國立高雄大學圖資館 |

Language: English

Back

Bionic optimization in structural de...

Gekeler, Simon.

Bionic optimization in structural designstochastically based methods to improve the performance of parts and assemblies /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bionic optimization in structural designedited by Rolf Steinbuch, Simon Gekeler.
Reminder of title:	stochastically based methods to improve the performance of parts and assemblies /
other author:	Steinbuch, Rolf.
Published:	Berlin, Heidelberg :Springer Berlin Heidelberg :2016.
Description:	xii, 160 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Engineering.
Online resource:	http://dx.doi.org/10.1007/978-3-662-46596-7
ISBN:	9783662465967$q(electronic bk.)

Bionic optimization in structural designstochastically based methods to improve the performance of parts and assemblies /
Bionic optimization in structural designstochastically based methods to improve the performance of parts and assemblies /[electronic resource] :edited by Rolf Steinbuch, Simon Gekeler. - Berlin, Heidelberg :Springer Berlin Heidelberg :2016. - xii, 160 p. :ill. (some col.), digital ;24 cm.

Motivation -- Bionic Optimization Strategies -- Problems and Limitations of Bionic Optimization -- Application to CAE Problems -- Applications of Bionic Optimization -- Current Fields of Interest -- Future Tasks in Optimization.

The book provides suggestions on how to start using bionic optimization methods, including pseudo-code examples of each of the important approaches and outlines of how to improve them. The most efficient methods for accelerating the studies are discussed. These include the selection of size and generations of a study's parameters, modification of these driving parameters, switching to gradient methods when approaching local maxima, and the use of parallel working hardware. Bionic Optimization means finding the best solution to a problem using methods found in nature. As Evolutionary Strategies and Particle Swarm Optimization seem to be the most important methods for structural optimization, we primarily focus on them. Other methods such as neural nets or ant colonies are more suited to control or process studies, so their basic ideas are outlined in order to motivate readers to start using them. A set of sample applications shows how Bionic Optimization works in practice. From academic studies on simple frames made of rods to earthquake-resistant buildings, readers follow the lessons learned, difficulties encountered and effective strategies for overcoming them. For the problem of tuned mass dampers, which play an important role in dynamic control, changing the goal and restrictions paves the way for Multi-Objective-Optimization. As most structural designers today use commercial software such as FE-Codes or CAE systems with integrated simulation modules, ways of integrating Bionic Optimization into these software packages are outlined and examples of typical systems and typical optimization approaches are presented. The closing section focuses on an overview and outlook on reliable and robust as well as on Multi-Objective-Optimization, including discussions of current and upcoming research topics in the field concerning a unified theory for handling stochastic design processes.

ISBN: 9783662465967$q(electronic bk.)

Standard No.: 10.1007/978-3-662-46596-7doiSubjects--Topical Terms:

210888
Engineering.

LC Class. No.: TA174

Dewey Class. No.: 620.0042

Bionic optimization in structural designstochastically based methods to improve the performance of parts and assemblies /
LDR:03208nmm a2200325 a 4500 001 481680
003 DE-He213
005 20160722144002.0
006 m d
007 cr nn 008maaau
008 161007s2016 gw s 0 eng d
020 $a 9783662465967$q(electronic bk.)
020 $a 9783662465950$q(paper)
024 7 $a 10.1007/978-3-662-46596-7 $2 doi
035 $a 978-3-662-46596-7
040 $a GP $c GP
041 0 $a eng
050 4 $a TA174
072 7 $a TBD $2 bicssc
072 7 $a TEC016020 $2 bisacsh
072 7 $a TEC016000 $2 bisacsh
082 0 4 $a 620.0042 $2 23
090 $a TA174 $b .B615 2016
245 0 0 $a Bionic optimization in structural design $h [electronic resource] : $b stochastically based methods to improve the performance of parts and assemblies / $c edited by Rolf Steinbuch, Simon Gekeler.
260 $a Berlin, Heidelberg : $b Springer Berlin Heidelberg : $b Imprint: Springer, $c 2016.
300 $a xii, 160 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Motivation -- Bionic Optimization Strategies -- Problems and Limitations of Bionic Optimization -- Application to CAE Problems -- Applications of Bionic Optimization -- Current Fields of Interest -- Future Tasks in Optimization.
520 $a The book provides suggestions on how to start using bionic optimization methods, including pseudo-code examples of each of the important approaches and outlines of how to improve them. The most efficient methods for accelerating the studies are discussed. These include the selection of size and generations of a study's parameters, modification of these driving parameters, switching to gradient methods when approaching local maxima, and the use of parallel working hardware. Bionic Optimization means finding the best solution to a problem using methods found in nature. As Evolutionary Strategies and Particle Swarm Optimization seem to be the most important methods for structural optimization, we primarily focus on them. Other methods such as neural nets or ant colonies are more suited to control or process studies, so their basic ideas are outlined in order to motivate readers to start using them. A set of sample applications shows how Bionic Optimization works in practice. From academic studies on simple frames made of rods to earthquake-resistant buildings, readers follow the lessons learned, difficulties encountered and effective strategies for overcoming them. For the problem of tuned mass dampers, which play an important role in dynamic control, changing the goal and restrictions paves the way for Multi-Objective-Optimization. As most structural designers today use commercial software such as FE-Codes or CAE systems with integrated simulation modules, ways of integrating Bionic Optimization into these software packages are outlined and examples of typical systems and typical optimization approaches are presented. The closing section focuses on an overview and outlook on reliable and robust as well as on Multi-Objective-Optimization, including discussions of current and upcoming research topics in the field concerning a unified theory for handling stochastic design processes.
650 0 $a Engineering. $3 210888
650 0 $a Computer simulation. $3 182122
650 0 $a Computational intelligence. $3 210824
650 0 $a Engineering design. $3 190286
650 2 4 $a Engineering Design. $3 273752
650 2 4 $a Simulation and Modeling. $3 273719
650 2 4 $a Computational Intelligence. $3 338479
700 1 $a Steinbuch, Rolf. $3 737863
700 1 $a Gekeler, Simon. $3 737864
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-662-46596-7
950 $a Engineering (Springer-11647)