Self-adapting control parameters in differential evolution
Huang Houkuan · Jisuanji gongcheng yu sheji · 2012
Parameters setting is an important problem of evolution algorithms,including differential evolution algorithm.It has an effect on the performance of evolution algorithms.According to the problem of parameters of differential evolution,a method is presented,which uses self-adaptive as a scientific evidence to adjust parameters and set F and CR combined with modulated probability.An algorithm is presented,which depends on the fitness of individual and modulated probability set the parameters F and CR automatically.This method can get the optimal control parameters for different optimization problem without user interaction.Moreover,two trial vectors are created by recombination for increased colony diversity and avoided premature convergence.These vectors compete with the parent individual to be the next generation.Experimental results indicate that the proposed algorithm is efficient and feasible.It is superior to other related methods such as DE,jDE,FADE,MPDE,DDE both on the quality of solution and on the convergence rate,especially for high dimension functions.