Abstrct
合作型协同进化算法
Cooperative co-evolution (CC) is an effective framework that can be used to solve large-scale optimization problems. It typically divides a problem into components and uses one optimizer to solve the components in a round-robin fashion.
合作型协同进化（CC）是一种有效的框架，可用于解决大规模优化问题。 它通常将问题划分为组件，并使用一个优化器以循环方式解决组件。
问题
The relative contribution of each component to the overall fitness value may vary. Furthermore, using one optimizer may not be sufficient when solving a wide range of components with different characteristics.
每个组分对总体适合度值的相对贡献可以变化。 此外，在解决具有不同特征的各种组件时，使用一个优化器可能还不够。
解决方案
We propose a novel CC framework which can select an appropriate optimizer to solve a component based on its contribution to the fitness improvement. In each evolutionary cycle, the candidate optimizer and component that make the greatest contribution to the fitness improvement are selected for evolving.
我们提出了一种新颖的CC框架，它可以根据其对适应度提升的贡献来选择合适的优化器来解决子问题。 在每个进化周期中，选择对适应度提升做出最大贡献的候选优化器和子问题用于进化。
实验结果
We evaluated the efficacy of the proposed CC with Optimizer Selection (CCOS) algorithm using large-scale benchmark problems. The numerical experiments showed that CCOS outperformed the CC model without optimizer selection ability. When compared against several other state-of-the-art algorithms, CCOS generated competitive solution quality.
我们使用大规模基准问题评估了已提出的CC方法与优化器选择（CCOS）算法的效果。 数值实验表明，CCOS优于没有优化选择能力的CC模型。与其他几种最先进的算法相比，CCOS具有竞争力。
关键字
Large-scale optimization, cooperarive co-evolution, algorithm selection, algorithm hybridization, resources allocation
大规模优化，合作型协同进化，算法选择，算法混合，资源分配
Conclusion
方法
In this paper, we have investigated how the use of alternative optimizers at different evolutionary stages impacted on the solution quality generated by the CC when used to solve LSGO problems. Instead of employing only one optimizer to solve all the components, we proposed an online optimizer selection framework to select the best optimizer from a portfolio for each component. At each evolutionary cycle, the component and optimizer pair that previously contributed the most to the overall fitness improvement was selected for evolving.
在本文中，我们研究了在用于解决LSGO问题时，在不同演化阶段使用替代优化器如何影响CC产生的解的质量。 我们提出了一个在线优化器选择框架，针对每个子问题，从集合中选择最佳优化器，而不是仅使用一个优化器来解决所有子问题。 在每个进化周期中，选择先前对整体适应性改善贡献最大的子问题和优化器对进行演化。
实验
We experimentally demonstrated that the proposed CCOS algorithm was successful in selecting the best optimizer when solving the CEC’2010 benchmark problems. Significantly, CCOS could potentially generate statistically better solution quality than the default CC algorithm with no optimizer selection ability. When compared against several other state-of-the-art algorithms, CCOS also achieved competitive results.
我们通过实验证明，在解决CEC'2010基准问题时，所提出的CCOS算法成功地选择了最佳优化器。 值得注意的是，CCOS可能比没有优化器选择能力的默认CC算法产生统计上更好的解决方案质量。 与其他几种最先进的算法相比，CCOS也取得了有竞争力的成果。
