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Optimisation design of medium frequency transformer for the offshore dc grid based on multi-objective genetic algorithm

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IET POWER ELECTRONICS 2017年第15期10卷 2157-2162页

作者： Zhang, Liang Zhang, Dan Shui, Henghua Yuan, Yubo Pei, Qian Zhu, Jihong Nanjing Inst Technol Sch Power Elect Engn 1 Hong Jing AveJiangning Sci Pk Nanjing Jiangsu Peoples R China Tsinghua Univ Dept Comp Sci & Technol East Main BldgRoom 9-310 Beijing Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Jiangsu Elect Power Co Res Inst Nanjing 211167 Jiangsu Peoples R China State Grid Suqian Power Supply Co Suqian 223800 Peoples R China

A DC/DC converter is a vital component for offshore DC grids, in which a medium frequency transformer (MFT) is the main element. Therefore, it is necessary to optimise the core material and the design method so that the loss of the MFT would be reduced. In this study, the multi-objective genetic algorithm is proposed, by which two MFTs with different core materials are both optimised. The magnetic flux density of the transformer core and the current density of the windings are introduced as the optimised variables. Also, the key equations for transformer design are put forward as the objective functions. Moreover, with constant iteration of the process of segmentation, parallel selection, mergence, recombination and variation, the Pareto optimal solution is determined. Furthermore, to verify the optimisation results, the temperature field and electric strength of the amorphous alloy MFT are calculated and simulated by finite-element analysis. Finally, a MFT prototype with an amorphous alloy core is built, and the experiment is carried out.

关键词： DC-DC power convertors offshore installations power transformers genetic algorithms cores distributed power generation finite-element analysis Pareto optimal solution windings magnetic flux density dc-dc converter multiobjective genetic algorithm offshore dc grid medium frequency transformer optimisation design

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Constraint handling methods for a generative envelope design using genetic algorithms: The case of a highly constrained problem

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INTERNATIONAL JOURNAL OF ARCHITECTURAL COMPUTING 2022年第3期20卷 587-609页

作者： Duclos-Prevet, Claire Guena, Francois Efron, Mariano Ecole Natl Super Architecture Paris Villette 144 Ave Flandres F-75019 Paris France

The use of genetic algorithms as generative and performance design techniques often involves, in practice, constraint handling, which can be a complex task. Moreover, environmental simulations are computationally expensive and managing constraints can avoid wasting time on infeasible solutions. Despite these two incentives, and the benefits of an immense literature, both applied and theorical, on constrained optimization, there are only few guidelines and tools directly applicable by architects to address this issue. This paper proposes to fill this gap by identifying, classifying, and implementing different constraint management techniques available to architects. Seven methods have been tested for a highly constrained envelope design problem, consisting in the optimization of a sun-shading system. Three of them are easily replicable to different types of projects while the four others need to find a problem-specific heuristic. It appears that the second category is more efficient but implies the use of generative techniques that are more difficult to implement than parametric models.

关键词： Optimization under constraint performative envelope design generative and sustainable design agent-based modeling multiobjective genetic algorithm visual comfort

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Hedging foreign exchange rate risk: Multi-currency diversification

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EUROPEAN JOURNAL OF MANAGEMENT AND BUSINESS ECONOMICS 2016年第1期25卷 2-7页

作者： Alvarez-Diez, Susana Alfaro-Cid, Eva Fernandez-Blanco, Matilde O. Univ Murcia Dept Quantitat Methods Econ Campus Espinardo E-30100 Murcia Spain Inst Technol Informat Camino Vera S-N Valencia 46022 Spain Univ Valencia Dept Corp Finance Avda Tarongers S-N Valencia 46022 Spain

This article proposes a multi-currency cross-hedging strategy that minimizes the exchange risk. The use of derivatives in small and medium-sized enterprises (SMEs) is not common but, despite its complexity, can be interesting for those with international activities. In particular, the reduction in the exchange risk borne through the use of natural multi-currency cross-hedging is measured, considering Conditional Value-at-Risk (CVaR) and Value-at-Risk (VaR) for measuring market risk instead of the variance. CVaR is minimized using linear programmes, while a multiobjective genetic algorithm is designed for minimizing VaR, considering two scenarios for each currency. The results obtained show that the optimal hedge strategy that minimizes VaR is different from the minimum CVaR hedge strategy. A very interesting point is that, just by investing in other currencies, a significant risk reduction in VaR and CVaR can be obtained. (c) 2015 AEDEM. Published by Elsevier Espana, S.L.U.

关键词： Cross-hedging Conditional Value-at-Risk Value-at-Risk Multi-currency diversification multiobjective genetic algorithm

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A New Methodology for Model Based Robust Fuzzy Digital PID Controller Design

A New Methodology for Model Based Robust Fuzzy Digital PID C...

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3rd Global Congress on Intelligent Systems (GCIS)

作者： Pires, Danubia Soares de Oliveira Serra, Ginalber Luiz Fed Inst Educ Sci & Technol Dept Electroelect Lab Computat Intelligence Appl Technol Sao Luis MA Brazil

ISBN: (纸本)9781467330725;9780769548609

In this paper, a robust fuzzy digital PID control strategy, via multiobjective genetic algorithm, based on the gain and phase margins specifications, with applications to uncertain dynamic systems with time delay, is proposed. A mathematical formulation based on the gain and phase margins, the fuzzy model and PID digital controller structures, and the time delay of the uncertain dinamic system, is deduced. A multiobjective genetic strategy is defined to tune the fuzzy controller parameters, so the gain and phase margins of the fuzzy control system are close to the specified ones. Computational results show the efficiency of the proposed methodology through the accuracy in the gain and phase margins of the fuzzy PID control system compared to the specified ones and tracking of the reference trajectory.

关键词： robust control fuzzy systems digital PID controller uncertain dynamic systems multiobjective genetic algorithm

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Combined DVFS and Mapping Exploration for Lifetime and Soft-Error Susceptibility Improvement in MPSoCs

Combined DVFS and Mapping Exploration for Lifetime and Soft-...

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Design, Automation and Test in Europe Conference and Exhibition (DATE)

作者： Das, A. Kumar, A. Veeravalli, B. Bolchini, C. Miele, A. Natl Univ Singapore Dept Elect & Comp Engn Singapore 117548 Singapore Politecn Milan Dip Elettron Informaz & Bioingn I-20133 Milan Italy

ISBN: (纸本)9783981537024

Energy and reliability optimization are two of the most critical objectives for the synthesis of multiprocessor systems-on-chip (MPSoCs). Task mapping has shown significant promise as a low cost solution in achieving these objectives as standalone or in tandem as well. This paper proposes a multiobjective design space exploration to determine the mapping of tasks of an application on a multiprocessor system and voltage/frequency level of each tasks (exploiting the DVFS capabilities of modern processors) such that the reliability of the platform is improved while fulfilling the energy budget and the performance constraint set by system designers. In this respect, the reliability of a given MPSoC platform incorporates not only the impact of voltage and frequency on the aging of the processors (wear-out effect) but also on the susceptibility to soft-errors - a joint consideration missing in all existing works in this domain. Further, the proposed exploration also incorporates soft-error tolerance by selective replication of tasks, making the proposed approach an interesting blend of reactive and proactive fault-tolerance. The combined objective of minimizing core aging together with the susceptibility to transient faults under a given performance/energy budget is solved by using a multi-objective genetic algorithm exploiting tasks' mapping, DVFS and selective replication as tuning knobs. Experiments conducted with real-life and synthetic application graphs clearly demonstrate the advantage of the proposed approach.

关键词： fault tolerance genetic algorithms multiprocessing systems power aware computing radiation hardening (electronics) system-on-chip DVFS MPSoCs core aging minimization multiobjective design space exploration multiobjective genetic algorithm multiprocessor systems-on-chip proactive fault-tolerance reactive fault-tolerance reliability optimization selective tasks replication soft-error susceptibility improvement soft-error tolerance synthetic application graphs task mapping wear-out effect Aging Computer architecture Engines Fault tolerance Fault tolerant systems Transient analysis

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Comparison study of SPEA2+, SPEA2, and NSGA-II in diesel engine emissions and fuel economy problem

Comparison study of SPEA2+, SPEA2, and NSGA-II in diesel eng...

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IEEE Congress on Evolutionary Computation

作者： Hiroyasu, T Nakayama, S Miki, M Doshisha Univ Dept Engn Kyoto 6100321 Japan

ISBN: (纸本)0780393635

Recently, the technology that can control NOx and Soot values of diesel engines by changing the electronically controllable parameters has been developed. However, there is a trade-off relationship between fuel economy and NOx values. Therefore, the diesel engines that can change their characteristics with along to the driving environment should be emerged in the future. For designing these kinds of engines, the Pareto solutions that can express the trade-off between fuel economy and NOx values are needed. In that case, the derived non dominated solutions should have the diversity not only in the objective space but also in the design variable space. SPEA2+ is one of multi objective genetic algorithms and is developed based on SPEA2. The derived non dominated solutions by SPEA2+ have the diversity in both objective space and design variable space. In this study, the diesel engines that have high fuel economy and small amounts of NOx and Soot are designed by SEPA2+. The results are compared with those of SPEA2 and NSGA-II. From the discussions, it is found that the solutions of SPEA2+ have the diversity not only in the objective space but also in the design variable space. These characteristics are very suitable for designing diesel engines whose parameters are changing against the driving environment.

关键词： air pollution control diesel engines fuel economy genetic algorithms nitrogen compounds NOx value NSGA-II Pareto solution SPEA2 SPEA2+ Soot value diesel engine emission electronically controllable parameter multiobjective genetic algorithm

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Hierarchical Planning Method for Product Supply based on Multi Objective genetic algorithm

Hierarchical Planning Method for Product Supply based on Mul...

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15th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Kurihara, Kenzo Maruyama, Hirohito Masuda, Kazuaki Kanagawa Univ Fac Eng Dept Syst Creat Yokohama Kanagawa 2218686 Japan

ISBN: (纸本)9781424468508

Manufacturers have to make dexterous supply plans in order to cope with the demand fluctuations. The demands will change day to day, and also the predicted demand values should be modeled by stochastic variables. Under these uncertain demand conditions, we formulate product supply planning into multi-objective problem pursuing profit maximization and risk minimization. We will propose a method that can generate Pareto optimal solutions based on multi-objective genetic algorithm and Monte Carlo simulation. The method adopts searching mode switching strategy to obtain quality Pareto optimal solution, and the method consists of two layers;the upper layer is a global search stage and the lower one is a local search stage to search better solutions around global solutions. Since the local search stage is time consuming, we also propose a fast calculation method of evaluating individuals for genetic algorithm.

关键词： Monte Carlo methods Monte Carlo simulation Pareto optimal solutions Pareto optimisation dexterous supply plans genetic algorithms hierarchical planning method multi-objective problem pursuing profit maximization multiobjective genetic algorithm planning product supply planning risk management risk minimization supply chains

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multiobjective GA Optimization for Energy Efficient Electric Vehicle Drivetrains 16

Multiobjective GA Optimization for Energy Efficient Electric...

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16th International Conference on Ecological Vehicles and Renewable Energies (EVER)

作者： Chakraborty, Sajib Padmaji, Vinayak Tran, Dai-Duong Araujo, Javier A. Corea Geury, Thomas El Baghdadi, Mohamed Hegazy, Omar Vrije Univ Brussel VUB ETEC Dept Pl Laan 2 B-1050 Brussels Belgium Vrije Univ Brussel VUB MOBI Res Grp Pl Laan 2 B-1050 Brussels Belgium Flanders Make B-3001 Heverlee Belgium Applus IDIADA Grp Santa Oliva 43710 Spain

ISBN: (纸本)9781665449021

This paper investigates the impact of using wide bandgap (WBG) technology-based bidirectional interleaved HV DC/DC converters on the performance of battery electric vehicles (BEVs). An existing electric vehicle is upgraded using off-the-shelf components. There are a variety of batteries, high voltage (HV) DC/DCs, inverters, electric motors, transmissions, etc., available off-the-shelf;hence, numerous possible combinations can be formed, which make the optimal component selection process more complicated through analytical methods. In this paper, a multiobjective genetic algorithm (MOGA) is adopted to minimize the electric energy consumption by improving drivetrain efficiency based on the optimal variant selection of the components. It is found from the virtual simulation framework in MATLAB/Simulink (R) that overall, there is a 9.2% reduction in the energy consumption over a given driving cycle, i.e., Worldwide Harmonized Light Vehicles Test Procedure-3a (WLTP3a). To this end, the drivetrain performance in terms of acceleration time from 0-90 km/h is also improved by 10.2%, while the efficiency is improved by 1.5% compared to the conventional e-drivetrain.

关键词： multiobjective genetic algorithm HV DC/DC converter WBG Electric Vehicle accleration time and energy consumption

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Design of the Diversity Antenna for the TV Monitor

Design of the Diversity Antenna for the TV Monitor

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2010 IEEE International Symposium Antennas and Propagation/CNC-USNC/URSI Radio Science Meeting

作者： Kuwahara, Yoshihiko Yamashita, Takanori Shizuoka Univ Grad Sch Engn Hamamatsu Shizuoka 4328561 Japan

ISBN: (纸本)9781424449682

In this paper, we examine about the arrangement of the diversity antenna that is composed by the monopole antennas pasted on the monitor back of the PC and the television receiver. Mean Effective Gain (MEG) and Pattern Cross Correlation (PCC) were used as evaluation parameter (Taga, 1990). An antenna enclosed to the monitor is modeled by the wire grid model and the performance of the antenna is analyzed by the method-of-moment (MOM). The best position of the antenna was decided by multi-objective genetic algorithm (Kuwahara, 2005). A simple model based on the simulation results was experimentally manufactured. Evaluation tests were carried out using the fading simulator and validities of the design were confirmed.

关键词： TV monitor antenna radiation patterns diversity antenna diversity reception evaluation parameter fading simulator genetic algorithms mean effective gain method of moments method-of-moment monopole antennas monopole antennas multiobjective genetic algorithm pattern cross correlation television receiver television receivers wire grid model

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Mixed Compositional Pattern-Producing Network-NeuroEvolution of Augmenting Topologies Method for the Locomotion Control of a Snake-Like Modular Robot

Mixed Compositional Pattern-Producing Network-NeuroEvolution...

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SICE International Symposium on Control Systems (SICE ISCS) part of the 10th SICE Multi-Symposium on Control Systems (MSCS)

作者： Song, Yu-Lun Chiu, Wei-Yu Manoharan, Shri Harish Natl Tsing Hua Univ Dept Elect Engn MOCaRL Lab Hsinchu Taiwan

ISBN: (纸本)9784907764760

Snake robots, which are a type of bionic robots, have high adaptability to various environments. Because of the unique structure and many degrees of freedom (DoFs) of snake robots, these robots can move on rough terrain or in narrow spaces. However, developing a controller for the locomotion of snake robots with many DoFs is challenging. A control model developed for a snake robot moving in one environment might be unsuitable in other environments. Thus, a mixed compositional pattern-producing network (CPPN)-NeuroEvolution of Augmenting Topologies (NEAT) method is proposed in this paper. This method is based on a neuroevolution algorithm and serpentine locomotion, and it can be used to control the locomotion of a snake-like modular robot in multiple environments with different obstacles. In the proposed method, a group of neural networks is constructed for each environment, and these networks are trained using a neuroevolution algorithm for several generations. Some pretrained neural networks for each environment are selected and then integrated to obtain mixed neural networks. These mixed neural networks are then evolved using a multiobjective genetic algorithm (MOGA) to improve the locomotion of a snake-like modular robot in multiple environments. The results of this study indicated that the mixed CPPN-NEAT method outperformed the combined CPPN-NEAT method and use of MOGA alone (without pretraining, combined networks, and mixed networks) in locomotion control for a snake-like modular robot in three environments with different obstacles.

关键词： multiobjective genetic algorithm neuroevolution algorithm serpentine locomotion snake-like modular robot

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