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Titlebook: Emerging Methods for Multidisciplinary Optimization; Jan Blachut,Hans A. Eschenauer Conference proceedings 2001 Springer-Verlag Wien 2001

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書目名稱Emerging Methods for Multidisciplinary Optimization
編輯Jan Blachut,Hans A. Eschenauer
視頻videohttp://file.papertrans.cn/309/308310/308310.mp4
叢書名稱CISM International Centre for Mechanical Sciences
圖書封面Titlebook: Emerging Methods for Multidisciplinary Optimization;  Jan Blachut,Hans A. Eschenauer Conference proceedings 2001 Springer-Verlag Wien 2001
描述This volume provides an up-to-date overview of major advances, emerging trends, and projected industrial applications in the field of multidisciplinary optimization. It concentrates on the current status of the field, exposes commonalities, innovative, promising, and speculative methods. This book provides a view of today’s multidisciplinary optimization environment through a balenced theoretical and practical treatment. The contributors are the foremost authorities in each area of specialisation.
出版日期Conference proceedings 2001
關(guān)鍵詞multidisciplinary optimizations; algorithms; genetic algorithms; genetic programming; optimization
版次1
doihttps://doi.org/10.1007/978-3-7091-2756-8
isbn_softcover978-3-211-83335-3
isbn_ebook978-3-7091-2756-8Series ISSN 0254-1971 Series E-ISSN 2309-3706
issn_series 0254-1971
copyrightSpringer-Verlag Wien 2001
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Old and New Non-Gradient Methods in Engineering Optimization, second from fast progress in the computing environment. The first has resulted in new optimization techniques like simulated annealing, tabu search and genetic algorithms whilst the second has made many of the existing zero order methods computationally affordable. This chapter discusses simulated
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Optimal Engineering Design by Means of Stochastic Optimization Methods,, fixed quantities, but must be considered as random variables with a given probability distribution. Evaluating the performance of the system by expected cost functions or taking into account the reliability of designs, the basic optimization problem under stochastic uncertainty is replaced by a ce
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Strategies for Modeling, Approximation, and Decomposition in Genetic Algorithms Based Multidiscipling design, and particularly, in the context of using formal methods of design optimization. The computational models considered in this discussion include genetic algorithms, neural networks, and a modeling of the biological immune system. The focus of the chapter is two-fold. First, it introduces t
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CISM International Centre for Mechanical Scienceshttp://image.papertrans.cn/e/image/308310.jpg
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Native Advertising: Daten und Fakten,“best-possible” or “optimal” structures / systems are the ones that correspond to the designer’s desired concepts, meeting at the same time the multidisciplinary requirements and/or specifications referring to manufacturing, assembling, operation, etc. In comparison with the “trial-and-error” approa
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https://doi.org/10.1007/978-3-030-58718-5, fixed quantities, but must be considered as random variables with a given probability distribution. Evaluating the performance of the system by expected cost functions or taking into account the reliability of designs, the basic optimization problem under stochastic uncertainty is replaced by a ce
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