Sentence examples for shuffling complex from inspiring English sources

Complex datasets were randomised by shuffling complex membership while retaining the size of complexes.

Finally, the model is calibrated using the shuffled complex evolution (SCE-UA) algorithm, and model uncertainty is evaluated via the Bayesian method.

In contrast, the shuffled complex evolution algorithm was able to constrain a piece-wise linear function to closely match the shallow gradational water content profile.

The shuffled complex evolution method, as well as experimental results have been used together to calibrate the new zonal model and improve its prediction.

Compared to MOSCEM (Multiobjective Shuffled Complex Evolution Metropolis), an existing Monte Carlo sampling method for multi-objective optimization, MOMCMC exhibits significantly faster convergence to the Pareto optimal front.

The considered EAs are: GA, continuous ant colony optimization (CACO), particle swarm optimization (PSO), differential evolution (DE), artificial bee colony optimization (ABC), harmony search (HS), shuffled complex evolution (SCE), and simplex simulated annealing (SIMPSA).

Some 15 20 times the dimension of the problem may be the proper initial sample size; 2) The ASMO method is much more efficient than the widely used Shuffled Complex Evolution global optimization method.

Shuffled Complex Evolution (SCE) algorithm was first used to estimate kinetic parameters of the hypothetical three component charring material decomposing in three parallel steps and consequently applied to real material (beech wood).

Performance improvements varied but were as high as 38% for a real-coded genetic algorithm (RGA), 21% for shuffled complex evolution (SCE), 16% for simulated annealing (SA), 8% for particle swarm optimization (PSO), and 7% for dynamically dimensioned search (DDS).

These two models were calibrated with a shuffled complex evolution approach using PRISM and proxy seasonal temperature and precipitation to reconstruct streamflow for the upper reaches of the West Walker River basin at Coleville, CA. Overall, the modified Thornthwaite model performed best during calibration, with R2 values of 0.96 and 0.80 using PRISM and proxy inputs, respectively.

Based on this observation, in this article four optimization schemes, including the genetic algorithm (GA), simulated annealing (SA), shuffled complex evolution (SCE) and particle swam optimization (PSO), are employed to determine the threshold values while transforming the pinned field of the cover image into a binary feature image.