[论文解读] A Hybrid Genetic Algorithm with Type-Aware Chromosomes for Traveling Salesman Problems with Drone

Sasan Mahmoudinazlou, Changhyun Kwon|arXiv (Cornell University)|Mar 1, 2023

Vehicle Routing Optimization Methods被引用 8

一句话总结

本文提出 HGA-TAC，一种带类型感知染色体的混合遗传算法，用于解决 TSPD 与 FSTSP，结合 GA、动态规划和局部搜索以提升解的质量与速度，在多个实例上创出新最佳。

ABSTRACT

There are emerging transportation problems known as the Traveling Salesman Problem with Drone (TSPD) and the Flying Sidekick Traveling Salesman Problem (FSTSP) that involve using a drone in conjunction with a truck for package delivery. This study presents a hybrid genetic algorithm for solving TSPD and FSTSP by incorporating local search and dynamic programming. Similar algorithms exist in the literature. Our algorithm, however, considers more sophisticated chromosomes and less computationally complex dynamic programming to enable broader exploration by the genetic algorithm and efficient exploitation through dynamic programming and local search. The key contribution of this paper is the discovery of how decision-making processes for solving TSPD and FSTSP should be divided among the layers of genetic algorithm, dynamic programming, and local search. In particular, our genetic algorithm generates the truck and the drone sequences separately and encodes them in a type-aware chromosome, wherein each customer is assigned to either the truck or the drone. We apply local search to each chromosome, which is decoded by dynamic programming for fitness evaluation. Our new algorithm is shown to outperform existing algorithms on most benchmark instances in both quality and time. Our algorithms found the new best solutions for 538 TSPD instances out of 920 and 74 FSTSP instances out of 132.

研究动机与目标

• 通过Truck-Drone协作（TSPD/FSTSP）来提升送达路径优化的动力
• 开发集成 GA、DP 与局部搜索的混合优化框架
• 在带类型感知染色体中对卡车与无人机决策进行区分编码，以增强探索与开发利用
• 在基准实例上展示相较现有方法的性能提升

提出的方法

• 提出三层架构：探索用 GA，最优 rendezvous 决策用 DP，精细化通过局部搜索
• 引入带类型感知的染色体（TAC）编码：卡车节点为正数，无人机节点为负数，单染色体中表示两者序列
• 开发 Join 动态规划算法，complexity 为 O(n^2)，用于确定最优发射/着陆点
• 设计针对 TAC 编码的带类型感知交叉和高效局部搜索策略
• 根据无人机射程将子种群分为两到三组，并实现逃离策略以避免局部最优

实验结果

研究问题

• RQ1 应如何在 GA、DP 与局部搜索之间分配决策，以高效解决 TSPD 与 FSTSP
• RQ2 带类型感知染色体编码是否能提升卡车-无人机路径问题的探索与解质量
• RQ3 Join DP 对评估 GA 产生解的计算影响如何
• RQ4 提出的基于 TAC 的方法是否在基准实例上产生新最佳解？

主要发现

• 该方法在 920 个 TSPD 实例中获得了 538 个的新最佳解
• 该方法在 132 个 FSTSP 实例中获得了 93 个的新最佳解
• HGA-TAC 在大多数基准实例上在解质量和运行时间两方面优于现有算法
• Join DP 算法对给定染色体的时间复杂度为 O(n^2)，实现高效评估
• 当无人机射程受限时使用两到三组子种群以提升多样性与性能