Meng Weiping, He Yang, Zhou Yongquan
College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China.
Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China.
Biomimetics (Basel). 2025 Jan 15;10(1):57. doi: 10.3390/biomimetics10010057.
This paper proposes a Q-learning-driven butterfly optimization algorithm (QLBOA) by integrating the Q-learning mechanism of reinforcement learning into the butterfly optimization algorithm (BOA). In order to improve the overall optimization ability of the algorithm, enhance the optimization accuracy, and prevent the algorithm from falling into a local optimum, the Gaussian mutation mechanism with dynamic variance was introduced, and the migration mutation mechanism was also used to enhance the population diversity of the algorithm. Eighteen benchmark functions were used to compare the proposed method with five classical metaheuristic algorithms and three BOA variable optimization methods. The QLBOA was used to solve the green vehicle routing problem with time windows considering customer preferences. The influence of decision makers' subjective preferences and weight factors on fuel consumption, carbon emissions, penalty cost, and total cost are analyzed. Compared with three classical optimization algorithms, the experimental results show that the proposed QLBOA has a generally superior performance.
本文通过将强化学习的Q学习机制集成到蝴蝶优化算法（BOA）中，提出了一种基于Q学习驱动的蝴蝶优化算法（QLBOA）。为了提高算法的整体优化能力，提升优化精度，并防止算法陷入局部最优，引入了具有动态方差的高斯变异机制，还采用了迁移变异机制来增强算法的种群多样性。使用18个基准函数将所提出的方法与五种经典元启发式算法和三种BOA变量优化方法进行比较。将QLBOA用于解决考虑客户偏好的带时间窗绿色车辆路径问题。分析了决策者主观偏好和权重因素对燃料消耗、碳排放、惩罚成本和总成本的影响。与三种经典优化算法相比，实验结果表明所提出的QLBOA具有总体上更优的性能。
