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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.
After designing output feedback SMC, teaching and learning based optimization (TLBO) technique is utilized to optimize feedback gain and switching vector of the controller.
In the upper-level formulation, the optimal pattern search (OPS) algorithm is utilized to optimize the proportion of wind and PV capacity.
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.
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.
The desirability function technique is utilized to optimize the responses.
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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.
Response surface methodology as a multiple responses optimization technique was utilized to optimize several important factors such as the biomass content of the medium (w/v), inoculum size and agitation rate.
It is expected that the proposed steady-state model, parameter estimation and optimization methods can be utilized to optimize and design modem-type FCC processes under full and partial combustion modes.
Then the D-optimal experimental design software was utilized to optimize the preparation of the GO based catalysts.
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.
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