Urban spatial location service prediction algorithm based on fast adaptive genetic algorithm‐least squares support vector machine under the background of Internet of Things

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Abstract
Summary: 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.
Funding Information
  • Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN201902103)
  • Open Fund for Chongqing Key Laboratory of Spatial Data Mining and Big Data Integration for Ecology and Environment (ZKPT0120193007)

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