Differential Evolution with Tournament-based Mutation Operators

Differential Evolution(DE) has emerged as a powerful and efficient evolutionary algorithm for solving global optimization problems. It adopts the stochastic searching method to make selection of the parents in the mutation operator, which benefits the search of global optimization value. However, the selection method reveals the convergence in low speed. So for the sake of better convergence performance, in this paper, we propose the Tournament-based mutation operators to accelerate the differential evolution. The proposed algorithm employs the tournament selection for mutation. The process of tournamentbased mutation operators is that the base and differential vectors are replaced by the tournament best vector but other vectors are randomly selected. It is helpful to improve the convergence besides maintain the diversity of DE algorithms. We also integrate the algorithm into jDE to verify the effect on it. Experimental results indicate that our proposed tournamentbased mutation operators are highly competitive to the original DE algorithm and are able to enhance the performance of jDE.