NDLI: Efficient resource allocation for genetic algorithm based multi-objective optimization with 1,000 simulations

Content Provider	Springer Nature Link
Author	Goel, Tushar Stander, Nielen Lin, Yih Yih
Copyright Year	2009
Abstract	This study pertains to practical use of the GA for industrial applications where only a limited number of simulations can be afforded. Specifically, an attempt is made to find an efficient allocation of the total simulation budget (population size and number of generations) for constrained multi-objective optimization. A study is conducted to seek improvements while restricting the number of simulations to 1,000. Parallelization is exploited using concurrent simulations for each GA generation on a HP quad-core cluster, and resulted in a significant time savings. Furthermore, the efficient distribution of computational effort to achieve the greatest improvement in performance was explored. Two analytical examples as well as an automotive crashworthiness simulation of a finite element model with 58,000 elements were used as test examples. Various population sizes and numbers of generations were tried while limiting the total number of simulations to 1,000. The optimization performance was compared with Monte-Carlo and space filling sampling methods. It was observed that using the GA, many feasible and trade-off solutions could be found. It is shown that allowing a large number of generations is beneficial to get good trade-off solutions. For the vehicle design, significant improvements in the performance were observed. This example also suggests that, for problems with a small feasible region, the number of feasible solutions can be significantly increased in the first few generations involving about 200 simulations.
Starting Page	421
Ending Page	432
Page Count	12
File Format	PDF
ISSN	1615147X
Journal	Structural and Multidisciplinary Optimization
Volume Number	41
Issue Number	3
e-ISSN	16151488
Language	English
Publisher	Springer-Verlag
Publisher Date	2009-08-19
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Multi-objective evolutionary algorithm Crashworthiness Population sensitivity Resource allocation NSGA-II Optimization Engineering Design Computational Mathematics and Numerical Analysis Theoretical and Applied Mechanics
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Computer Graphics and Computer-Aided Design Control and Systems Engineering Computer Science Applications Software

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