This study explores the Traveling Salesman Problem (TSP) by applying both deterministic and stochastic algorithms. The deterministic algorithms include the Greedy Algorithm (GrA), Brute Force (BF), Branch and Bound (B&B), Dynamic Programming (DP), and Nearest Neighbor (NN), while the stochastic algorithms include Genetic Algorithm (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), and Particle Swarm Optimization (PSO). The research addresses limitations in the practical application of optimization algorithms in the tourism industry by focusing on minimizing travel distance, time, and cost across eight tourist locations using multi-objective optimization. Unlike most studies that focus solely on theoretical models, this work offers a comprehensive real-world comparison of algorithm performance for tourism route planning. The analysis shows that BF, B&B, and DP achieve optimal results in distance, time, and cost. Among stochastic methods, SA performs competitively alongside GA and PSO for smaller datasets. Notably, DP emerges as the most practical solution for small to medium-sized TSP instances, balancing computational efficiency and optimality. This highlights DP's potential as an effective and efficient algorithm for real-world tourism applications, offering optimal solutions with minimal execution time.
Hossen et al. (Wed,) studied this question.