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Cooperation Coevolution Differential Evolution with Gradient Descent Strategy for Large Scale

机译：大规模协同合作演化差分进化与梯度下降策略

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摘要

In order to better solve the large scale optimization problem, we propose a cooperation coevolution differential evolutionary (CCDE) algorithm with a gradient descent strategy (GDS). The GDS based CCDE algorithm (CCDE/GDS) benefits the solution of large scale optimization problems in two critical aspects. Firstly, the optimization turned out to be far less time consuming due to that GDS is helpful for guiding the search direction on the globally best individual position. More importantly, the GDS is controlled by an elastic operator to be carried out only when the globally best individual has been trapped, making the algorithm fast respond to the large scale evolutionary environment. Secondly, GDS was reported in the literature to approximate the local best value on most object functions. Therefore, the GDS used in CCDE can promote the globally best individual position to more promising region when it is trapped into local optimum, so as to achieve high accuracy. We designed experiments on CEC2010 benchmark functions for evaluating our newly proposed algorithm, which shows that the proposed algorithm and modified framework can obtain very competitive results on the large scale optimization problem efficiently.
机译：为了更好地解决大规模优化问题，我们提出了一种带有梯度下降策略（GDS）的协同协同进化微分进化算法（CCDE）。基于GDS的CCDE算法（CCDE / GDS）在两个关键方面有益于大规模优化问题的解决。首先，由于GDS有助于在全球最佳个人位置上指导搜索方向，因此优化过程所耗费的时间要少得多。更重要的是，GDS由弹性算符控制，仅在全局最佳个体被困时才执行，从而使算法快速响应大规模进化环境。其次，文献中报道了GDS，以估计大多数对象函数上的局部最佳值。因此，CCDE中使用的GDS在陷入局部最优时可以将全球最佳个人位置提升到更有希望的区域，从而实现较高的准确性。我们在CEC2010基准函数上设计了实验，以评估我们提出的算法，结果表明，提出的算法和改进的框架可以有效地在大规模优化问题上获得非常有竞争力的结果。

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