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The Kriging surrogate modeling technique and multiobjective particle swarm optimization (MOPSO) algorithm were implemented to optimize the FGF-filled bumper beam.
Secondly, a multi-objective particle swarm optimization (MOPSO) algorithm is implemented to optimize the DSMC parameters in order to decrease the normalized angle error of the pole and normalized distance error of the cart, simultaneously.
The proposed procedure which included optimization and sensitivity analysis was implemented to optimize the design parameters of the column and obtain the sensitive variables simultaneously, without requirement to run various simulations.
The design approach is implemented to optimize a real design mold and the final optimal design is prototyped in SLA and the manufacturability in PBF has been discussed.
A Doehlert design was implemented to optimize this process.
They are implemented to optimize data sets obtained from simulation of the fifth order lowpass filter.
Other than design of the overall on-board system, reinforcement learning is implemented to optimize the strategy toward the actuation of the cuttlefish.
Taguchi method based on three levels, six variables L27 orthogonal array robust design was implemented to optimize experimental conditions.
In order to improve the machining performance, the Box Behnken method was implemented to optimize the parameters.
Pole crossover design is implemented to optimize the filter parameters with an independent choice of belt velocity and control gain.
Principal component analysis and Box Behnken experimental design was implemented to optimize the agglomerates with good micromeritics and mechanical properties.
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