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The analysis of the results provided the mathematical function of the effective and overall efficiencies based on the experimental variables that can be adopted to optimize the mirror positions at any given time.
The most logical QTL-class performance-derived novel breeding strategy could be adopted to optimize the breeding efficiency of small-to moderate-sized breeding programs in rice breeding improvement programs.
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Particle swarm optimization is adopted to optimize the parameter settings through the well-trained BP model, where each particle is assessed using fitness function.
Both dual- and triple-objective optimizations were adopted to optimize the shape of the inlet/outlet diffusion segment, the response surface methodology (RSM) was used to generate approximate functions relating to the objectives and design parameters, and the non-dominated sorting genetic algorithm (NSGA-II) was selected to conduct the optimizations.
Bothe single objective and multi-objective optimization are adopted to optimize the configurations of multi rows film cooling holes, and the Multi-Island Genetic Algorithm and The response surface approximation with the Non-dominated Sorting Genetic Algorithm (NSGA-II) are selected to conduct the optimizations.
In order to prevent the interaction between two types of variables and enhance global search ability of traditional particle swarm optimization (PSO), intelligent single particle optimizer (ISPO) is adopted to optimize this model.
Several different multiobjective optimization schemes were adopted to optimize the coated bead size and volume fraction, which reveal that the optimal design parameters of particle diameter and volume fraction are 100 μm – 35% and 38 μm – 17.5% for the cortical and cancellous bones respectively, agreeing with clinical data.
Further, different optimization methods are adopted to optimize TMD parameters when considering different performance indices.
In order to prevent the interaction between two types of variables and enhance global search ability, intelligent single particle optimizer (ISPO) is adopted to optimize this model.
The rated power, capacity, location of BESS, and three operation strategy control variables are decision variables, and intelligent single particle optimizer (ISPO) is adopted to optimize this model.
The model of DFIG for small signal stability analysis was presented in [3] and the particle swarm optimization (PSO) method was adopted to optimize the controller parameters.
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