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This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically.
We need to be globally optimizing, rather than worrying about each little piece.
Due to this property these types of algorithms are potentially capable of globally optimizing any class of objective functions [2].
After that, we use the proposed GA to train the ANN by globally optimizing the weights of the network.
By using dynamic programming, the interpolatory discrete developable surface is obtained by globally optimizing an objective that minimizes tangent plane variations over a boundary triangulation.
After globally optimizing one particular class of such designs, we find thermal transfer efficiencies of 78% at temperatures over 1,000°C, with overall system energy conversion efficiencies of 37%, exceeding the Shockley-Quiesser efficiency limit of 31% for photovoltaic conversion under unconcentrated sunlight.
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The precalculated data is then used in a MIP to ultimately choose the optimal combination, and thus globally optimize profits.
Finally, we give an introduction to the techniques of optimal control theory that allow to generalize and globally optimize the dynamics of the system by using a variational approach.
Within this general context, we present a novel approach to globally optimize metabolic networks.
Apparently, a centralized scheduler with comprehensive knowledge of the network status can globally optimize the resource utility.
Linear programming is used, together with classical design techniques, to globally optimize a variety of metries over this traffic ensemble.
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