Formal and structural multiplicity in early truss design

2016 ◽  
pp. 1334-1341
Author(s):  
M Rinke
Keyword(s):  
Truss Design

Automatic Truss Design by Optimized Growth

1994 ◽  
Vol 120 (10) ◽  
pp. 3063-3070 ◽  
Author(s):  
William Keith Rule
Keyword(s):  
Truss Design

Closure to “Space Truss Design in Inelastic Range”

1980 ◽  
Vol 106 (7) ◽  
pp. 1656-1657
Author(s):  
Lewis C. Schmidt ◽  
Peter R. Morgan ◽  
John A. Clarkson
Keyword(s):  
Truss Design ◽  
Space Truss

A novel approach to discrete truss design problems using mixed integer neighborhood search

2018 ◽  
Vol 58 (6) ◽  
pp. 2411-2429 ◽  
Author(s):  
Mohammad Shahabsafa ◽  
Ali Mohammad-Nezhad ◽  
Tamás Terlaky ◽  
Luis Zuluaga ◽  
Sicheng He ◽  
...  
Keyword(s):  
Mixed Integer ◽  
Neighborhood Search ◽  
Design Problems ◽  
Truss Design ◽  
Novel Approach

A Dual Approach to Optimum Truss Design

1976 ◽  
Vol 4 (3) ◽  
pp. 235-255 ◽  
Author(s):  
Andrew B. Templeman
Keyword(s):  
Dual Approach ◽  
Truss Design

Sensitivity analysis of fuzzy-genetic approach applied to cabled-truss design

2014 ◽  
Vol 26 (4) ◽  
pp. 1931-1942 ◽  
Author(s):  
Vitor C. Finotto ◽  
Wilson R.L. da Silva ◽  
Petr Štemberk ◽  
Michael Valášek
Keyword(s):  
Sensitivity Analysis ◽  
Genetic Approach ◽  
Truss Design ◽  
Fuzzy Genetic

scholarly journals A Study on Scalable Representations for Evolutionary Optimization of Ground Structures

2012 ◽  
Vol 20 (3) ◽  
pp. 453-472 ◽  
Author(s):  
Alexandre Devert ◽  
Thomas Weise ◽  
Ke Tang
Keyword(s):  
State Of The Art ◽  
Computational Cost ◽  
Global Optimum ◽  
Design Parameters ◽  
Optimization Scheme ◽  
Design Problems ◽  
Truss Design ◽  
Objective Value ◽  
Ground Structures ◽  
2D Space

This paper presents a comparative study of two indirect solution representations, a generative and an ontogenic one, on a set of well-known 2D truss design problems. The generative representation encodes the parameters of a trusses design as a mapping from a 2D space. The ontogenic representation encodes truss design parameters as a local truss transformation iterated several times, starting from a trivial initial truss. Both representations are tested with a naive evolution strategy based optimization scheme, as well as the state of the art HyperNEAT approach. We focus both on the best objective value obtained and the computational cost to reach a given level of optimality. The study shows that the two solution representations behave very differently. For experimental settings with equal complexity, with the same optimization scheme and settings, the generative representation provides results which are far from optimal, whereas the ontogenic representation delivers near-optimal solutions. The ontogenic representation is also much less computationally expensive than a direct representation until very close to the global optimum. The study questions the scalability of the generative representations, while the results for the ontogenic representation display much better scalability.

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