Abstract | ||
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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 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 nonseparable 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
<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\max \{n,n_{ns}\times \log _{2} k\})$ </tex-math></inline-formula>
, where
<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>
,
<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n_{ns}(\leq n)$ </tex-math></inline-formula>
, and
<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k( < n)$ </tex-math></inline-formula>
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 terms of efficiency and accuracy of problem decomposition. |
Year | DOI | Venue |
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2022 | 10.1109/TEVC.2022.3144684 | IEEE Transactions on Evolutionary Computation |
Keywords | DocType | Volume |
Cooperative co-evolution,differential grouping (DG),large-scale global optimization (LSGO),problem decomposition | Journal | 26 |
Issue | ISSN | Citations |
6 | 1089-778X | 0 |
PageRank | References | Authors |
0.34 | 34 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Xiaoliang Ma | 1 | 182 | 18.51 |
Zhitao Huang | 2 | 0 | 0.34 |
Xiaodong Li | 3 | 1560 | 84.64 |
Wang Lei | 4 | 429 | 50.67 |
Yutao Qi | 5 | 0 | 2.03 |
Zexuan Zhu | 6 | 989 | 57.41 |