Optimized Green Intelligent Transportation System Using Edge Computing with Multi-objective Metaheuristic Algorithm

Intelligent Transportation System (ITS) employs traditional optimization methods, including gradient-based or evolutionary methods. These methods often fail to simultaneously balance predictive accuracy, latency, and resource efficiency while respecting strict operational constraints. Multi-objective system design under explicit constraints is also a challenge in engineering and artificial intelligence. To address these gaps, we propose a Transfer Learning-Enhanced Multi-Objective Whale Optimisation Algorithm (TL-MOWOA) for constrained multi-objective optimisation of control parameters in Intelligent Transportation Systems (ITS). The framework integrates transfer learning to accelerate convergence by reusing knowledge from related optimisation tasks. In this way, it improves adaptability across diverse traffic scenarios. Evolutionary operators of the Whale Optimisation Algorithm, within the TL-MOWOA Algorithm, preserve population diversity and ensure robust global search. The transfer-learned priors guide the search toward promising feasible regions, hence reducing the risk of stagnation. Experimental evaluation was conducted on synthetic benchmarks, including balanced and high-dimensional constrained. The results signify that the TL-MOWOA Algorithm better for state-of-the-art algorithms. The proposed TL-MOWOA Algorithm achieves up to 15% reduction in average travel time, 12% reduction in fuel consumption, 20% reduction in congestion index, and 18% reduction in emissions as compared to NSGA-III, MOPSO, MOWOA, and standalone TL-based EAs. The proposed TL-MOWOA Algorithm provides a scalable and adaptive solution for ITS optimisation.