Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/28174
Title: Hybrid multi-objective shape design optimization using Taguchi's method and genetic algorithm
Authors: Uludağ Üniversitesi/Mühendislik Fakültesi.
0000-0002-8297-0777
0000-0003-1790-6987
Yıldız, Ali Rıza
Öztürk, Nursel
Kaya, Necmettin
Öztürk, Ferruh
AAG-9336-2021
R-4929-2018
F-7426-2011
AAG-9923-2021
7102365439
7005688805
7005013334
56271685800
Keywords: Genetic algorithms
Multi-objective optimization
Shape optimization
Taguchi's method
Topology optimization
Structural optimization
Neural-network
Search
Parameter estimation
Robust parameters
Shape optimization
Vehicle components
Genetic algorithms
Multiobjective optimization
Problem solving
Taguchi methods
Issue Date: Oct-2007
Publisher: Springer
Citation: Yıldız, A. R. (2007). "Hybrid multi-objective shape design optimization using Taguchi's method and genetic algorithm". Structural and Multidisciplinary Optimization, 34(4), 317-332.
Abstract: This research is based on a new hybrid approach, which deals with the improvement of shape optimization process. The objective is to contribute to the development of more efficient shape optimization approaches in an integrated optimal topology and shape optimization area with the help of genetic algorithms and robustness issues. An improved genetic algorithm is introduced to solve multi-objective shape design optimization problems. The specific issue of this research is to overcome the limitations caused by larger population of solutions in the pure multi-objective genetic algorithm. The combination of genetic algorithm with robust parameter design through a smaller population of individuals results in a solution that leads to better parameter values for design optimization problems. The effectiveness of the proposed hybrid approach is illustrated and evaluated with test problems taken from literature. It is also shown that the proposed approach can be used as first stage in other multi-objective genetic algorithms to enhance the performance of genetic algorithms. Finally, the shape optimization of a vehicle component is presented to illustrate how the present approach can be applied for solving multi-objective shape design optimization problems.
URI: https://doi.org/10.1007/s00158-006-0079-x
https://link.springer.com/article/10.1007%2Fs00158-006-0079-x
http://hdl.handle.net/11452/28174
ISSN: 16151488
Appears in Collections:Scopus
Web of Science

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