Decomposition-based design optimization method using genetic co-evolution

Ryoo, Juntaek; Hajela, P.

doi:10.1080/03052150410001657587

Cited by 4 publications

(1 citation statement)

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“…The considered class of design optimization problem with simulation-based objective functions is in engineering practice often solved using genetic algorithms (Dhingra and Rao 1992;Atiqullah and Rao 2000;Ryoo and Hajela 2004). An alternative is to use a nonlinear optimization algorithm, designed for non-convex optimization problems or simulation-based objective functions, and which can also handle discrete variables.…”

Section: Tested Algorithmsmentioning

confidence: 99%

A splitting algorithm for simulation-based optimization problems with categorical variables

Nedelkova

Cromvik

Lindroth

et al. 2018

Engineering Optimization

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In the design of complex products, some product components can only be chosen from a finite set of options. Each option then corresponds to a multidimensional point representing the specifications of the chosen components. A splitting algorithm that explores the resulting discrete search space and is suitable for optimization problems with simulation-based objective functions is presented. The splitting rule is based on the representation of a convex relaxation of the search space in terms of a minimum spanning tree and adopts ideas from multilevel coordinate search. The objective function is underestimated on its domain by a convex quadratic function. The main motivation is the aim to find-for a vehicle and environment specification-a configuration of the tyres such that the energy losses caused by them are minimized. Numerical tests on a set of optimization problems are presented to compare the performance of the algorithm developed with that of other existing algorithms. ARTICLE HISTORY

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Section: Tested Algorithmsmentioning

confidence: 99%