A sequential surrogate-based multiobjective optimization method: effect of initial data set

Process optimization based on high-fidelity computer simulations or real experimentation is commonly expensive. Therefore, surrogate models are frequently used to reduce the computational or experimental cost. However, surrogate models need to achieve a maximum accuracy with a limited number of samp...

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Bibliographic Details
Published in:Wireless networks 2020-11, Vol.26 (8), p.5727-5750
Main Authors: Villarreal-Marroquin, Maria Guadalupe, Mosquera-Artamonov, Jose Daniel, Cruz, Celso E., Castro, Jose M.
Format: Article
Language:English
Subjects:
Communications Engineering
Computer Communication Networks
Electrical Engineering
Engineering
Experimentation
Industrial applications
IT in Business
Multiple objective analysis
Networks
Optimization
Pareto optimization
Sequential sampling
Wireless networks
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Summary:Process optimization based on high-fidelity computer simulations or real experimentation is commonly expensive. Therefore, surrogate models are frequently used to reduce the computational or experimental cost. However, surrogate models need to achieve a maximum accuracy with a limited number of sampled points. Sequential sampling is a procedure in which sequentially surrogates are fitted and each surrogate defines the points that need to be sampled and used to fit the next model. For optimization purposes, points are sampled on regions of high potential for the optimal solutions. In this work, we first compared the effect of using different initial sets of points (experimental designs) in a sequential surrogate-based multiobjective optimization method. The optimization method is tested on five benchmark problems and the performance is quantified based on the total number of function evaluations and the quality of the final Pareto Front. Then an industrial applications on titanium welding is presented to show the use of the method. The case study is based on real experimental data.
ISSN:1022-0038
1572-8196
DOI:10.1007/s11276-019-02212-2