Nash evolutionary algorithms : Testing problem size in reconstruction problems in frame structures
Greiner, D., Periaux, J., Emperador, J.M., Galván, B., & Winter, G. (2016). Nash evolutionary algorithms : Testing problem size in reconstruction problems in frame structures. In M. Papadrakakis, V. Papadopoulos, G. Stefanou, & V. Plevris (Eds.), ECCOMAS Congress 2016 : VII European Congress on Computational Methods in Applied Sciences and Engineering : Proceedings, Volume II (pp. 3493-3504). National Technical University of Athens; ECCOMAS. https://doi.org/10.7712/100016.2050.6785
Date
2016Copyright
© the Authors, 2016.
The use of evolutionary algorithms has been enhanced in recent years for solving
real engineering problems, where the requirements of intense computational calculations are
needed, especially when computational engineering simulations are involved (use of finite
element method, boundary element method, etc). The coupling of game-theory concepts in
evolutionary algorithms has been a recent line of research which could enhance the efficiency
of the optimum design procedure and the quality of the design solutions achieved. They have
been applied in several fields of engineering and sciences, mainly, in aeronautical and structural
engineering (e.g: in computational fluid dynamics and solid mechanics problems).
Among them, Nash-evolutionary algorithms (Nash-EAs) have been recently applied in the
single-objective reconstruction inverse design problem in structural engineering (aiming to
obtain the structure whose maximum stresses match those stresses considered as references),
with successful speed-up of the structural optimum search. Several test cases of different
search space size bar structures are handled here, with bar sized structures up to 105 bar elements.
Particularly, frames -bar structures with rigid nodes where bending moment and
shear effort should also be taken into consideration- are handled here. Influence of the structural
size in the comparative performance of Nash-EAs will be investigated and tested. The
performance of Nash-EAs improves significantly the one of the standard panmictic evolutionary
algorithms. According to the results shown here, this advantage is greater when the problem
size increases.
...
Publisher
National Technical University of Athens; ECCOMASParent publication ISBN
978-618-82844-0-1Conference
European congress on computational methods in applied sciences and engineeringIs part of publication
ECCOMAS Congress 2016 : VII European Congress on Computational Methods in Applied Sciences and Engineering : Proceedings, Volume IIKeywords
Original source
http://www.eccomas.org/cvdata/cntr1/spc7/dtos/img/mdia/eccomas-2016-vol-2.pdfPublication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/26371382
Metadata
Show full item recordCollections
Related items
Showing items with similar title or keywords.
-
Distributed multi-objective optimization methods for shape design using evolutionary algorithms and game strategies
Leskinen, Jyri (University of Jyväskylä, 2012) -
Thirty Years of Progress in Single/Multi-disciplinary Design Optimization with Evolutionary Algorithms and Game Strategies in Aeronautics and Civil Engineering
Periaux, Jacques; Tuovinen, Tero (Springer, 2023)This article reviews the major improvements in efficiency and quality of evolutionary multi-objective and multi-disciplinary design optimization techniques achieved during 1994–2021. At first, we introduce briefly Evolutionary ... -
An Interactive Simple Indicator-Based Evolutionary Algorithm (I-SIBEA) for Multiobjective Optimization Problems
Chugh, Tinkle; Sindhya, Karthik; Hakanen, Jussi; Miettinen, Kaisa (Springer, 2015)This paper presents a new preference based interactive evolutionary algorithm (I-SIBEA) for solving multiobjective optimization problems using weighted hypervolume. Here the decision maker iteratively provides her/his ... -
A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms
Chugh, Tinkle; Sindhya, Karthik; Hakanen, Jussi; Miettinen, Kaisa (Springer, 2019)Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, ... -
A Data-Driven Surrogate-Assisted Evolutionary Algorithm Applied to a Many-Objective Blast Furnace Optimization Problem
Chugh, Tinkle; Chakraborti, Nirupam; Sindhya, Karthik; Jin, Yaochu (Taylor & Francis Inc., 2017)A new data-driven reference vector-guided evolutionary algorithm has been successfully implemented to construct surrogate models for various objectives pertinent to an industrial blast furnace. A total of eight objectives ...