  One of the challenges in optimization of high dimensional problems is finding
appropriate solutions in a way that are as close as possible to the global
optima. In this regard, one of the most common phenomena that occurs is the
curse of dimensionality in which a large scale feature space generates more
parameters that need to be estimated. Heuristic algorithms, such as
Gravitational Search Algorithm, are among the tools proposed for optimizing
large-scale problems, but in this case, they cannot solve the problem on their
own. This paper proposes a novel method for optimizing large scale problems by
improving the gravitational search algorithm's performance. In order to
increase the efficiency of the gravitational search algorithm in solving large
scale problems, the proposed method combines this algorithm with the
cooperative-coevolution methods. For the evaluation of the performance of the
proposed algorithm, we consider two approaches. In the first approach, the
proposed algorithm is compared with the gravitational search algorithm, and in
the second approach, it is compared with some of the most significant research
in this field. In the first approach, our method was able to improve the
performance of the original gravitational algorithm to solve large scale
problems, and in the second one, the results indicate more favorable
performance, in some benchmark functions, compared with other cooperative
methods.
