论文标题
修改的蜜蜂菌落优化算法，用于定期多孔介质中孔尺度传输的计算参数识别
Modified Bee Colony optimization algorithm for computational parameter identification for pore scale transport in periodic porous media
论文作者
论文摘要
本文讨论了一种基于蜜蜂群的特定智能行为的优化方法，称为修改的蜂群算法（MBC）。该算法以Shekel，Rozenbroke，Himmelblau和Rastrigin功能进行检查，然后将其应用于周期性多孔介质中的反应流问题的参数识别。仿真结果表明，MBC算法的性能和效率与其他参数识别方法和策略相当，同时，它能够更好地捕获考虑到所考虑的问题类别的局部最小值。所提出的识别方法适用于不同的几何形状（随机和周期性）以及一系列过程参数。在本文中，该方法的潜力在识别低卵石和高damkoler数量的Langmuir等温线的参数中，在具有圆形夹杂物的2D周期性多孔介质中反应性流动。利用了空间和隐式时间离散化的有限元近似。
This paper discusses an optimization method called Modified Bee Colony algorithm (MBC) based on a particular intelligent behavior of honeybee swarms. The algorithm was checked in a few benchmarks like Shekel, Rozenbroke, Himmelblau and Rastrigin functions, then was applied to parameter identification for reactive flow problems in periodic porous media. The simulation results show that the performance and efficiency of MBC algorithm are comparable to the other parameter identification methods and strategies, at the same time it is able to better capture local minima for the considered class of problems. The proposed identification approach is applicable for different geometries (random and periodic) and for a range of process parameters. In this paper the potential of the approach is demonstrated in identifying parameters of Langmuir isotherm for low Peclet and high Damkoler numbers reactive flow in a 2D periodic porous media with circular inclusions. Finite element approximation in space and implicit time discretization are exploited.
