Distance-base Differential Evolution Algorithm
Zexi Deng · Journal of Bijie University · 2013
For premature convergence and instability of differential evolution in solving function optimization problem,a distance-base differential evolution algorithm(DDE) is proposed.In order to improve the population' s diversity and the ability of breaking away from the local optimum,the difference between particles was considered,and the Euclidean distance was used to calculate the difference between a particle and the known best global particle,then the particle tuned adaptively the value of the crossover probability factor according to the difference,at the same time the Cauchy mutation operator is adapted to mutate partial individuals. The experimental results show that the new algorithm is better than the original differential evolution algorithm in convergence rate and accuracy.