Volltext-Downloads (blau) und Frontdoor-Views (grau)

Bionic optimization in structural design : stochastically based methods to improve the performance of parts and assemblies

  • 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.

Export metadata

Additional Services

Share in Twitter Search Google Scholar


Editor of HS Reutlingen:Steinbuch, Rolf
Editor of HS Reutlingen:Gekeler, Simon
Place of publication:Berlin, Heidelberg
Editor:Rolf Steinbuch, Simon Gekeler
Document Type:Anthology
Publication year:2016
Tag:Bionik; Evolutionärer Algorithmus; Neuronales Netz; Schwarmintelligenz; Strukturoptimierung
Page Number:160
PPN:Im Katalog der Hochschule Reutlingen ansehen
DDC classes:620 Ingenieurwissenschaften und Maschinenbau
Open access?:Nein
Licence (German):License Logo  In Copyright - Urheberrechtlich geschützt