Evolution strategies for engineering design optimisation
Computer simulations of complex engineering problems have become a standard tool of modern product development and design. The increasing computational power at modest costs leads to a growing interest in directly using computer simulation codes for automatic product optimization. Traditional numeri...
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| Vydané v: | Computational Fluid and Solid Mechanics 2003 s. 2394 - 2397 |
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| Hlavní autori: | , |
| Médium: | Kapitola |
| Jazyk: | English |
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Elsevier Ltd
2003
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| ISBN: | 0080440460, 9780080440460 |
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| Abstract | Computer simulations of complex engineering problems have become a standard tool of modern product development and design. The increasing computational power at modest costs leads to a growing interest in directly using computer simulation codes for automatic product optimization. Traditional numerical optimization methods have some drawbacks that make them difficult to use with complex simulation software. Gradient-based methods are always local optimizers, thus requiring additional methods such as random restarts to find global optima. Evolutionary optimization is a way to overcome some of these limitations. This chapter presents a paper that introduces evolution strategies as a robust and fault-tolerant optimization method, which does not rely on gradients, is easily adaptable to massively parallel computing systems and can be used for single and multiple-criteria optimization. It describes a complex and aerodynamical test problem that was solved by an evolution strategy. This paper introduces the basic elements of evolution strategies and addresses their important features such as self adaptation, robustness, and multiprocessor implementations. |
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| AbstractList | Computer simulations of complex engineering problems have become a standard tool of modern product development and design. The increasing computational power at modest costs leads to a growing interest in directly using computer simulation codes for automatic product optimization. Traditional numerical optimization methods have some drawbacks that make them difficult to use with complex simulation software. Gradient-based methods are always local optimizers, thus requiring additional methods such as random restarts to find global optima. Evolutionary optimization is a way to overcome some of these limitations. This chapter presents a paper that introduces evolution strategies as a robust and fault-tolerant optimization method, which does not rely on gradients, is easily adaptable to massively parallel computing systems and can be used for single and multiple-criteria optimization. It describes a complex and aerodynamical test problem that was solved by an evolution strategy. This paper introduces the basic elements of evolution strategies and addresses their important features such as self adaptation, robustness, and multiprocessor implementations. |
| Author | Willmes, Lars Bäck, Thomas |
| Author_xml | – sequence: 1 givenname: Lars surname: Willmes fullname: Willmes, Lars organization: NuTech Solutions GmbH, Martin-Schmeißer Weg 15, 44227 Dortmund, Germany – sequence: 2 givenname: Thomas surname: Bäck fullname: Bäck, Thomas organization: NuTech Solutions GmbH, Martin-Schmeißer Weg 15, 44227 Dortmund, Germany |
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| Copyright | 2003 Elsevier Ltd |
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| DOI | 10.1016/B978-008044046-0.50588-1 |
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| References | Deb (bib7) 2001 Emmerich M, Bäck T, Willmes L. Asynchronous evolution strategies for distributed direct optimisation. In: Giannakoglou K, Tsahalis D, Periaux J, Papailiou K, Fogarty T (Eds), Evolutionary Methods for Design, Optimisation and Control. Barcelona, 2002. Schwefel, Bäck (bib3) 1998 Bäck, Fogel, Michalewicz (bib2) 1997 Naujoks B, Willmes L, Haase W, Bäck T, Schütz M. Multipoint airfoil optimization using evolution strategies. In: Proceedings of the European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS'00), Barcelona 2000. Bäck (bib1) 1996 Hansen, Ostermeier, Gawelcyk (bib5) 1994; 4 Schwefel (bib4) 1995 |
| References_xml | – reference: Naujoks B, Willmes L, Haase W, Bäck T, Schütz M. Multipoint airfoil optimization using evolution strategies. In: Proceedings of the European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS'00), Barcelona 2000. – year: 1998 ident: bib3 article-title: Artificial Evolution: How and Why? publication-title: Genetic Algorithms and Evolution Strategies in Engineering and Computer Science – year: 1995 ident: bib4 publication-title: Evolution and Optimum Seeking – volume: 4 year: 1994 ident: bib5 article-title: A derandomized approach to self-adaptation of evolution strategies publication-title: Evolut Comput – reference: Emmerich M, Bäck T, Willmes L. Asynchronous evolution strategies for distributed direct optimisation. In: Giannakoglou K, Tsahalis D, Periaux J, Papailiou K, Fogarty T (Eds), Evolutionary Methods for Design, Optimisation and Control. Barcelona, 2002. – year: 1996 ident: bib1 publication-title: Evolutionary Algorithms in Theory and Practice – year: 2001 ident: bib7 publication-title: Multi-Objective Optimization using Evolutionary Algorithms – year: 1997 ident: bib2 publication-title: Handbook of Evolutionary Computation |
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| Title | Evolution strategies for engineering design optimisation |
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