Multilevel cooperative coevolution for large scale optimization
Multilevel cooperative coevolution for large scale optimization
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DOI：
10.1109/cec.2008.4631014
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发表时间：
2008-06
期刊：
影响因子：
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通讯作者：
Zhenyu Yang;K. Tang;X. Yao
中科院分区：
文献类型：
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作者：
Zhenyu Yang;K. Tang;X. Yao
本文提出了一种求解大规模优化问题的多级协同进化框架。我们的动机是改善我们以前的工作分组为基础的合作协同进化（EACC-G），其中有一个很难确定的参数，组的大小，在解决问题的分解。问题分解器以组大小为参数，采用随机分组策略将目标向量分解为低维子分量。在MLCC中，一组问题分解器是基于不同的组大小的随机分组策略构造的。该算法将进化过程分为若干个周期，在每个周期开始时，MLCC采用自适应机制根据分解器的历史性能选择分解器。由于不同的群体规模会捕获原始目标变量之间不同的交互水平，因此MLCC能够在不同水平之间进行自适应。建议MLCC的有效性进行评估的一套基准功能提供的CECpsila 2008特别会议。
In this paper, we propose a multilevel cooperative coevolution (MLCC) framework for large scale optimization problems. The motivation is to improve our previous work on grouping based cooperative coevolution (EACC-G), which has a hard-to-determine parameter, group size, in tackling problem decomposition. The problem decomposer takes group size as parameter to divide the objective vector into low dimensional subcomponents with a random grouping strategy. In the MLCC, a set of problem decomposers is constructed based on the random grouping strategy with different group sizes. The evolution process is divided into a number of cycles, and at the start of each cycle MLCC uses a self-adapted mechanism to select a decomposer according to its historical performance. Since different group sizes capture different interaction levels between the original objective variables, MLCC is able to self-adapt among different levels. The efficacy of the proposed MLCC is evaluated on the set of benchmark functions provided by CECpsila2008 special session.