The cooperative coevolution (CC) framework achieves a promising performance in solving large scale global optimization problems. The framework encounters difficulties on nonseparable problems, where variables interact with each other. Using the static grouping methods, variables will be theoretically grouped into one big subcomponent, whereas the random grouping strategy endures low efficiency. In this paper, a dynamic CC framework is proposed to tackle the challenge. The proposed framework works in a computationally efficient manner, in which the computational resources are allocated to a series of elitist subcomponents consisting of superior variables. First, a novel estimation method is proposed to evaluate the contribution of variables using the historical information of the best overall fitness. Based on the contribution and the interaction information, a dynamic grouping strategy is conducted to construct the dynamic subcomponent that evolves in the next evolutionary period. The constructed subcomponents are different from each other, and therefore the required parameters to control the optimization of each subcomponent vary a lot in each evolutionary period. A stage-by-stage parameter adaptation strategy is proposed to adapt the optimizer to the dynamic optimization environment. Experimental results indicate that the proposed framework achieves competitive results compared with the state-of-the-art CC frameworks.

中文翻译：

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大规模优化的动态协同协同进化

合作协同进化（CC）框架在解决大规模全局优化问题方面取得了有前途的性能。该框架在不可分离的问题上遇到了困难，变量之间会相互影响。使用静态分组方法，变量理论上会被分组为一个大的子组件，而随机分组策略则效率低下。在本文中，提出了一个动态 CC 框架来应对这一挑战。所提出的框架以计算效率高的方式工作，其中计算资源被分配给一系列由高级变量组成的精英子组件。首先，提出了一种新的估计方法，使用最佳整体适应度的历史信息来评估变量的贡献。基于贡献和交互信息，进行动态分组策略以构建在下一个进化周期进化的动态子组件。构建的子组件彼此不同，因此控制每个子组件优化所需的参数在每个进化时期都有很大差异。提出了一种逐阶段参数自适应策略，使优化器适应动态优化环境。实验结果表明，与最先进的 CC 框架相比，所提出的框架取得了有竞争力的结果。因此，控制每个子组件优化所需的参数在每个进化时期都有很大差异。提出了一种逐阶段参数自适应策略，使优化器适应动态优化环境。实验结果表明，与最先进的 CC 框架相比，所提出的框架取得了有竞争力的结果。因此，控制每个子组件优化所需的参数在每个进化时期都有很大差异。提出了一种逐阶段参数自适应策略，使优化器适应动态优化环境。实验结果表明，与最先进的 CC 框架相比，所提出的框架取得了有竞争力的结果。