TADE: Tight adaptive differential evolution

Abstract

Differential Evolution (DE) is a simple and effective evolutionary algorithm to solve optimization problems. The existing DE variants always maintain or increase the randomness of the differential vector when considering the trade-off of randomness and certainty among three components of the mutation operator. This paper considers the possibility to achieve a better trade-off and more accurate result by reducing the randomness of the differential vector, and designs a tight adaptive DE variant called TADE. In TADE, the population is divided into a major subpopulation adopting the general “current-to-pbest” strategy and a minor subpopulation utilizing our proposed strategy of sharing the same base vector but reducing the randomness in differential vector. Based on success-history parameter adaptation, TADE designs a simple information exchange scheme to avoid the homogeneity of parameters. The extensive experiments on CEC2014 suite show that TADE achieves better or equivalent performance on at least 76.7% functions comparing with five state-of-the-art DE variants. Additional experiments are conducted to verify the rationality of this tight design.