Some improvement to the mutation donor of differential evolution

Purpose – The purpose of this paper is to improve the existing differential evolution (DE) mutation operator so as to accelerate its convergence.Design/methodology/approach – A new general donor form for mutation operation in DE is presented, which defines a donor as a convex combination of the triplet of individuals selected for a mutation. Three new donor schemes from that form are deduced.Findings – The three donor schemes were empirically compared with the original DE version and three existing variants of DE by using a suite of nine well‐known test functions, and were also demonstrated by a practical application case – training a neural network to approximate aerodynamic data. The obtained numerical simulation results suggested that these modifications to the mutation operator could improve the DE's convergence performance in both the convergence rate and the convergence reliability.Research limitations/implications – Further research is still needed for adequately explaining why it was possible to s...

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