This paper addresses the challenge of locating electric vehicle (EV) charging stations in large cities by proposing an improved sparrow search algorithm (ISSA), an enhancement of the original bio-inspired sparrow search algorithm (SSA). While SSA has shown effectiveness, it suffers from premature convergence and limited population diversity in complex, high-dimensional problems. ISSA mitigates these drawbacks through three modifications: dynamic inertia weights to balance exploration and exploitation; chaotic initialisation with a logistic map to increase population variety; and a population diversity index to prevent stagnation. A mathematical model for site selection is developed to minimise installation costs and meet demand while respecting urban constraints such as capacity limits and green zones. The research aims to improve SSA with adaptive components, construct a site-selection model and validate ISSA in a simulated urban environment. Simulation results in a hypothetical city demonstrate ISSA’s superior performance, achieving 41.9% faster convergence, 21.1% lower total installation costs and 32.6% higher computational efficiency compared to SSA. In terms of charging infrastructure planning, the optimised station layout ensures effective coverage of EV charging demand while respecting station capacity constraints and urban land-use limitations, leading to a more balanced and cost-efficient deployment of charging stations.
Du et al. (Wed,) studied this question.