  It has been shown that cooperative coevolution (CC) can effectively deal with
large scale optimization problems (LSOPs) through a divide-and-conquer
strategy. However, its performance is severely restricted by the current
context-vector-based sub-solution evaluation method since this method needs to
access the original high dimensional simulation model when evaluating each
sub-solution and thus requires many computation resources. To alleviate this
issue, this study proposes a novel surrogate model assisted cooperative
coevolution (SACC) framework. SACC constructs a surrogate model for each
sub-problem obtained via decomposition and employs it to evaluate corresponding
sub-solutions. The original simulation model is only adopted to reevaluate some
good sub-solutions selected by surrogate models, and these real evaluated
sub-solutions will be in turn employed to update surrogate models. By this
means, the computation cost could be greatly reduced without significantly
sacrificing evaluation quality. To show the efficiency of SACC, this study uses
radial basis function (RBF) and success-history based adaptive differential
evolution (SHADE) as surrogate model and optimizer, respectively. RBF and SHADE
have been proved to be effective on small and medium scale problems. This study
first scales them up to LSOPs of 1000 dimensions under the SACC framework,
where they are tailored to a certain extent for adapting to the characteristics
of LSOP and SACC. Empirical studies on IEEE CEC 2010 benchmark functions
demonstrate that SACC significantly enhances the evaluation efficiency on
sub-solutions, and even with much fewer computation resource, the resultant
RBF-SHADE-SACC algorithm is able to find much better solutions than traditional
CC algorithms.
