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检索条件"任意字段=2016 International Congress on Computation Algorithms in Engineering"

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Multi-objective optimization of HEV fuel economy and emissions using evolutionary computation

Multi-objective optimization of HEV fuel economy and emissio...

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2004 SAE World congress

作者： Hu, Xiaolin Wang, Zhongfan Liao, Lianying Automotive Engineering Institute Wuhan University of Technology China

ISBN: (纸本)0768013194

How to match various components of the Hybrid Electric Vehicle (HEV) and manage the energy distribution is critical for HEV design and control considering that there are at least two sets of energy output systems, the fuel converter (engine or fuel cell) and the energy storage system (battery stack). It is an optimization issue. Much work has been done in this field but a significant portion optimizes the component sizes and control strategy parameters separately. What's more, most work concerns single objective optimization although more than one objective is more common and natural in HEV optimization. This paper uses an advanced soft computing technique, Non-dominated Sorting Genetic Algorithm (NSGA-II), one of the most efficient Multi-Objective Evolutionary algorithms (MOEAs), to optimize a parallel HEV's fuel economy and emissions including HC, CO, and NOx emissions simultaneously, instead of converting them into a single objective. The task is to find the trade-off solutions, namely, Pareto-optimal solutions, among the four objectives. In addition, three component sizing parameters and four control strategy parameters are treated as variables all together. The obtained set of optimal solutions demonstrates great success either in converging to the real Pareto front or in providing sufficiently diverse solutions. It is concluded that the MOEA serves as a useful guide in the HEV developing process, especially in the earlier phases, since it can furnish the developers with a somewhat comprehensive preview of the problem. Copyright © 2004 SAE international.

关键词： Hybrid vehicles

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A mating strategy for multi-parent genetic algorithms by integrating tabu search

A mating strategy for multi-parent genetic algorithms by int...

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2003 congress on Evolutionary computation, CEC 2003

作者： Ting, Chuan-Kang Büning, Hans Kleine International Graduate School of Dynamic Intelligent Systems Germany Department of Computer Science Electrical Engineering and Mathematics University Paderborn 33098 Paderhom Germany

ISBN: (纸本)0780378040

Multiparent crossovers have been validated their outperformance on several optimization problems. However, there are two issues to be considered - the number of parents and the disruptiveness caused by multiple parents. We present a tabu multiparent genetic algorithm (TMPGA) to address these two issues by integrating tabu search into the mating of multiparent genetic algorithms. TMPGA utilizes the tabu restriction and the aspiration criterion to sift selected parents in consideration of population diversity and selection pressure. Furthermore, the resulting mating validity further adjusts the number of parents participating in a mating. Experiments are conducted with four common test functions. The results indicate that TMPGA can achieve better performance than both two-parent GA and multiparent GA with the diagonal crossover. © 2003 IEEE.

关键词： Genetic algorithms

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Parallel differential evolutionary algorithms for physique states characterization in bioengineering

Parallel differential evolutionary algorithms for physique s...

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international Conference on Parallel Processing

作者： Manyri, L Régis, S Doncescu, A Desachy, J Urribelarea, JL Lab Anal & Architecture Syst F-31400 Toulouse 4 France

ISBN: (纸本)0769520189

This paper presents a methodology to detect on line the physiological state of strains in bioreactor The biologic reactions means that one of the pathways of the metabolism is activated and in this case the microorganisms will produce or will consummate The discrete-event system (DES) is synthesised applying the Maximum of Modulus of the Wavelet Transform on measured signals constrained to the biotechnologist expert validation. The determination of Holder Coefficient by Differential Evolutionary algorithms allows to make the difference between different discontinuities and to obtain the segmentation of the signals. All these evaluations leads to associate the signals variations during the time to physiological states.

关键词： Biomedical engineering Biomedical measurements Bioreactors Biotechnology Discrete wavelet transforms Evolutionary computation Fungi Laboratories Mathematical model Wavelet analysis

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computation and visualization of 3-D Marangoni and magnetically-driven flows in droplets

Computation and visualization of 3-D Marangoni and magnetica...

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2003 ASME international Mechanical engineering congress

作者： Huo, Y. Ai, X. Li, B.Q. Sch. of Mech./Materials Engineering Washington State University Pullman WA 99164 United States

This paper presents a computational and visualization tool kit for the numerical modeling of 3-D Marangoni and/or magnetically-driven turbulent flows in droplets under normal and/or microgravity conditions. The computational part involves the finite element solution of steady-state and transient 3-D Marangoni flows in electrically levitated droplets and the higher order finite difference method for the direction numerical simulation of turbulences in electromagnetically levitated droplets. Both the electrically and magnetically droplets have been used for study of fundamentals governing solidification processing in normal and mciro gravity. The visualization part is developed based on the UNIX/X-motif platform and on the advanced algorithms for mutli-dimensional computer graphics. The visualization tool kit employs the algorithms for data retrieving, partitioning, sorting and searching algorithms, and 3-D/2-D object clipping. An efficient algorithm used for plane and body cutting and particle tracing is presented. The mathematical formulation used in developing the above computational tool kit, including computational and differential geometry, is also discussed. Examples are given to illustrate the effectiveness and efficiency of the tool kit as applied to the numerical simulation and computer visualization of complex steady state and transient three-dimensional Marangoni and turbulent magnetically driven flows in free droplets.

关键词： Turbulent flow

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SCYCLE - Advanced loading pattern optimization code using new algorithm

SCYCLE - Advanced loading pattern optimization code using ne...

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2nd international congress on Advances in Nuclear Power Plants, ICAPP 2003

作者： Liu, Zhihong Shi, Gong Hu, Yongming Department of Engineering Physics Tsinghua university China Institute of Nuclear Energy Technology Tsinghua University Beijing 100084 China

In the field of global optimization, there are many algorithms such as Simulated Annealing Algorithm (SA), Genetic Algorithm (GA), Artificial Neural Network (ANN), and so on. They are all based on the imitation of nature. This paper presents a new global optimization algorithm - statistic inductive algorithm (SIA), which is based on probability theory. The calculated results on some standard testing functions and a 'travelling salesman problem' (TSP) show that SIA is of more effective quality on global optimization than SA and GA. In addition to these results, according to this fundamental optimization idea, this paper analyzed why SA and GA can have effect on global search, and what inherent defects SA and GA have. Reloading optimization code used SIA has been completed. Calculations were performed on various reactor cores. Compared with SA, GA, and Ant System, SIA need less computation time, while the result is much better than them. Conclusion is drawn that SIA is more suitable for in-core fuel management.

关键词： Simulated annealing

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An implementation of genetic algorithms as a basis for a trading system on the foreign exchange market

An implementation of genetic algorithms as a basis for a tra...

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congress on Evolutionary computation

作者： A. Hryshko T. Downs School of Information Technology & Electrical Engineering University of Queensland QLD Australia

Foreign exchange trading has emerged in recent times as a significant activity in many countries. As with most forms of trading, the activity is influenced by many random parameters so that the creation of a system that effectively emulates the trading process is very helpful. In this paper, we try to create such a system with a genetic algorithm engine to emulate trader behaviour on the foreign exchange market and to find the most profitable trading strategy.

关键词： Genetic algorithms Economic forecasting Information technology Australia Exchange rates Supply and demand Demand forecasting Engines international trade Stock markets

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A mating strategy for multi-parent genetic algorithms by integrating tabu search

A mating strategy for multi-parent genetic algorithms by int...

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congress on Evolutionary computation

作者： C.-K. Ting H.K. Buning International Graduate School of Dynamic Intelligent Systems University of Paderborn Paderborn Germany International Graduate School of Dynamic Intelligent Systems Paderborn Germany Department of Computer Science Electrical Engineering and Mathematics University Paderborn Paderborn Germany

关键词： Genetic algorithms Testing Space exploration Ground penetrating radar Intelligent systems Computer science Mathematics Biological system modeling Evolution (biology) Genetic mutations

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Dynamic compensation of intelligent sensors

Dynamic compensation of intelligent sensors

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2003 ASME international Mechanical engineering congress

作者： Schoen, Marco P. Department of Mechanical Engineering College of Engineering Idaho State University Pocatello ID 83209 United States

Commercial sensors have generally, due to their own characteristics, some undesirable influences on the measured quantity and its precision. In particular, the dynamic characteristics can be reflected on to the measured quantity and lead to false or delayed interpretation of the underlying physical process. The quality and therefore the cost of the sensor is often tied with the dynamic performance of these instruments. Intelligent sensors are able to adapt to changing environments, calibrate themselves, and predict the pattern of the future signal. This paper presents algorithms to improve the dynamic performance of sensors, identify the dynamic characteristics of the sensor, and to predict the future pattern of the measured quantity. In particular, two inverse filters are proposed for the improvement of the sensors dynamic performance. One filter incorporates an optimal constant feedback gain that reduces the computational cost and increases the accuracy. A system identification method is used to identify the sensor's dynamic properties and allows for adaptation of the inverse filter's parameters. This identification algorithm computes the optimum input to the system i.e. the sensor. The optimization is based on the inverse correlation matrix of the information matrix. A genetic algorithm is used to perform both optimizations, for the computation of the optimal input, and for the optimal constant feedback gain. In addition, a predictive filter formulation is given that is based on the identified system. Simulation results indicate that both inverse filters are capable of recovering the original or true signal. The second filter shows superiority in terms of convergence, lower computational cost, and lower error due to its optimized parameters. The predictive filter indicates good working accuracy for the signal prediction.

关键词： Intelligent agents

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Stochastic approaches to dynamic neural network training. Actuator fault diagnosis study 15th

Stochastic approaches to dynamic neural network training. Ac...

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15th World congress of the international Federation of Automatic Control, 2002

作者： Patan, Krzysztof Parisini, Thomas Institute of Control and Computation Engineering University of Zielona Góra ul. Podgórna 50 Zielona Córa65-246 Poland Dept. of Electrical Electronic and Computer Engineering DEEI-University of Trieste via Valeno 10 Trieste34127 Italy

ISBN: (纸本)9783902661746

A paper deals with application of stochastic methods for dynamic neural network training. The considered network is composed of dynamic neurons, which contain inner feedbacks. This network can be used as a part of a fault diagnosis system to generate residuals. Up-to-date training algorithms, based on the classical back propagation, suffer from entrapment in local minima of an error function. Two stochastic algorithms are tested as training algorithms to overcome these difficulties. Efficiency of the proposed learning methods is checked using data recorded at Lublin Sugar Factory, Poland. Copyright © 2002 IFAC.

关键词： Fault detection

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Preliminary statement on the current progress of multi-objective evolutionary algorithm performance measurement

Preliminary statement on the current progress of multi-objec...

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2002 congress on Evolutionary computation, CEC 2002

作者： Ang, Kiam Heong Chong, Gregory Li, Yun Centre for Systems and Control Department of Electronics and Electrical Engineering University of Glasgow Glasgow G 12 8LT United Kingdom

ISBN: (纸本)0780372824

Although multi-objective evolutionary algorithm techniques are becoming mature, benchmark measures for evaluating the algorithms still require further research, as convergence theories can hardly be applied here and the only practical method for performance comparison is through benchmark tests. This paper investigates the current progress on multi-objective evolutionary algorithm performance measurement. The paper is focused on identifying deficiencies existing in the current performance measure techniques. It is shown that, whilst some performance indicators are conclusive and consistent, it is critical for some cases to include the 'diversity' indicator in a benchmark test. Possible ways forward are also identified. © 2002 IEEE.

关键词： Benchmarking

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