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In the following we use a quasi Newton method provided by the R-function optim for numerical optimization and for numerical integration we used the R-function integrate (R Development Core Team, 2012).
The potential for numerical optimization procedures to make optimal sequencing decisions in hybrid dynamic systems is explored.
There are many evolutionary algorithms for numerical optimization problems.
Nelder Mead simplex algorithm was used for numerical optimization.
In this study, a novel population-based across neighborhood search (ANS) is proposed for numerical optimization.
We developed a framework of procedures for numerical optimization in spatially explicit dynamic ecosystem simulation models.
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Therefore, we apply a "Genetic Algorithm for Numerical Optimizations of Constrained Problems" (GENOCOP) to solve this multi-objective problem using a goal attainment technique.
For the analysis, we extend a widely used SEIRS model to incorporate age-dependent screening rate profile and apply a gradient-based line search algorithm for ease of numerical optimization.
These results demonstrate the value of numerical optimization for selecting undetermined coefficients in the construction of high-order Runge Kutta methods with a balance between competing objectives.
A few numerical studies are presented to validate the theoretical framework for which the well-known method of moving asymptotes (MMA) is adopted for the numerical optimization of the problem.
In this study, we consider the scenario that differential evolution (DE) is applied for global numerical optimization and the index-based neighborhood information of population is used for enhancing the performance of DE.
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