Cooperative co-evolution (CC) is an effective strategy to solve large-scale continuous optimization problem. However

its grouping method may mislead the search direction when solving the large-scale overlapping problem (decision variables are non-separable and interact with each other). In order to overcome this issue

we propose a differential evolution cooperative coevolution with correlation learning between variables (DECC-CLV) to improve the performance of CC. DECC-CLV firstly detects the positive and negative correlations of variables based on the projected value of decision variables on the principal component of the population

and then groups variables into different groups. During the evolutionary process

DECC-CLV employs the expectation maximization algorithm for probabilistic principal component analysis on the population to deduce the complexity. Comparing with the state-of-the-art CCs on the large-scale overlapping benchmark functions on CEC2013

the experimental results verified the effectiveness and applicability of our proposed algorithm.