Examination of the Red Panda Algorithm for Solving the Traveling Salesman Problem: An Example of Determining Routes to Disaster and Emergency Assembly Areas

This study analyzes the Red Panda Optimization Algorithm (RPOA), inspired by nature, in solving the Traveling Salesman Problem (TSP). Specifically, it aims to determine the shortest and most efficient routes to assembly areas during disasters. TSP is a fundamental problem with a wide range of applications in logistics, route planning, distribution, and facility location. It is a problem classified as NP-hard (difficult to solve) with a large solution space, and has been studied by researchers for many years. Due to the rapidly growing solution space, solving it with classical methods is impractical. For this problem, where classical methods are insufficient, RPOA, which mimics the feeding and resting behaviors of red pandas, offers an effective search strategy. In this study, the algorithm is tested using the coordinates of 115 real disaster and emergency assembly areas located in Karşıyaka district of İzmir province. The performance of RPOA is compared with the classical randomized TSP algorithm; its solution quality and computation time are analyzed. The results show that RPOA produces shorter routes and, in some cases, reaches a solution faster. These findings demonstrate the algorithm's usability in time-critical applications such as disaster management.