TY - JOUR
T1 - A computational intelligence algorithm for expensive engineering optimization problems
AU - Tenne, Yoel
PY - 2012/8
Y1 - 2012/8
N2 - The modern engineering design optimization process often replaces laboratory experiments with computer simulations, which leads to expensive black-box optimization problems. Such problems often contain candidate solutions which cause the simulation to fail, and therefore they will have no objective value assigned to them, a scenario which degrades the search effectiveness. To address this, this paper proposes a new computational intelligence optimization algorithm which incorporates a classifier into the optimization search. The classifier predicts which solutions are expected to cause a simulation failure, and its prediction is used to bias the search towards solutions for which the simulation is expected to succeed. To further enhance the search effectiveness, the proposed algorithm continuously adapts during the search the type of model and classifier being used. A rigorous performance analysis using a representative application of airfoil shape optimization shows that the proposed algorithm outperformed existing approaches in terms of the final result obtained, and performed a search with a competitively low number of failed evaluations. Analysis also highlights the contribution of incorporating the classifier into the search, and of the model and classifier selection steps.
AB - The modern engineering design optimization process often replaces laboratory experiments with computer simulations, which leads to expensive black-box optimization problems. Such problems often contain candidate solutions which cause the simulation to fail, and therefore they will have no objective value assigned to them, a scenario which degrades the search effectiveness. To address this, this paper proposes a new computational intelligence optimization algorithm which incorporates a classifier into the optimization search. The classifier predicts which solutions are expected to cause a simulation failure, and its prediction is used to bias the search towards solutions for which the simulation is expected to succeed. To further enhance the search effectiveness, the proposed algorithm continuously adapts during the search the type of model and classifier being used. A rigorous performance analysis using a representative application of airfoil shape optimization shows that the proposed algorithm outperformed existing approaches in terms of the final result obtained, and performed a search with a competitively low number of failed evaluations. Analysis also highlights the contribution of incorporating the classifier into the search, and of the model and classifier selection steps.
KW - Classification
KW - Evolutionary algorithms
KW - Expensive optimization problems
KW - Model-selection modeling
UR - http://www.scopus.com/inward/record.url?scp=84862131579&partnerID=8YFLogxK
U2 - 10.1016/j.engappai.2012.03.009
DO - 10.1016/j.engappai.2012.03.009
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AN - SCOPUS:84862131579
SN - 0952-1976
VL - 25
SP - 1009
EP - 1021
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
IS - 5
ER -