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Titlebook: Optimization in Industry; Ian C. Parmee,Prabhat Hajela Conference proceedings 2002 Springer-Verlag London Limited 2002 algorithm.algorithm

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發(fā)表于 2025-3-21 16:11:58 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Optimization in Industry
編輯Ian C. Parmee,Prabhat Hajela
視頻videohttp://file.papertrans.cn/704/703254/703254.mp4
概述Includes supplementary material:
圖書封面Titlebook: Optimization in Industry;  Ian C. Parmee,Prabhat Hajela Conference proceedings 2002 Springer-Verlag London Limited 2002 algorithm.algorithm
描述Optimization in Industry comprises a collection of papers presented at the third US United Engineering Foundation‘s ‘Optimization in Industry‘ Conference. The main thrust of this, the third conference of the series is related to engineering optimization including both manufacture and parametric design. The papers included explore the relationships between well-established deterministic optimization methods and the emerging stochastic and mainly population-based search and optimization algorithms. A mix of approaches across a wide range of engineering disciplines is included. It illustrates the manner in which various techniques can be utilised either in a stand-alone manner or within hybrid systems to give best performance in terms of optimal design and computational efficiency. The papers span scientific, application, awareness/information dissemination and industrial requirements areas. They provide information on available search and optimization techniques and their application to specific design problems and across the field of manufacturing generally. Papers identifying and dealing with problems of incorporating novel optimization techniques within day-to-day design practice
出版日期Conference proceedings 2002
關(guān)鍵詞algorithm; algorithms; calibration; control; engineering design; evolution; evolutionary algorithm; evoluti
版次1
doihttps://doi.org/10.1007/978-1-4471-0675-3
isbn_softcover978-1-85233-534-2
isbn_ebook978-1-4471-0675-3
copyrightSpringer-Verlag London Limited 2002
The information of publication is updating

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發(fā)表于 2025-3-21 21:27:45 | 只看該作者
Strategies for Structural and Thermal Optimization in the Field of Automotive Radio and Navigation Snt thermal management strategies. This includes the selection of materials and components as well as their design and placement. The design of such electronic systems requires the interaction of mechanical, electrical and software engineers in order to achieve a system solution, hence a multi-disciplinary design and optimization problem.
板凳
發(fā)表于 2025-3-22 01:30:51 | 只看該作者
Optimal Positioning of Clamps for Workpiece Adjustment Using Multi-objective Evolutionary Computatio different approaches with Genetic Algorithms, Evolution Strategies, Co-Evolution and Multi-objective Evolutionary Algorithms. We conclude with the results of experiments and the discussion of those. In real use the implemented software improves automation, saves working hours, reduces losses of production and prevents accidents at work.
地板
發(fā)表于 2025-3-22 06:45:14 | 只看該作者
Genetic Algorithms Solve Combinatorial Optimisation Problems in the Calibration of Combustion Engineand a . heuristic as a local search. Finally, well-defined look-up tables, that lead to smooth maps, are composed from multiple valued look-up tables. Again a genetic algorithm finds better solutions than local search heuristics.
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Conference proceedings 2002nce. The main thrust of this, the third conference of the series is related to engineering optimization including both manufacture and parametric design. The papers included explore the relationships between well-established deterministic optimization methods and the emerging stochastic and mainly p
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發(fā)表于 2025-3-22 20:56:51 | 只看該作者
Optimization: The Competitive Edgeion and demonstrate that this is the one technology that is useful across a broad spectrum of design activities. Given today’s emphasis on conversation of natural resources and reduced energy consumption, the motivation for using optimization is compelling. Indeed, this is a tool that will provide its provide its users the competitive edge.
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Time-saving Robust Design Considering Vehicle Crash Performanceefficiency of the process is shown with two application examples coming from the vehicle crash discipline. It is shown, that the method can handle large finite element models (>100k finite elements) in a limited time.
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發(fā)表于 2025-3-23 09:06:51 | 只看該作者
Aerodynamic Optimization for the Transonic Compressor Stator Blade condition. To make optimization a more realistic flow condition, an objective function for an incidence toughness, which is under an off-design flow condition, was proposed. A three dimensional space that consisted of the above three objectives made the Pareto surface, providing the trade-off relationship among them.
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