A New Differential Evolution Algorithm with Random Parameters

Differential evolution algorithm’s performance often depends heavily on the parameter settings. Based on analyzing the influence of the parameters setting in the experiment, the effects and the optimal selection of those major parameters on DE are analyzed, and some conclusions are derived. A new differential evolution algorithm which the scale constant ( F ) and crossover constant ( CR ) are generated as random numbers within a certain range in each iteration process is proposed. The experimental results shows that the new algorithm is simple, easy to realize and can get higher precision and better stability.