation control power distribution economics power generation economics environmental factors invertors evolutionary computation optimisation reactive power control distribution network system distributed photovoltaic system economic benefit environmental benefit power system two-quadrant power output characteristics PV inverter optimisation model IEEE-33 node distribution system optimisation model differential evolution algorithm soft voltage boundary constraint reactive power compensation device global voltage violation regulation strategy active power loss hard voltage boundary constraint

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