Quantum-inspired NSGA-II for multi-objective optimization of electric vehicle charging stations.

The growing adoption of electric vehicles (EVs) demands reliable and economically viable charging infrastructures that minimize investment risk while ensuring grid stability. Conventional planning approaches often fall short by either overestimating installation needs or underutilizing existing network capacity. This study presents an Entangled Adaptive Hybrid Quantum-Inspired NSGA-II (EAH-QNSGA-II) designed to address these challenges through a multi-objective optimization framework. The method incorporates quantum-inspired representation, entanglement-driven crossover, adaptive quantum rotation, and a localized search mechanism, enabling a balanced exploration–exploitation process and maintaining high-quality Pareto solutions. The proposed framework is evaluated on four benchmark datasets: Palo Alto EV charging records, Boulder public charging usage, the Multi-Faceted EV Charging Transactions dataset, and the IEEE 33-bus distribution system for grid integration. The proposed method aims at minimizing installation cost while simultaneously maximizing service coverage and improving grid load stability in the presence of renewable energy variability. Experimental findings reveal that EAH-QNSGA-II delivers significant gains compared with state-of-the-art approaches. Against classical NSGA-II, the method achieves 34.1% lower installation cost, 30.2% better coverage, and 41.7% stronger grid load balancing. When compared with quantum-inspired PSO, Jaya, EAQGA, and AHQSOA, the proposed method enhances hypervolume by 18–24% and spread by 15–19%, highlighting superior convergence and diversity. These results confirm EAH-QNSGA-II as an efficient and scalable solution framework for future EV charging infrastructure deployment.