论文标题
通过合作协调指针网络加速大规模旅行推销员问题的遗传算法，并通过增强学习
Accelerating the Genetic Algorithm for Large-scale Traveling Salesman Problems by Cooperative Coevolutionary Pointer Network with Reinforcement Learning
论文作者
论文摘要
在本文中，我们提出了一个两阶段优化策略，用于解决名为CCPNRL-GA的大规模旅行推销员问题（LSTSP）。首先，我们假设一个良好的人作为精英的参与可以加速优化的融合。基于这一假设，在第一阶段，我们将城市聚集并分解为多个子组件，并通过可重复使用的指针网络（PTRNET）优化每个子组件。在亚组件优化之后，我们将所有子巡回仪组合在一起以形成有效的解决方案，该解决方案与GA的第二阶段相结合。我们验证了我们对10个LSTSP的提案的绩效，并将其与传统EAS进行比较。实验结果表明，精英个人的参与可以极大地加速LSTSP的优化，我们的建议在与LSTSP的打交道方面有广泛的前景。
In this paper, we propose a two-stage optimization strategy for solving the Large-scale Traveling Salesman Problems (LSTSPs) named CCPNRL-GA. First, we hypothesize that the participation of a well-performed individual as an elite can accelerate the convergence of optimization. Based on this hypothesis, in the first stage, we cluster the cities and decompose the LSTSPs into multiple subcomponents, and each subcomponent is optimized with a reusable Pointer Network (PtrNet). After subcomponents optimization, we combine all sub-tours to form a valid solution, this solution joins the second stage of optimization with GA. We validate the performance of our proposal on 10 LSTSPs and compare it with traditional EAs. Experimental results show that the participation of an elite individual can greatly accelerate the optimization of LSTSPs, and our proposal has broad prospects for dealing with LSTSPs.
