Scalable Multi-Objective Optimization for Robust Traffic Signal Control in Uncertain Environments Based on Hierarchical Reinforcement Learning

Effective urban traffic signal control in large-scale networks remains challenging due to complex interdependencies among intersections and unpredictable fluctuations in traffic conditions. To address these challenges, this paper proposes a novel Adaptive Hybrid Multi-Objective Optimization Algorithm with Reinforcement Learning (AHMOA-RL) for robust and scalable traffic signal management. The core innovation of AHMOA-RL lies in a hierarchical optimization framework that efficiently decomposes the problem into global region-level coordination and local intersection-level refinements, significantly reducing computational complexity while ensuring synchronized control across extensive urban networks. A Q-learning agent dynamically selects among multiple evolutionary operators—Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, and Local Search—to strategically balance exploration and exploitation during optimization. Additionally, a memory-based evaluation mechanism leveraging historical data is integrated to smooth transient traffic anomalies and provide stable performance estimates. Extensive simulations on large-scale city networks inspired by Manhattan, Paris, São Paulo, and Istanbul demonstrate that AHMOA-RL consistently outperforms state-of-the-art methods, achieving substantial reductions in average vehicle delays, improved network stability, and enhanced robustness under diverse traffic conditions. The algorithm’s compact Pareto fronts and superior convergence characteristics validate its effectiveness for practical deployment in complex urban environments.