Metaheuristic Approach for Solving the Traveling Salesman Problem with Drone

Abstract

A well-known combinatorial optimization issue called The Traveling Salesman issue (TSP) has applications in many fields, including drone-based delivery and monitoring systems. In this study, we examine the effectiveness of three distinct implementations of the Simulated Annealing (SA), Sparrow Search Algorithm (SSA), and Tabu Search (TS) metaheuristic algorithms to solve the TSP for drone applications. The goal is to evaluate each algorithm's performance and assess whether it is appropriate for use in practical drone deployment scenarios while considering limitations like the maximum drone distance and the number of trips. Our approach focuses on how SA, SSA, and TS are implemented individually and outlines the experimental setup used to compare performance. To guarantee a neutral comparison, the study is carried out in simulation using Python in the same environment for all of the methods. The goal is to reduce the vehicle and drone's combined total distance travelled.