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Unfortunately, since it is usually the case that several sub-optimal solutions are possible, the use of global optimization methods is necessary to somehow guarantee that the best possible solution is located.
The solution of these problems is very challenging, and typically requires the use of global optimization techniques, even for the derivation of approximate solutions.
In the sequel, we show the use of global optimization in the form of Particle Swarm Optimization (PSO) to carry out calibration of the connections of the interrelationships between the nodes of the map.
The presence of nonconvex bilinear terms necessitates the use of global optimization techniques for which we employ a new global MIQCQP solver, GAMS/GloMIQO and verify the solutions with BARON.
The tuning of such complex simulation models 'by hand' to match patient data is not feasible, and thus we present an automated approach based on the use of global optimization algorithms and high-performance computing.
To avoid getting trapped in suboptimal solutions, the use of global optimization methods is suggested.
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We used a combination of global optimization (adaptive simulated annealing) and local search (Nelder-Mead simplex method) to find the best-fit parameter set shown in Table 2.
The structural, optical and electronic properties of 13-atom Ag Au nanoalloys are determined by a combination of global optimization using semi-empirical potentials and density functional theory calculations.
Parallel implementations of global optimization methods have been used recently in systems biology (see [ 21] and references therein).
Decomposition of global optimization problem is introduced.
A wide variety of global optimization techniques have been developed to address nonlinear optimization problems [ 39- 42].
More suggestions(15)
use of many optimization
use of ordinal optimization
use of Lagrangian optimization
use of discrete optimization
use of convex optimization
use of global stock
use of nonlinear optimization
use of constrained optimization
use of numerical optimization
use of different optimization
use of analytical optimization
use of automated optimization
use of global balance
use of global memory
use of global sensitivity
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