基于logistic模型的自适应差分进化算法

Adaptive differential evolution algorithm based on logistic model

提出一种基于logistic模型的自适应差分进化算法.该算法在运行过程中可自动调节缩放因子和交叉概率因子的大小,能在算法初期保持种群多样性,提高全局最优值的搜索能力,而在算法后期,随着局部最优值搜索能力的提高算法渐趋稳定.对几种典型Benchmarks函数进行了测试,实验结果表明所提出的算法收敛速度快、计算精度高.

An adaptive differential evolution algorithm based on logistic model is presented.The algorithm can automatically adjust scaling factor and crossover factor during the running time,so it can keep the individuals diversity and improve searching ability of global optimum in the population at the initial generations.However,the algorithm is gradually stabilized with searching ability of local optimum improved at a later time.Several classic Benchmarks functions are tested and the results show that the proposed algorithms have fast convergence and higher calculation accuracy.