Research on sustainable carrying capacity of urban tourism environment based on multi objective optimization algorithm
As tourism becomes more and more strategic in the development of modern cities, the state and government are paying more attention to the tourism environment than ever before. The development of the tourism environment involves many interests such as residents, local government and enterprises, whic...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123023004711 |
| _version_ | 1797682475320737792 |
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| author | Qihong Tan |
| author_facet | Qihong Tan |
| author_sort | Qihong Tan |
| collection | DOAJ |
| description | As tourism becomes more and more strategic in the development of modern cities, the state and government are paying more attention to the tourism environment than ever before. The development of the tourism environment involves many interests such as residents, local government and enterprises, which can cause serious harm to the city's economy and environment if not handled properly. Therefore, it is necessary to optimize the carrying capacity of the tourism environment. The study improves the crossover, mutation and elite strategies of non-dominated sorting genetic algorithm II (NSGA-II), and establishes a multi-objective optimization model of urban tourism environment based on this. The results showed that the improved algorithm had a faster convergence speed and the resulting solutions were more uniformly distributed for both the variance probability of 0.005 and 0.05. Compared with the traditional NSGA-II algorithm and the multi-objective genetic algorithm, the Pareto solution set obtained does not appear to be missing in the interval [0,1] and is more widely distributed. In the tests of the DTLZ1 and DTLZ2 functions, the IGD variance values of the improved algorithm were 1.745 E+01 and 3.315E-03, respectively, which showed strong stability. In the empirical analysis, the optimization results obtained by the improved algorithm in the peak, low and flat tourism seasons are more reasonable and maintain a high degree of balance, indicating that it can provide effective guidance for the sustainable development of the urban tourism environment. |
| first_indexed | 2024-03-12T00:00:15Z |
| format | Article |
| id | doaj.art-91b89c4b0070432cb0232ab69b66c24f |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-03-12T00:00:15Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-91b89c4b0070432cb0232ab69b66c24f2023-09-18T04:30:49ZengElsevierResults in Engineering2590-12302023-09-0119101344Research on sustainable carrying capacity of urban tourism environment based on multi objective optimization algorithmQihong Tan0School of Business Administration, Xi'an Eurasia University, Xi'an, 710065, ChinaAs tourism becomes more and more strategic in the development of modern cities, the state and government are paying more attention to the tourism environment than ever before. The development of the tourism environment involves many interests such as residents, local government and enterprises, which can cause serious harm to the city's economy and environment if not handled properly. Therefore, it is necessary to optimize the carrying capacity of the tourism environment. The study improves the crossover, mutation and elite strategies of non-dominated sorting genetic algorithm II (NSGA-II), and establishes a multi-objective optimization model of urban tourism environment based on this. The results showed that the improved algorithm had a faster convergence speed and the resulting solutions were more uniformly distributed for both the variance probability of 0.005 and 0.05. Compared with the traditional NSGA-II algorithm and the multi-objective genetic algorithm, the Pareto solution set obtained does not appear to be missing in the interval [0,1] and is more widely distributed. In the tests of the DTLZ1 and DTLZ2 functions, the IGD variance values of the improved algorithm were 1.745 E+01 and 3.315E-03, respectively, which showed strong stability. In the empirical analysis, the optimization results obtained by the improved algorithm in the peak, low and flat tourism seasons are more reasonable and maintain a high degree of balance, indicating that it can provide effective guidance for the sustainable development of the urban tourism environment.http://www.sciencedirect.com/science/article/pii/S2590123023004711Multi-objective optimizationNSGA-II algorithmUrban tourismEnvironmental carrying capacity |
| spellingShingle | Qihong Tan Research on sustainable carrying capacity of urban tourism environment based on multi objective optimization algorithm Results in Engineering Multi-objective optimization NSGA-II algorithm Urban tourism Environmental carrying capacity |
| title | Research on sustainable carrying capacity of urban tourism environment based on multi objective optimization algorithm |
| title_full | Research on sustainable carrying capacity of urban tourism environment based on multi objective optimization algorithm |
| title_fullStr | Research on sustainable carrying capacity of urban tourism environment based on multi objective optimization algorithm |
| title_full_unstemmed | Research on sustainable carrying capacity of urban tourism environment based on multi objective optimization algorithm |
| title_short | Research on sustainable carrying capacity of urban tourism environment based on multi objective optimization algorithm |
| title_sort | research on sustainable carrying capacity of urban tourism environment based on multi objective optimization algorithm |
| topic | Multi-objective optimization NSGA-II algorithm Urban tourism Environmental carrying capacity |
| url | http://www.sciencedirect.com/science/article/pii/S2590123023004711 |
| work_keys_str_mv | AT qihongtan researchonsustainablecarryingcapacityofurbantourismenvironmentbasedonmultiobjectiveoptimizationalgorithm |