Cooperative co-evolution (CC) is an efficient and practical evolutionary framework for solving large-scale optimization problems. The performance of CC is affected by the variable decomposition. An accurate variable decomposition can help to improve the performance of CC on solving an optimization problem. The variable grouping methods usually spend many computational resources obtaining an accurate variable decomposition. To reduce the computational cost on the decomposition, we propose an efficient recursive differential grouping (ERDG) method in this article. By exploiting the historical information on examining the interrelationship between the variables of an optimization problem, ERDG is able to avoid examining some interrelationship and spend much less computation than other recursive differential grouping methods. Our experimental results and analysis suggest that ERDG is a competitive method for decomposing large-scale continuous problems and improves the performance of CC for solving the large-scale optimization problems.

中文翻译：

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大规模连续问题的有效递归微分分组

合作协同进化（CC）是解决大规模优化问题的高效实用的进化框架。CC的性能受变量分解的影响。准确的变量分解有助于解决优化问题时提高CC的性能。变量分组方法通常花费大量计算资源来获得准确的变量分解。为了减少分解的计算成本，我们在本文中提出了一种有效的递归差分分组（ERDG）方法。通过利用有关检查优化问题变量之间相互关系的历史信息，ERDG可以避免检查某些相互关系，并且比其他递归微分分组方法花费更少的计算。