The optimal reservoir operation in dry seasons is an important topic in water resources management due to conflict of interest. This paper tends to address this issue by providing possible reservoir operation for indi...
详细信息
The optimal reservoir operation in dry seasons is an important topic in water resources management due to conflict of interest. This paper tends to address this issue by providing possible reservoir operation for individual months, together with multiobjective optimization and other constraints. A multiobjectivesgenetic algorithm optimization model is presented to determine the optimum releases of the Bigge reservoir, Germany, by assuming two inflow scenarios for dry seasons. The first one represents the minimum recorded monthly inflow during the period between 1995 and 1996;whereas the second scenario was the minimum monthly inflow during a five consecutive years generated using Monte Carlo model. The objectives of this study are to maximize energy production, the benefits of recreation, as well as the benefits of the energy produced, and to minimize the total penalty due to deviation from the targets. Several trade-off Pareto optimal solutions were obtained. A compromise solution is presented from a set of Pareto optimal solutions to help the decision maker. Details of the model formulations and implementation are described. The results demonstrate the efficiency of the developed model to determine the optimum releases effectively in the two inflow scenarios of dry seasons achieving all constrains. (C) 2014 American Society of Civil Engineers.
Radiofrequency (RF) cavities hold immense importance in various accelerator applications, but their optimization poses significant challenges due to complex situations involved. In this study, a recently proposed mult...
详细信息
Radiofrequency (RF) cavities hold immense importance in various accelerator applications, but their optimization poses significant challenges due to complex situations involved. In this study, a recently proposed multiobjective optimization algorithm is utilized to optimize the 325 MHz double spoke cavity, which is characterized by 38 geometric parameters and is one of the most complex cavities commonly used in accelerators. The algorithm utilized combines neural network dynamically to speed up convergence of MOGAs, and it is called DNMOGA. Remarkably, when comparing to two manually optimized cavities (MOCs) respectively, DNMOGA consistently produces some cavities that outperform the MOC in all indicators concerned. This result announces the robust generalization capability exhibited by DNMOGA, and further shows the possibility of designing cavities employing the state-of-art optimization algorithms instead of manual optimization processes completely.
暂无评论