The particle swarm optimizer (PSO) is a population-based optimization technique that can be widely utilized to many applications. The cooperative particle swarm optimization (CPSO) applies cooperative behavior to improve the PSO on finding the global optimum in a high-dimensional space. This is achieved by employing multiple swarms to partition the search space. However, independent changes made by different swarms on correlated variables will deteriorate the performance of the algorithm. This paper proposes a separability detection approach based on covariance matrix adaptation to find non-separable variables so that they can previously be placed into the same swarm to address the difficulty that the original CPSO encounters.