A Global Information Based Adaptive Threshold for Grouping Large Scale Global Optimization Problems

TL;DR

This paper introduces a global information based adaptive threshold (GIAT) to enhance differential grouping in cooperative coevolution, improving the decomposition accuracy for large scale global optimization problems.

Contribution

It proposes a new interaction indicator and a threshold setting method based on global interaction information, improving decomposition accuracy in cooperative coevolution.

Findings

Enhanced decomposition accuracy on benchmark functions

Improved robustness and correctness of variable grouping

Better performance compared to traditional threshold methods

Abstract

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.