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Meanwhile, the particle swarm optimization (PSO) algorithm is utilized to optimize the SRITCSD method, which is exploited to select a nearly optimal combination of features and a set of parameters utilized in the SVM.
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.
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.
Additionally, the performance measure is utilized to optimize the geometry of the structure, determine the optimal ratio of mass in the absorber to mass of the host structure, and determine the frequencies of the absorbers.
In the upper-level formulation, the optimal pattern search (OPS) algorithm is utilized to optimize the proportion of wind and PV capacity.
Moreover, the multi-objective NSGA-II genetic algorithm is utilized to optimize the design of the TEG both in terms of optimal output power and in terms of mass.
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.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com