Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization
Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization
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DOI：
10.1109/tevc.2013.2281543
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发表时间：
2014-06-01
影响因子：
14.3
通讯作者：
Yao, Xin
中科院分区：
文献类型：
--
作者：
Omidvar, Mohammad Nabi;Li, Xiaodong;Yao, Xin
协同进化被引入到进化算法中，其目的是通过分而治之的范式来解决日益复杂的优化问题。理论上，协适应子分量的思想对于解决大规模优化问题是可取的。然而，在实践中，如果没有关于问题的先验知识，就不清楚问题应该如何分解。在本文中，我们提出了一种自动分解策略，称为差分分组，可以揭示决策变量的底层交互结构，并形成子组件，使它们之间的相互依赖性保持在最低限度。我们展示了数学上如何这样的分解策略可以从部分可分性的定义。实证研究表明，这种近优分解可以大大提高大规模全局优化问题的解的质量。最后，我们展示了这样的自动分解如何允许更好地近似各种子组件的贡献，从而更有效地将计算预算分配给各种子组件。
Cooperative co-evolution has been introduced into evolutionary algorithms with the aim of solving increasingly complex optimization problems through a divide-and-conquer paradigm. In theory, the idea of co-adapted subcomponents is desirable for solving large-scale optimization problems. However, in practice, without prior knowledge about the problem, it is not clear how the problem should be decomposed. In this paper, we propose an automatic decomposition strategy called differential grouping that can uncover the underlying interaction structure of the decision variables and form subcomponents such that the interdependence between them is kept to a minimum. We show mathematically how such a decomposition strategy can be derived from a definition of partial separability. The empirical studies show that such near-optimal decomposition can greatly improve the solution quality on large-scale global optimization problems. Finally, we show how such an automated decomposition allows for a better approximation of the contribution of various subcomponents, leading to a more efficient assignment of the computational budget to various subcomponents.