基于改进遗传神经网络的农业观光旅游竞争力评价  

Agricultural tourism competitiveness evaluation based on improved genetic neural network

作　　者：韩燕妮[1] HAN Yan-ni(School of Finance and Economics,Xianyang Vocational Technical College,Xianyang 712000,Shaanxi,China)

机构地区：[1]咸阳职业技术学院财经学院,陕西咸阳712000

出　　处：《湖北农业科学》2022年第22期215-219,共5页Hubei Agricultural Sciences

基　　金：2021年度咸阳职业技术学院科研基金重点项目(2021SKB01)。

摘　　要：旅游业作为第三产业的重要组成部分,已经从高速发展阶段转向高质量发展阶段。近年来,农业观光旅游成为中国深入供给侧结构性改革的创新点。但由于中国农业观光旅游发展时间短,现有的评价体系不适合农业观光景点的旅游竞争力评价,景点质量层次不齐。此次研究提出一种改进的遗传神经网络模型以对农业观光旅游景点的旅游竞争力进行评价。通过扩大种群数量来提升种群多样性,采用二阶段求解方式降低求解难度,再对遗传算法进行并行处理。改进后的算法在效率上提升30%左右,在性能上提升10%~20%。此次研究提出的并行方案和构建的竞争力评价指标都具备较高的实用性和客观性,可为相关研究提供一定参考。Tourism,as an important part of the tertiary industry,has shifted from the high-speed development stage to the high-quality development stage.In recent years,agricultural tourism has become the innovation point of supply-side structural reform in China.However,due to the short development time of agricultural sightseeing tourism in China,the existing evaluation system is not suitable for the evaluation of tourism competitiveness of agricultural sightseeing scenic spots,and the quality levels of scenic spots are not uniform.This study proposed an improved genetic neural network model to evaluate the tourism competitiveness of agricultural sightseeing scenic spots.The diversity of the population was improved by expanding the number of the population,and the two-stage solution was adopted to reduce the difficulty of solving the population,and then the genetic algorithm was processed in parallel.The improved algorithm improved the efficiency by about 30%and the performance by about 10%~20%.The parallel scheme proposed in this study and the competitiveness evaluation index constructed had high practicality and objectivity,which could provide some reference for related research.

关 键 词：农业观光 旅游竞争力 遗传神经网络 二阶段求解 并行化 

分 类 号：F592[经济管理—旅游管理] TP183[经济管理—产业经济]