Novel differential evolution algorithm for function optimization

This paper proposed a novel differential evolution algorithm to overcome the premature convergence and slow convergent speed during the late evolution in differential evolution algorithm.The new algorithm was based on single population without intermediate population,in which mutation operation,crossover operation and selection operation were used on the current population.In addition,the parameters of mutation and crossover in the new DE were time-varying.The probability of mutation decreased with the evolution,while the probability of crossover was increasing.Results of several typical benchmark functions show the algorithm can avoid premature convergence and improve the performance of differential evolution algorithm in optimization.