System identification using differential evolution with mean-best mutation
Ming‐Feng Yeh, Hung‐Ching Lu, Min-Shyang Leu, Yi-Fanlee · 2015
This paper attempts to propose a new mutation strategy, termed the mean-best mutation strategy, for differential evolution (DE) algorithm to enhance global search ability and to avoid premature convergence. In the proposed mutation strategy (denoted by DE/mBest/1), the base vector is the mean of the p top-ranked individuals, and denoted by mBest. That is, the randomly selected base vector of DE/rand/1 or the best vector of DE/best/1 is replaced by mBest in the proposed scheme. DE/mBest/1 is applied to identify an unknown system whose structure is assumed to be known in advance. The search performance of DE/mBest/1 is compared with two standard DEs(DE/rand/l and DE/best/1), two of our previous works and 2-Opt based DE in terms of parameter accuracy, convergence speed and reliability. Simulation results demonstrate the effectiveness of the proposed DE algorithm.