The tourism industry has evolved through the adoption of advanced algorithms and data-driven approaches in itinerary planning. Key challenges include optimizing travel routes, minimizing transportation costs, and accommodating diverse tourist preferences. Traditional planning systems often struggle with the combinatorial complexity involved in generating efficient, personalized, multi-day itineraries.To address this issue, a travel planning system utilizing the Firefly Algorithm (FA), adapted to solve the Traveling Salesman Problem (TSP) in a tourism context, is presented. The system integrates user defined preferences, travel distance, transportation costs, and destination ratings to generate optimized routes. Experimental results in Yogyakarta demonstrate that the Firefly Algorithm reduced travel distance by 23.2% and 16.1%, transportation cost by 23.7% and 16.1%, and travel time by 18.1% and 12.5%, compared to the Genetic Algorithm and Komodo Mlipir, respectively.
Index Terms—firefly algorithm, travel itenary, route optimiza tion, multi-day travel, transportation cost