<![CDATA[Efficient route planning is essential in modern tourism, particularly for multi-destination travel in complex urban environments. This study proposes a tourism route optimization model based on Ant Colony Optimization (ACO) integrated with real-world geographic distance data. The problem is modeled as a weighted graph, where tourist destinations are represented as nodes and edge weights are derived from distances obtained via the Google Maps Directions API. The objective is to determine an optimal route that minimizes total travel distance across multiple destinations. The ACO algorithm employs a probabilistic search mechanism based on pheromone trails and heuristic visibility to iteratively construct candidate routes. Experimental results show that the model consistently converges to the optimal route sequence 1 → 4 → 2 → 5 with a total distance of 8323 meters under appropriate parameter settings. The findings also indicate that heuristic influence plays a critical role in ensuring convergence and solution quality. Compared to a deterministic baseline based on Dijkstra’s algorithm, ACO demonstrates greater flexibility in exploring multiple route combinations, making it more suitable for multi-destination routing. The integration of real-world data enhances the practical applicability of the model, although it introduces a higher computational cost.]]>
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