The rapid growth of global maritime transportation has intensified demands for safe and efficient shipping routes optimization. Conventional particle swarm optimization (Particle Swarm Optimization PSO) and its quantum variant (Quantum-behaved Particle Swarm Optimization QPSO) frequently encounter challenges such as premature convergence and generate infeasible solutions under complex navigational constraints. To overcome these limitations, we propose a novel Q-learning–based quantum-behaved Particle Swarm Optimization (QLQPSO) algorithm. This approach incorporates a multi-dimensional state space and a Q-learning controller to adaptively regulate the contraction–expansion balance, attraction-center switching, simulated annealing, and path smoothing. Furthermore, a customized “projection + smoothing” operator is introduced to ensure navigational feasibility of the generated routes. Experimental evaluations conducted on the China-Australia shipping corridor demonstrate that the QLQPSO algorithm achieves superior performance, including the shortest voyage distance (3772 nm), reduced curvature, and near-perfect feasibility, consistently outperforming PSO, QPSO, Reinforcement Learning Particle Swarm Optimization(RLPSO), and Reinforcement Learning Quantum-behaved Particle Swarm Optimization(RLQPSO). Ablation studies further verify the critical roles of the Q-learning-driven scheduling mechanism and the constraint-repair operat or in enhancing algorithm effectiveness. In summary, the QLQPSO algorithm presents a robust and efficient solution for maritime route optimization in complex nautical environments, offering significant potential for practical applications in the maritime industry.
Liankang et al. (Thu,) studied this question.