Metamodel-based multi-objective robust design optimization of structures

Martínez-Frutos, Jesús; Marti-Montrull, P.

doi:10.2495/op120041

Cited by 5 publications

(2 citation statements)

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“…Previously, robust design approach has been combined with some different methods in multi-objective optimization such as the weighted sum method (Zadeh, 1963), goal programming (Charnes & Cooper, 1977), physical programming (Messac & Ismail-Yahaya, 2002), compromise programming (Chen et al, 1999), desirability function (Costa et al, 2011) and Lp metrics methods (Miettinen, 2012). Recently, some developed methods have been proposed as evolutionary algorithms such as simulated annealing (Suman & Kumar, 2006), particle swarm optimization (Parsopoulos & Vrahatis, 2002) and non-dominated sorting genetic algorithm (Deb et al, 2002), and Non-dominated Sorting Genetic Algorithm II (NSGA-II) (Martınez-Frutos & Marti-Montrull, 2012). …”

Section: Multi-objective Robust Optimizationmentioning

confidence: 99%

An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

Parnianifard¹,

Azfanizam²,

Ariffin³

et al. 2018

10.5267/j.ijiec

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Nowadays, process optimization has been an interest in engineering design for improving the performance and reducing cost. In practice, in addition to uncertainty or noise parameters, a comprehensive optimization model must be able to attend other circumstances which might be imposed in problems under real operational conditions such as dynamic objectives, multiresponses, various probabilistic distribution, discrete and continuous data, physical constraints to design parameters, performance cost, computational complexity and multi-process environment. The main goal of this paper is to give a general overview on topics with brief systematic review and concise discussions about the recent development on comprehensive robust design optimization methods under hybrid aforesaid circumstances. Both optimization methods of mathematical programming based on Taguchi approach and robust optimization based on scenario sets are briefly described. Metamodels hybrid robust design is discussed as an appropriate methodology to decrease computational complexity in problems under uncertainty. In this context, the authors' policy is to choose important topics for giving a systematic picture to those who wish to be more familiar with recent studies about robust design optimization hybrid metamodels, also by attending real circumstances in practice. In particular, production and project management are considered as two important methodologies that could be improved by applications of advanced robust design combining with metamodel methods.

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Section: Multi-objective Robust Optimizationmentioning

confidence: 99%

An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

Parnianifard¹,

Azfanizam²,

Ariffin³

et al. 2018

10.5267/j.ijiec

View full text Add to dashboard Cite

show abstract

“…Los trabajos presentados anteriormente utilizan parte de soluciones no-dominadas para la actualización del meta-modelo. Esta forma de proceder proporciona en ocasiones soluciones satisfactorias convergiendo al frente de Pareto real (Gaspar-Cunha y Vieira, 2004;Martínez-Frutos y Martí, 2011;Voutchkov y Keane, 2006). Sin embargo, al igual que ocurre en el problema con unúnico objetivo, dicho enfoqueúnicamente se basa en las solucionesóptimas para la actualización del meta-modelo (explotación) y no considera la incertidumbre epistémica del mismo (exploración).…”

Section: Introductionunclassified