基于自适应维度选择的记忆进化算法

Memory Evolutionary Algorithm Based on Adaptive Dimension Selection

提出一种基于自适应选择维度的记忆进化算法。该算法设置一个三维数组保存有用的进化信息,用于引导后续的进化过程,增强局部搜索能力,在变异过程中结合记忆信息自适应地选择维度进行变异,加强变异的有效性,当代种群中的最优个体通过自学习提高算法求解精度。标准函数仿真结果表明,该算法适合求解高维优化问题,局部收敛速度快,全局收敛能力强,算法稳定性高。

This paper proposes a Memory Evolutionary Algorithm Based on Adaptive Selecting Dimension（MEABASD）.It sets a three-dimensional array to save useful evolutionary information in order to guide the evolution of the follow-up,which can enhance the local search ability.In mutation process,combining with memory information,it adaptively selects the dimension to mutation to strengthen the effectiveness of mutation.The best contemporary populations does self-learning operator to improve the precision of the algorithm.Simulation results on standard test functions show that the algorithm is suitable for the high-dimension optimization problem,and it has the characteristics of rapid convergence,powerful global search capability and high stability