Merged Differential Grouping for Large-scale Global Optimization

The divide-and-conquer strategy has been widely used in cooperative co-evolutionary algorithms to deal with large-scale global optimization problems, where a target problem is decomposed into a set of lower-dimensional and tractable subproblems to reduce the problem complexity. However, such strategy usually demands a large number of function evaluations to obtain an accurate variable grouping. To address this issue, a merged differential grouping method is proposed in this paper based on subset-subset interaction and binary search. In the proposed method, each variable is firstly identified as either a separable variable or a non-separable variable. Afterward, all separable variables are put into the same subset, and the non-separable variables are divided into multiple subsets using a binary-tree-based iterative merging method. With the proposed algorithm, the computational complexity of interaction detection is reduced to O(max{n, nns ×log2 k}), where n, nns(≤n), and k(< n) indicate the numbers of decision variables, non-separable variables, and subsets of non-separable variables, respectively. The experimental results on benchmark problems show that merged differential grouping is very competitive with the other state-of-the-art methods in terms of efficiency and accuracy of problem decomposition.