In this paper, the optimization issue of electric vehicle charging station layout (EVCSL) for tourist attractions is addressed, and an improved PSO is used to solve the optimization issue. Specifically, the improved particle swarm optimization (PSO) is proposed to obtain an appreciative planning solution of EVCSL, and dynamic weight adjustment strategy and integration into the two-population genetic mode are proposed to improve the optimization quality for PSO. Simulation results show that the proposed improvement strategies can increase the optimization quality for PSO effectively so that a more appreciative planning solution of EVCSL can be obtained.

This work discusses the electric vehicle (EV) ordered charging planning (OCP) optimization problem. To address this issue, an improved dual-population genetic moth–flame optimization (IDPGMFO) is proposed. Specifically, to obtain an appreciative solution of EV OCP, the design for a dual-population genetic mechanism integrated into moth–flame optimization is provided. To enhance the global optimization performance, the adaptive nonlinear decreasing strategies with selection, crossover and mutation probability, as well as the weight coefficient, are also designed. Additionally, opposition-based learning (OBL) is also introduced simultaneously. The simulation results show that the proposed improvement strategies can effectively improve the global optimization performance. Obviously, more ideal optimization solution of the EV OCP optimization problem can be obtained by using IDPGMFO.

This work discuses an optimization problem of ordered charging planning (OCP) for electric vehicle clusters (EVCs) in tourist attractions. To address this issue, an improved moth-flame optimization integrated with genetic mechanism (IMFO-G) is proposed to solve the optimization problem. Specifically, based on the moth-flame optimization integrated with genetic mechanism, the adaptive nonlinear decreasing strategies with selection, crossover and mutation probability as well as weight coefficient are designed, and the opposition-based learning is also introduced simultaneously. As a result, the global optimization performance can be improved effectively. The simulation results show that the proposed improvement strategies can effectively improve the global optimization performance for IMFO-G, and a more ideal optimization solution of the OCP optimization problem for EVCs in tourist can be obtained.

The traffic data around the scenic spot and the daily average passenger flow trend data associated with the tourist attractions are closely related to the travel. Considering the multiple factors hindering travel, the traffic structure adjustment model around the scenic spot to meet the needs of tourists is put forward, which can also be used as a reference for the management and guidance of tourist attractions. As for solving the problem that the traditional linear regression statistical model has a large error in the parameters of a single function, this paper proposes a prediction model based on Fourier transform. Using the least squares method of time trajectory and the corresponding relationship of spatial trajectory fusion, it improves the matching mode of the convolutional network and establishes a time domain division model to ensure the smooth flow of tourism traffic. Through the integration of real-time data optimization calculation of the time trajectory and spatial adjacent nodes, it provides short-term traffic commuting trend prediction for tourism projects. The experimental results indicate that the prediction results of the proposed method have the best fitting degree with the actual data. The calculation accuracy is more than 8. 5% higher than that of the comparative method, which can more accurately predict the traffic trend around the scenic spot. It also provides a reference for intelligent traffic management as well as for other traffic management platforms.