Research on the optimization of tourist attraction route planning through multiple big data

Abstract

Tourism travel is a prevalent form of leisure and entertainment. This paper provides a brief overview of an attraction route planning algorithm based on multi-source data, which combines multi-source data to assess the effectiveness of the planned route and utilizes the particle swarm optimization (PSO) algorithm for path optimization. The genetic algorithm (GA) operations were incorporated with the PSO algorithm to enhance optimization performance. Subsequently, simulation experiments were conducted to compare the GA-PSO algorithm with using the PSO algorithm and GA. Moreover, a comparative analysis was performed on the performance of the path planning algorithm using single-source and multi-source data. The results demonstrated that the GA-PSO algorithm exhibited the fastest convergence in optimization search and achieved the best fitness value at stabilization. Among the three path schemes, the GA-PSO algorithm performed the best, followed by the GA, while the PSO algorithm was found to be the least optimal. Furthermore, path planning with multi-source data demonstrated better alignment with tourists’ landscape preferences, enabling personalized routes construction.