  By taking the idea of divide-and-conquer, cooperative coevolution (CC)
provides a powerful architecture for large scale global optimization (LSGO)
problems, but its efficiency relies highly on the decomposition strategy. It
has been shown that differential grouping (DG) performs well on decomposing
LSGO problems by effectively detecting the interaction among decision
variables. However, its decomposition accuracy depends highly on the threshold.
To improve the decomposition accuracy of DG, a global information based
adaptive threshold setting algorithm (GIAT) is proposed in this paper. On the
one hand, by reducing the sensitivity of the indicator in DG to the roundoff
error and the magnitude of contribution weight of subcomponent, we proposed a
new indicator for two variables which is much more sensitive to their
interaction. On the other hand, instead of setting the threshold only based on
one pair of variables, the threshold is generated from the interaction
information for all pair of variables. By conducting the experiments on two
sets of LSGO benchmark functions, the correctness and robustness of this new
indicator and GIAT were verified.
