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This failure warranted implementing multiple variable optimization technique.
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Heat transfer coefficients were maximized individually and simultaneously with minimal RI using multiple-variable optimization.
The key merits of ADMM is its ability to split or decouple multiple variables in optimization problems, which enables one to find a solution to a large-scale global optimization problem by coordinating solutions to smaller sub-problems.
On the other hand, GA is a powerful tool to cope with a complicated optimization problem with multiple variables to optimize.
Therefore, we attempt to transform the above maximum problem with multiple variables into a single variable optimization problem.
Furthermore, it could consider multiple variables during the optimization process using principles of operations research and systems analysis.
A multiple-variable dynamic optimization of fed-batch reactor for biomass production is studied using a differential geometry approach.
We propose the maximization of EE for the SSR scheme as a multiple variable-based, non-convex optimization problem and provide approximations to reduce it to a quasi-convex optimization.
Many control problems involve simultaneous optimization of multiple variables that competing with each other.
This implementation benefits from global information about metrics and key performance indicators (KPIs), as well as computational capacity to run powerful optimization algorithms involving multiple variables or cells.
Optimization and analysis of multiple variables in an experiment can be achieved by response surface methodology (RSM) and Box Behnken design (BBD) (Woraharn et al. 2015).
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