基于网格化拉马克学习机制的差分进化算法

引入拉马克进化理念，提出一种基于网格化拉马克学习机制的差分进化算法。该算法在网格划分机制建立起的分布式搜索框架下，采用单元格最优解保护机制、学习步长机制、解空间同仁机制和定矢变异机制组成拉马克学习模式。仿真结果表明，所提算法可以充分发挥拉马克学习的局部搜索能力，又可有效避免早熟收敛，其求解精度明显优于其他比较算法。将所提算法应用于电力系统最优潮流计算问题，获得了良好的优化效果。

Differential evolution algorithm based on gridded Lamarckian learning

By introducing Lamarckian evolutionism, an improved differential evolution algorithm based on the gridded Lamarckian learning mechanism(DE-GLam) is proposed. Under a distributed search framework set by mesh generation mechanism, this algorithm integrates the cell optimum protection mechanism, learning step mechanism, solution space mechanism and directive variation mechanism to form the Lamarck learning mode. The simulation results show that the DE-GLam algorithm not only fully exerts the local search ability of Lamarckian learning mechanism, but also effectively avoids premature convergence, and the solving precision is superior to other comparison algorithms. The validity of the proposed method is illustrated by the optimal power flow calculation.