Research on location selection of electric vehicle charging facilities based on hybrid algorithm

This study addresses the location problem of electric vehicle charging facilities by constructing a bi-level programming model and designing a hybrid algorithm for its solution. The upper planning model aims to minimize the construction and operation as well as maintenance costs of charging facilities, while the lower planning model focuses on minimizing the charging costs for users. Traditional heuristic algorithms often encounter issues of getting trapped in local optima and yielding low solution accuracy when solving location models. Therefore, this study proposes a hybrid algorithm, namely the particle swarm-grey wolf hybrid algorithm(IPSOGWO), based on the particle swarm optimization algorithm (PSO) and an improved grey wolf optimization(GWO). This algorithm incorporates a nonlinear convergence factor to enhance the search and development capabilities of GWO and combines the two algorithms by improving the position update formula of PSO. Experimental results from optimizing four benchmark functions demonstrate that IPSOGWO outperforms traditional heuristic algorithms in terms of convergence speed and accuracy. Based on this hybrid algorithm, the location model is effectively solved. The results reveal that the proposed algorithm and model successfully reduce the overall location cost and provide novel approaches and insights for the scientific placement of electric vehicle charging facilities.