Multi-Destination Tourism Itinerary Optimization via Multi-Objective Ant Colony Algorithm (Informatica, 2025)
With the booming global tourism industry and increasingly diversified tourist demands, multi-destination itinerary design faces the challenge of efficiently planning to meet complex constraints and personalized needs. This paper constructs a multi-destination itinerary design model based on the ant colony optimization algorithm and realizes multi-objective optimization through the collaborative work of the path optimization module, demand matching module, and dynamic constraint processing module. Experimental results show that in different scenarios of the number of destinations, the ant colony optimization algorithm is superior to the greedy algorithm, genetic algorithm, and particle swarm optimization algorithm in terms of path length, tourist satisfaction, and cost control. For example, in the 10 destinations scenario, the ant colony optimization algorithm has a path length of 500 km, a high tourist satisfaction rate, and a budget satisfaction rate. This study provides a scientific, reasonable, and personalized solution for tourism itinerary planning, enriching the optimization theory in tourism management.