Optimization of single warehouse multi traveler problem based on improved sand cat swarm optimization

The Single Warehouse Multiple Traveling Salesman Problem (SDMTSP) is an extension of the Traveling Salesman Problem (TSP), widely used in fields such as logistics distribution and transportation planning. This article proposes a new method for solving SDMTSP based on the optimized Sand Cat Swarm Optimization (SCSO). Building on the traditional Sand Cat Algorithm, interval point operations, the pheromone mechanism from the Ant Colony Algorithm (ACA), and an improved t-adaptive mutation operation are introduced to enhance the algorithm's performance in solving multiple traveling salesman problems. Simulation experiment results show that the improved Sand Cat Algorithm performs well under the conditions of N=100, N_Salesmen=4, N_minTour=5, α=0, β=0.1, and δ=0.9. With 300 repeated iterations, the algorithm performed well on multiple standard test problems and effectively reduced the total travel distance.