基于改进量子微粒群的轨道导引小车系统建模与优化

Modeling and optimization of RGV system based on improved QPSO

为提高自动小车存取系统中轨道导引小车系统的出入库作业效率,提出了一种基于改进量子微粒群的优化方法。分析了轨道导引小车系统出入库作业任务队列特征,建立了数学模型。在此基础上利用量子微粒群算法进行优化调度,并在该算法中引入高斯变异算子,克服了其容易陷入局部最优的缺点。通过仿真实验表明了方法的可行性和有效性。

To improve the performance of Rail-Guided Vehicles System（RGVS） in Automatic Vehicle Storage and Retrieval Systems（AVS/RS）,an optimization method based on improved Quantum Particle Swarm Optimization（QPSO） was proposed.Firstly,sequencing characteristics of tasks in RGVS were analyzed,and a mathematical model was established.Then,a QPSO algorithm was proposed to solve the scheduling problem.Meanwhile,the Gaussian mutation operator was introduced into this algorithm to overcome its shortcoming of falling into local convergence.Finally,feasibility and effectiveness of the presented method was shown by experimental results.