Water Ecotourism Route Recommendation Model Based on an Improved Cockroach Optimization Algorithm

Aiming to address the problems of the current research on water ecotourism routes, a water ecotourism route recommendation model based on an improved cockroach optimization algorithm is proposed. The aim is to recommend the tour routes with the lowest exhaust emissions. Firstly, depending on tourist...

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Veröffentlicht in:Water (Basel) Jg. 14; H. 13; S. 2014
Hauptverfasser: Zhou, Xiao, Chen, Lingyu, Su, Mingzhan, Tian, Jiangpeng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.07.2022
Schlagworte:
algorithms
Blattodea
Buses
Collaboration
Ecotourism
Food products
Optimization algorithms
Recommender systems
Route optimization
Tourism
transportation
Travel
water
Water sports
ISSN:2073-4441, 2073-4441
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Zusammenfassung:Aiming to address the problems of the current research on water ecotourism routes, a water ecotourism route recommendation model based on an improved cockroach optimization algorithm is proposed. The aim is to recommend the tour routes with the lowest exhaust emissions. Firstly, depending on tourists’ once-visited water scenic spots, a scenic spot recommendation model based on the improved item-based collaborative filtering algorithm is set up. Then, by combining the recommended scenic spots and integrating the random transportation modes selected by tourists, a tour route recommendation model based on an improved cockroach optimization algorithm is constructed, which can output the tour route that produces the lowest exhaust emissions. Finally, The sample experiment shows that, on the basis of combining with the multivariate random transportation modes, the proposed algorithm has greater advantages than the tour routes planned by the traditional electronic maps, as it can output the tour routes with the lowest exhaust emissions, reduce the damage exhaust emissions cause in the urban water environments and to water resources, and effectively protect the urban water ecological environments.
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ISSN:2073-4441
2073-4441
DOI:10.3390/w14132014