A new multi-objective discrete robust optimization algorithm for engineering design

•A novel multi-objective discrete robust optimization algorithm was proposed.•The presented method was verified by a mathematical example and a ten-bar truss design.•Robust optimization design for the crashworthiness design of full vehicle was conducted. This paper proposes a novel multi-objective d...

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Vydáno v:Applied Mathematical Modelling Ročník 53; s. 602 - 621
Hlavní autoři: Sun, Guangyong, Zhang, Huile, Fang, Jianguang, Li, Guangyao, Li, Qing
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Inc 01.01.2018
Elsevier BV
Témata:
Classification
Crashworthiness
Decision analysis
Decision making
Design engineering
Design optimization
Genetic algorithms
Grey relational analysis
Impact strength
Multi-objective discrete robust optimization
Multiple criteria decision making
Multiple objective analysis
Optimization algorithms
Principal component analysis
Principal components analysis
Robustness (mathematics)
Sorting algorithms
Successive Taguchi method
Taguchi methods
Grey relational analysis
Multi-objective discrete robust optimization
Successive Taguchi method
Principal component analysis
ISSN:0307-904X, 1088-8691, 0307-904X
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Shrnutí:•A novel multi-objective discrete robust optimization algorithm was proposed.•The presented method was verified by a mathematical example and a ten-bar truss design.•Robust optimization design for the crashworthiness design of full vehicle was conducted. This paper proposes a novel multi-objective discrete robust optimization (MODRO) algorithm for design of engineering structures involving uncertainties. In the present MODRO procedure, grey relational analysis (GRA), coupled with principal component analysis (PCA), was used as a multicriteria decision making model for converting multiple conflicting objectives into one unified cost function. The optimization process was iterated using the successive Taguchi approach to avoid the limitation that the conventional Taguchi method fails to deal with a large number of design variables and design levels. The proposed method was first verified by a mathematical benchmark example and a ten-bar truss design problem; and then it was applied to a more sophisticated design case of full scale vehicle structure for crashworthiness criteria. The results showed that the algorithm is able to achieve an optimal design in a fairly efficient manner attributable to its integration with the multicriteria decision making model. Note that the optimal design can be directly used in practical applications without further design selection. In addition, it was found that the optimum is close to the corresponding Pareto frontier generated from the other approaches, such as the non-dominated sorting genetic algorithm II (NSGA-II), but can be more robust as a result of introduction of the Taguchi method. Due to its independence on metamodeling techniques, the proposed algorithm could be fairly promising for engineering design problems of high dimensionality.
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ISSN:0307-904X
1088-8691
0307-904X
DOI:10.1016/j.apm.2017.08.016