This paper presents a novel approach to solving the Flying Sidekick Travelling Salesman Problem (FSTSP) using a state-of-the-art self-adaptive genetic algorithm. The Flying Sidekick Travelling Salesman Problem is a combinatorial optimisation problem that extends the Travelling Salesman Problem (TSP) by introducing the use of drones. In FSTSP, the objective is to minimise the total time to visit all locations while strategically deploying a drone to serve hard-to-reach customer locations. Also, to the best of my knowledge, this is the first time a self-adaptive genetic algorithm (GA) has been used to solve the FSTSP problem. Experimental results on smaller-sized problem instances demonstrate that this algorithm can find a higher quantity of optimal solutions and a lower percentage gap to the optimal solution compared to rival algorithms. Moreover, on larger-sized problem instances, this algorithm outperforms all rival algorithms on each problem size while maintaining a reasonably low computation time.

翻译：本文提出了一种新颖的求解飞行副驾旅行商问题（Flying Sidekick Travelling Salesman Problem, FSTSP）的先进自适应遗传算法。飞行副驾旅行商问题是一种组合优化问题，它通过引入无人机的使用扩展了经典旅行商问题（TSP）。在FSTSP中，目标是在战略性地部署无人机以服务难以到达的客户位置的同时，最小化访问所有地点的总时间。此外，据我所知，这是首次使用自适应遗传算法（GA）求解FSTSP问题。在较小规模问题实例上的实验结果表明，与竞争算法相比，该算法能发现更高质量的最优解，且与最优解的百分比差距更小。在较大规模问题实例上，该算法在保持合理较低计算时间的同时，在每种问题规模下均优于所有竞争算法。