自适应局部增强微分进化改进算法

A Modified Differential Evolution Algorithm with Adaptive and Local Enhanced Operator

在分析微分进化算法基本原理基础上,为加快算法收敛速度,对其交叉概率和交叉因子进行自适应调整改进;为增强算法局部搜索能力,引入局部增强算子和扰动因子改进算法,即自适应局部增强微分进化算法。选取5个典型测试函数,将改进后算法与PSO算法、微分进化算法和局部增强微分进化算法仿真比较。仿真结果表明:自适应局部增强微分进化算法为收敛时间最短、迭代次数最少的优化算法,验证了算法改进的有效性。

The differential evolution algorithm is robust,easy to use,and requires few control parameters.However,as to the local optimizing ability,it is limited.Based on the principium analysis of the algorithm,the adaptive modification of the cross rate and the cross operator is proposed to improve the efficiency of the algorithm.To enhance the local optimizing,the local enhanced operator and the disturbed operator are proposed.Numerical study is carried out using five benchmark functions.Compared with the PSO algorithm,DE and MPDE,the ADMPDE is the most efficient algorithm of all,which verifies that the modification is effective.