The impact of multi-objective numerical optimization in Biomedical Engineering

Author(s): 
Luca Fuligno (Enginsoft), Alberto Clarich (ESTECO)

This article focuses mainly on the application of design of experiments, multi-objective optimization and robustness analysis technologies. At first, a brief overview of these techniques is given, to show how they were applied to solve several biomedical engineering problems. Then an example is presented, showing the application of these techniques in the design of an artificial lung. Optimization techniques can be applied to any design process, with the aim of enhancing performance, reducing costs, and improving reliability and durability of any device.

Among the available state-of-the-art features of the optimizers, the following should be stressed: the true multiobjectivity, the capability to handle discrete and continuous parameters in mixed problems, the performance of the algorithms versus their efficiency in terms of accuracy and robustness.

Design of Experiments (DOE) techniques are mainly oriented to plan efficient experimental or simulation campaigns, taking into account simultaneously many different variable parameters. Different DOE plans can be used for different purposes: parameter screening for main effects and the detection of interactions, generation of starting configuration sets for stochastic optimization, verification of parameter sensitivities, etc. The DOE, optimization and robust design technologies proved to be effective in several biomedical applications.


Their implementation, based on the modeFRONTIER environment, is straightforward, and can guarantee great improvements in design efficiency, time and cost savings, and productivity.

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