Development and utilization of tourism resources optimized by genetic algorithms: Taking rural tourism as an example

Abstract

Whether tourism resources can be scientifically and effectively developed and utilized is directly related to the amount of economic benefits that tourism resources bring to developers, as well as the revenue data of the local tourism industry. Therefore, how to develop tourism resources has become the main issue that countless scenic area developers need to study today. When formulating a tourism resource development plan using traditional methods, it is usually necessary to arrange researchers to evaluate the quantity and quality of the tourism resources owned by the scenic area. This process often takes several months, and after obtaining the evaluation data, it will be submitted to the decision-making level for repeated and unpredictable meetings and discussions. The discussion mainly focuses on the proportion of service investment and specific measures for each resource in the scenic area, and finally a preliminary plan is obtained. In response to the drawbacks of traditional methods such as time-consuming and cumbersome steps, this study attempts to apply genetic algorithms to optimize the development of tourism resources, hoping to provide an intelligent and efficient method for formulating development plans. The process of using genetic algorithms to develop tourism resource development plans is as follows. Firstly, the optimization task was modeled, abstracted into a mathematical representation that the model can understand, and model parameters were set for subsequent iterative tasks; then, the population was randomly initialized to provide a richer gene pool for the entire population, allowing individuals in the population to be distributed throughout the solution space. Next, it is necessary to iterate the population, where individuals within the population undergo selection, crossover, and mutation in each iteration round, while adding randomness to evolve towards higher fitness values. When the iteration round ends, the highest fitness value of individuals in the population can converge, and this individual represents the best solution considered by the model. Five simulation experiments were conducted in this article. The initial population size was 100, 120, 140, 80, and 70, and the number of iteration rounds was 100, 80, 70, 110, and 130. Finally, the highest fitness values of the five experiments all converge to 208.9, and the X of the individual with the highest fitness values converges to [1,1,1,0,0]. Y converges approximately at [0.44, 0.41, 0.15, 0,0], and Z converges at [0.4, 0.3, 0.3]. Finally, this article also compares with examples of rural tourism development to verify the effectiveness and practicality of genetic algorithms in optimizing tourism resources. After calculation, the sample distance between excellent development cases and model generated solutions is 7.512, the sample distance between negative cases and model generated solutions is 31.836, and the sample distance between fuzzy cases and model generated solutions is 16.757. The experimental results demonstrate that the use of genetic algorithms can provide scientific decision support and methodological guidance for the development and utilization of tourism resources.