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Bionic Optimization in Structural Design

Stochastically Based Methods to Improve the Performance of Parts and Assemblies

  • Rolf Steinbuch
  • Simon Gekeler

Table of contents

  1. Front Matter
    Pages i-xii
  2. Rolf Steinbuch
    Pages 1-10
  3. Rolf Steinbuch, Julian Pandtle, Simon Gekeler, Tatiana Popova, Frank Schweickert, Christoph Widmann et al.
    Pages 11-56
  4. Tatiana Popova, Iryna Kmitina, Rolf Steinbuch, Simon Gekeler
    Pages 57-77
  5. Rolf Steinbuch, Andreas Fasold-Schmid, Simon Gekeler, Dmitrii Burovikhin
    Pages 79-99
  6. Rolf Steinbuch, Iryna Kmitina, Tatiana Popova, Simon Gekeler, Oskar Glück, Ashish Srivastava
    Pages 101-123
  7. Rolf Steinbuch, Iryna Kmitina, Nico Esslinger
    Pages 125-146
  8. Simon Gekeler, Rolf Steinbuch
    Pages 147-153
  9. Back Matter
    Pages 155-160

About this book

Introduction

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.

Keywords

Bionic Optimization CAE-processes, Deterministic Optimization Gradient methods Evolutionary Approaches Evolutionary Optimization Gradient Methods Neural Nets Optimization Optimization in Metal Forming Particle Swarm Optimization Simulation Structural Optimization Tuned Mass Dampers Virtual Product Development

Editors and affiliations

  • Rolf Steinbuch
    • 1
  • Simon Gekeler
    • 2
  1. 1.Reutlingen UniversityReutlingenGermany
  2. 2.Reutlingen UniversityReutlingenGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-662-46596-7
  • Copyright Information Springer-Verlag Berlin Heidelberg 2016
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-662-46595-0
  • Online ISBN 978-3-662-46596-7
  • Buy this book on publisher's site
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