With the hot issues such as smart city and ecological city put forward, the development of intelligence and informatization of urban space has been established, especially the Internet of Things. With the wide application of location-based social networks, users can share their location of interest in location. By analyzing users' historical geographic information, location service recommendation can recommend geographic locations to users to help users obtain better access experience. Combined with genetic algorithm (GA) algorithm, a recommendation algorithm based on geographic location service optimization is proposed, which can better recommend to users. Aiming at the problem of slow convergence speed of GA, a fast adaptive genetic algorithm (FAGA) method is proposed to optimize location services. In the experimental part, comparing several functions, FAGA's test effect and convergence are ideal. By comparing FAGA-least squares support vector machine (LSSVM) algorithm with other methods in location service recommendation, FAGA-LSSVM method has more advantages.

中文翻译：

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物联网背景下基于快速自适应遗传算法-最小二乘支持向量机的城市空间位置服务预测算法

随着智慧城市、生态城市等热点问题的提出，城市空间尤其是物联网的智能化、信息化发展已经确立。随着基于位置的社交网络的广泛应用，用户可以分享他们感兴趣的位置。通过分析用户的历史地理信息，位置服务推荐可以向用户推荐地理位置，帮助用户获得更好的访问体验。结合遗传算法（GA）算法，提出了一种基于地理位置服务优化的推荐算法，可以更好地向用户推荐。针对遗传算法收敛速度慢的问题，提出了一种快速自适应遗传算法（FAGA）方法来优化位置服务。在实验部分，比较几个函数，FAGA的测试效果和收敛性比较理想。通过将FAGA-最小二乘支持向量机（LSSVM）算法与其他位置服务推荐方法进行比较，FAGA-LSSVM方法更具优势。