多种群并行的自适应差分进化算法

An Adaptive Differential Evolution Algorithm Based on a Multi-population Parallel

为了提高搜索速度,同时克服传统算法过早陷入局部最优值的不足,提出了一种改进自适应差分进化算法.改进算法在充分分析经典和改进变异操作算子的属性以及种群统计信息的基础上,按照个体适应度的差异,将个体分成不同的子种群并相应地引入与之匹配的变异算子,转换成一个多种群并行的优化问题,保证在加快算法收敛速度的同时有效跳出局部极值点,从而实现全局优化.同时对参数值实行自适应调整,使算法达到全局搜索能力与局部搜索能力的平衡.针对8个标准测试函数的仿真实验结果表明,所提出的算法与其他算法相比具有较好的效果.

A new adaptive differential evolution algorithm was put forward to improve search speed and avoid local optimal value.Sufficiently analyzing the characteristics of classic/adaptive mutation operators and the solution state,individuals were divided into three subgroups according to individual fitness values,thereby optimizing based on multiple populations,and different mutation operators were placed in different subpopulations.In addition,self-adaptive adjustment was introduced to adjust control parameters.Performance of the new approach was superior to other algorithms when tested on eight standard test functions.