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 sub -problems to reduce the problem complexity. However, such a strategy usually demands a large number of function evaluations to obtain an accurate variable grouping. To address this issue, a merged differential grouping (MDG) method is proposed in this article based on the subset-subset interaction and binary search. In the proposed method, each variable is first identified as either a separable variable or a nonseparable 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, n(ns) x log(2) k}), where n, n(ns)(<= n), and k(< n) indicate the numbers of decision variables, nonseparable variables, and subsets of nonseparable variables, respectively. The experimental results on benchmark problems show that MDG is very competitive with the other state-of-the-art methods in termsof efficiency and accuracy of problem decomposition.

Automated summary

This article introduces a merged differential grouping (MDG) method, which is a divide-and-conquer strategy to solve large-scale global optimization problems. By decomposing the problem into manageable subproblems and using binary search to group variables, the method improves the efficiency and accuracy of problem decomposition.