Differential Evolution with Mutation Operators Based on Truncation
Pan Huo, Chen Qiu, Wenyin Gong · 2013
Differential evolution(DE) is one of the most efficient algorithms in evolutionary algorithm family. Mutation operator is the core operator in DE. Generally, the parents in mutation operator are randomly selected from the parent population. In this paper, we proposed the truncation selection to accelerate DE. In the truncation selection, individuals are sorted according to their fitness. We select the individuals which have better fitness as individuals in mutation operator. In order to verify the efficiency of the algorithm, we compare the result of trunDE with the original DE. We also apply the method to jDE and make some comparisons. Experimental results indicate that our proposed algorithm can enhance the performance of the original DE algorithm and the jDE.