In order to improve the performance of Particle Swarm Optimization (PSO) algorithm in solving continuous function optimization problems, a chaotic particle optimization algorithm for complex functions is proposed. Firstly, the algorithm uses qubit Bloch spherical coordinate coding scheme to initialize the initial position of the population. This coding method can expand the ergodicity of the search space, increase the diversity of the population, and further accelerate the convergence speed of the algorithm. Secondly, Logistic chaos is used to search the elite individuals of the population, which effectively prevents the PSO algorithm from falling into local optimization, thus obtaining higher quality optimal solution. Finally, complex functions are used to improve chaotic particles to further improve the convergence speed and optimization accuracy of PSO algorithm. Through the optimization tests of four complex high-dimensional functions, the simulation results show that the improved algorithm is more competitive and its overall performance is better, especially suitable for the optimization of complex high-dimensional functions.

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.