Evolutionary optimization of computationally expensive problems via surrogate modeling
Ong, Yew S., Nair, Prasanth B. and Keane, Andrew J. (2003) Evolutionary optimization of computationally expensive problems via surrogate modeling. AIAA Journal, 41, (4), 687-696.
We present a parallel evolutionary optimization algorithm that leverages surrogate models for solving computationally
expensive design problems with general constraints, on a limited computational budget. The essential backbone of our framework is an evolutionary algorithm coupled with a feasible sequential quadratic programming solver in the spirit of Lamarckian learning.We employ a trust-region approach for interleaving use of exact models for the objective and constraint functionswith computationally cheap surrogatemodels during local search. In contrast to earlier work, we construct local surrogatemodels using radial basis functionsmotivated by the principle of transductive inference. Further, the present approach retains the intrinsic parallelism of evolutionary algorithms and can hence be readily implemented on grid computing infrastructures. Experimental results are presented for some benchmark test functions and an aerodynamic wing design problem to demonstrate that our algorithm converges to good designs on a limited computational budget.
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
|Divisions:||University Structure - Pre August 2011 > School of Engineering Sciences
University Structure - Pre August 2011 > Professional Services > Information Systems Services
|Date Deposited:||22 Mar 2006|
|Last Modified:||27 Mar 2014 18:11|
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