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And genetic algorithms (GAs), a general-purpose global optimization method, are utilized to optimize the membership functions of the fuzzy controller.
The biogeography based optimization (BBO) was also utilized to optimize the milling parameters.
After designing output feedback SMC, teaching and learning based optimization (TLBO) technique is utilized to optimize feedback gain and switching vector of the controller.
The derived models are utilized to optimize the amplifier structure through particle swarm optimization (PSO) to obtain a large resonance frequency subject to other performance constraints.
Then the D-optimal experimental design software was utilized to optimize the preparation of the GO based catalysts.
In the upper-level formulation, the optimal pattern search (OPS) algorithm is utilized to optimize the proportion of wind and PV capacity.
Statistical experimental design was utilized to optimize this adsorption process.
NSGA II is utilized to optimize the design.
The desirability function technique is utilized to optimize the responses.
Here, the ICA is utilized to optimize the skeletal structures.
Subsequently, DE is utilized to optimize the premise and consequent parameters of the model.
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