Building Sustainable Tourism Based on Genetic Algorithm Optimization of Partial Least Squares Method

Haoyu Sheng; Jingwen Fang; Xiaoman Su

doi:10.54097/2w1jyp06

Authors

Haoyu Sheng
Jingwen Fang
Xiaoman Su

DOI:

https://doi.org/10.54097/2w1jyp06

Keywords:

Sustainable Tourism, Genetic Algorithm, Partial Least Squares, Over-Tourism, Multi-Objective Optimization

Abstract

This study proposes a sustainable tourism optimization model by integrating Partial Least Squares Regression (PLSR) and Genetic Algorithms (GA). Targeting over-tourism issues in cities like Juneau and New York, the model balances economic growth, environmental protection, and social well-being. PLSR identifies key influencing factors such as income, crime rates, and emissions, while GA optimizes policy variables to achieve multi-objective goals. Results show strong explanatory power (R² > 0.9) and offer practical, data-driven strategies for sustainable urban tourism development.

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