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The particle swarm optimization (PSO), as it is called now, does not require any gradient information of the function to be optimized, uses only primitive mathematical operators and is conceptually very simple.
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These variables were accurately optimized using multivariate optimization strategies.
The objective function is optimized using non-linear, multi-resolution optimization techniques.
PCR assays were optimized using gradient PCRs to determine the optimal annealing temperature and primer concentration.
Mg2+ concentration was optimized using the condition determined to be optimal in the previous section.
These were subsequently optimized using the Levenberg Marquardt minimizer from the MATLAB optimization toolbox.
Molecular structures were optimized using DFT B3LYP/6-31 + G(d).
Enzyme production was optimized using statistical approach.
Ligands were optimized using this concept.
The stacking sequence was optimized using Genetic Algorithm.
Designs are first optimized using Finite Element Analysis FEAA) tools.
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