一种基于膜计算的交叉口多车协同优化方法（英文）

A new membrane computing-based multi-vehicles optimization method in intersection cooperative driving system

交叉口协同驾驶系统通过车辆与交叉口控制系统协同工作来实现交通的控制和管理,使得车辆能够安全和有效地通过交叉口。因此,交叉口协同驾驶系统通过多个无法比较的冲突目标实现协同和优化,是一个典型的带约束的多目标优化问题。为了解决交叉口协同控制系统的多目标优化问题,从V2X的角度提出了一种基于改进膜计算系统的多车优化方法。通过种群膜系统刻画了交叉口协同驾驶系统的多目标优化问题,然后通过提出的改进膜计算系统的多车优化方法对交叉口协同驾驶系统的多目标优化问题进行了求解。最后,通过实验和分析表明了提出的方法优于其他4种算法。

The intersection cooperative driving system（ICDS） envisions that vehicles and an intersection controller could cooperatively work together to improve traffic operations and managements so that vehicles can safely and efficiently cross the intersection. Thus, the cooperation and optimization of the ICDS, which is solved simultaneously on incommensurable and conflicting objectives, is a typical multi-objective optimization problem（MOOP） with constrained conditions. For solving the ICDS＇ s MOOP, an improved membrane computing-based multi-vehicles optimization method （IMC-MOOM） is proposed from vehicle-to-everything （V2X） perspective. In detail, the ICDS＇ s MOOP is described using population P system, and then the IMC-MOOM is proposed to solve the ICDS＇ s MOOP. Finally, experimental results and analysis demonstrate that our proposed method is superior or competitive to four optimization evolution algorithms recently reported in the literature.