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Knowledge of the properties of the value function with respect to the parameters can be used for developing optimization tools to find optimal system designs.
Nature is the rich principal source for developing optimization algorithms.
Further studies are suggested in developing optimization strategies and control systems.
Developing optimization software that is capable of solving large and complex real-life problems is a huge effort.
In this approach, we mention primarily some of the main risk examples in terms of cloud architecture layers and the developing optimization model of cloud risk appetite.
A case is made here that in developing optimization algorithms for specific practical applications it is better to treat the available evolutionary algorithms as part of a "toolbox" of metaheuristic components that can be configured in various hybridized combinations.
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We used an MIP solver to solve the developed optimization problem and integrated the optimal results to serve as an input for our simulations to evaluate the benefits of surface gateway optimization framework.
In order to optimize the model performance, a recently developed optimization technique based on rate rules (Cai and Pitsch, 2014) is extended in this study.
In recent years, more and more newly developed optimization strategies have been used to determine the optimal level of each experimental parameter.
The reservoir developers and managers have expressed increasing interest in newly developed optimization processes for intelligent well completions.
Particle swarm optimization (PSO) is one of the recently developed optimization algorithm, and it has been used as the global optimizer.
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