Jinling Du
Multi-objective evolutionary algorithm, smart tourism, recommendation of interest points, journey planning
The rapid development of smart tourism has brought new opportunities and challenges to people’s travel experience. However, due to numerous interest points and complex and diverse needs of tourists, traditional methods often cannot meet the needs of individual and multi-objective. To solve this problem, this paper proposes to establish an evaluation model which takes into account various factors, such as tourists’ interest preference, time constraint, and budget constraint. By introducing an improved evolutionary algorithm, genetic operators are combined with local search strategies to generate personalised travel routes. Experimental results show that the improved multi-objective evolutionary algorithm performs well in point of interest recommendation and trip planning. Compared with traditional methods, WAEA algorithm can better meet the individual needs of tourists and deal with multi-objective optimisation problems.
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