6533b81ffe1ef96bd1278771
RESEARCH PRODUCT
Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system
Pekka MakkonenYaochu JinKarthik SindhyaKaisa MiettinenTinkle ChughTomas Kratkysubject
ta1130209 industrial biotechnologyMathematical optimizationnumerical modelsOptimization problemlineaarinen optimointiLinear programmingComputer sciencesoftwarehydraulijärjestelmätventilationEvolutionary algorithmlinear programming02 engineering and technologyFunction (mathematics)Set (abstract data type)resistance020901 industrial engineering & automationhydraulic systemsilmanvaihto0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingShape optimizationoptimizationdescription
We tackle three different challenges in solving a real-world industrial problem: formulating the optimization problem, connecting different simulation tools and dealing with computationally expensive objective functions. The problem to be optimized is an air intake ventilation system of a tractor and consists of three computationally expensive objective functions. We describe the modeling of the system and its numerical evaluation with a commercial software. To obtain solutions in few function evaluations, a recently proposed surrogate-assisted evolutionary algorithm K-RVEA is applied. The diameters of four different outlets of the ventilation system are considered as decision variables. From the set of nondominated solutions generated by K-RVEA, a decision maker having substance knowledge selected the final one based on his preferences. The final selected solution has better objective function values compared to the baseline solution of the initial design. A comparison of solutions with K-RVEA and RVEA (which does not use surrogates) is also performed to show the potential of using surrogates. peerReviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-06-01 |