Self-adaptive differential evolution algorithm based on dimensionality group cross
Tao Zhou · Jisuanji gongcheng yu sheji · 2011
In order to overcome the shortcoming that high-dimensional multimodal optimization problems has a low resolving speed,low convergence accuracy and traps into local optima easily,a self-adaptive differential evolution algorithm based on dimensionality group cross is proposed to resolve this problem.Firstly,using the relationship between each two dimensions in population to split the solution vector into the smaller vectors.Secondly,crossover operation is proceed in each population according to the dimensions division,then,cooperation differential evolution is realized.Finally,4 benchmark functions are used to test this algorithm.Experimental result illustrate that the proposed algorithm has some advantages in convergence velocity,solution precision,and stabilization.