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Particle swarm optimization (PSO) is successfully implemented to optimize the parameters in Well Placement Optimization (WPO) and Well Control Optimization (WCO).
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.
One simple technique has been implemented to optimize the display of larger molecules.
Then, what strategies are implemented to optimize the cost function at the minimal cost?
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A Doehlert design was implemented to optimize this process.
Then, a central composite design (CCD) was implemented to optimize both the separation and the detection of plasticizers.
Here, GA has been implemented to optimize and determine the parameters for different fast chemical exchange models [21].
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 statistical experimental design method (Taguchi method with L9 orthogonal array design) was implemented to optimize experimental conditions for the preparation of Al2O3 nano-porous membrane.
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