A Global Information Based Adaptive Threshold for Grouping Large Scale Global Optimization Problems
This work addresses the efficiency of decomposition strategies in optimization algorithms for researchers in computational intelligence, though it appears incremental as it builds on existing differential grouping methods.
The paper tackled the problem of improving decomposition accuracy in cooperative coevolution for large-scale global optimization by proposing a global information based adaptive threshold algorithm (GIAT) and a new interaction indicator, which were verified on benchmark functions to enhance performance.
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.