Modified Mutation in Asynchronous Differential Evolution

This article presents a variant of an asynchronous differential evolution (ADE) algorithm for solving global optimization problems. The proposed algorithm uses the mean of two randomly chosen variables as a third variable to perform a mutation operation. The modification in a mutation operation is done to exploit the chosen random variables and to accelerate the convergence. The proposed algorithm is tested on a set of benchmark functions and compared with differential evolution (DE) and ADE. Results and comparisons show that the proposed work outperforms other algorithms in terms of number of function evaluation, standard deviation and convergence rate. Results are validated through non-parametric statistical analysis.