Efficient EV Charging Allocation in Fog Computing via Committee-Based Surrogate-Assisted PSO

Abstract

Efficient real-time resource allocation for electric vehicle (EV) charging in Fog computing environments demands fast and intelligent decision-making under strict quality-of-service constraints. Traditional metaheuristics like genetic algorithms and differential evolution yield high-quality solutions but incur prohibitive computational costs, limiting their applicability in real-time systems. This paper introduces the Committee-Based Active Learning Surrogate-Assisted Particle Swarm Optimization (QBC-SA-PSO) framework, which combines multiple surrogate models with a Query by Committee (QBC) strategy to intelligently approximate fitness evaluations. By balancing exploration and exploitation, the framework drastically reduces the need for expensive exact simulations while maintaining near-optimal solution quality. Experimental validation on EV charging datasets demonstrates that PSO-SA-QBC converges within only 33 iterations, achieving a 66% reduction compared to traditional simulation techniques replaced with exact fitness evaluations, while preserving over 99% solution quality.