Vehicle evacuation route planning in nuclear emergencies based on hybrid ant colony algorithm

ZHOU Huaifang; ZHANG Hua; HUO Jianwen; Li Linjing; CHEN Bo; Lin Haitao

doi:10.11889/j.1000-3436.2023-0030

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Vehicle evacuation route planning in nuclear emergencies based on hybrid ant colony algorithm

RADIATION PROTECTION | 更新时间：2023-12-18

- Vehicle evacuation route planning in nuclear emergencies based on hybrid ant colony algorithm
- Journal of Radiation Research and Radiation Processing Vol. 41, Issue 6, Pages: 60601(2023)
- 作者机构：
  
  1.西南科技大学信息工程学院绵阳 621010
  2.西南科技大学特殊环境机器人技术四川省重点实验室绵阳 621010
- 作者简介：
  
  ZHOU Huaifang (female) was born in December 1995, and obtained a master's degree from Xinjiang University in 2020. Now she is a doctoral candidate at Southwest University of Science and Technology, majoring in control science and engineering, E-mail: zhouhuaifang2020@163.com
  ZHANG Hua, professor, E-mail: swust_aa@126.com
- 基金信息：
  
  National Natural Science Foundation of China(12205245);Sichuan Provincial Science and Technology Plan(2021YFG0376)
- DOI：10.11889/j.1000-3436.2023-0030
  CLC：
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周怀芳, 张华, 霍建文, 等. 基于混合蚁群算法的核应急车辆疏散路径规划[J]. 辐射研究与辐射工艺学报, 2023, 41(06): 060601.

ZHOU Huaifang, ZHANG Hua, HUO Jianwen, et al. Vehicle evacuation route planning in nuclear emergencies based on hybrid ant colony algorithm[J]. Journal of Radiation Research and Radiation Processing, 2023, 41(6): 060601.
周怀芳, 张华, 霍建文, 等. 基于混合蚁群算法的核应急车辆疏散路径规划[J]. 辐射研究与辐射工艺学报, 2023, 41(06): 060601. DOI： 10.11889/j.1000-3436.2023-0030.

ZHOU Huaifang, ZHANG Hua, HUO Jianwen, et al. Vehicle evacuation route planning in nuclear emergencies based on hybrid ant colony algorithm[J]. Journal of Radiation Research and Radiation Processing, 2023, 41(6): 060601. DOI： 10.11889/j.1000-3436.2023-0030.

摘要

核事故的发生具有不可预测性和破坏性，为应急车辆制定合理的疏散计划将危险区域的人员撤离至安置点，可以有效减少人员所受到的伤害。针对核事故下应急车辆路径规划问题，以累积辐射剂量为评价指标，提出了一种基于混合蚁群算法（Hybrid ant colony algorithm，HACO）的车辆路径规划方法。首先，利用模糊网络建立了时间窗内疏散路径平均通行时间期望模型，同时结合累积辐射剂量计算模型，建立了能够随时间变化的动态累积辐射剂量计算模型。然后在蚁群算法迭代过程中引入模拟退火算法，并且在邻域搜索中引入A*算法启发式思想，提高了算法全局寻优能力。为进一步提高算法的局部搜索能力，引入帕累托排序方式，在蚁群算法信息素更新方式中加入距离对信息素增量的影响。仿真结果表明：HACO算法相较于蚁群算法平均收敛值提高了31%，稳定性提高了30%，能够为核事故下疏散路径规划预案的制定提供技术支持。

Abstract

Nuclear accidents, although unpredictable and devastating, can be mitigated through well-formulated evacuation plans. An efficient evacuation of residents from hazardous zones to safer locations can be ensured through such plans. To address the vehicle path planning challenge under nuclear accidents, this paper proposes a method based on the hybrid ant colony algorithm (HACO). Cumulative radiation dose is used as a key assessment metric. Initially, a model estimating the average time for evacuating a route within a given time window is designed using a fuzzy network. In addition, a time-varying dynamic radiation dose model is proposed by incorporating the cumulative radiation dose calculation. The ant colony algorithm's iterative process is enhanced by the incorporation of the simulated annealing algorithm, while the heuristic approach of A* algorithm is employed for neighborhood searches. This integration results in an enhanced capacity for global optimization of the algorithm. For refining the local search capabilities of the algorithm, Pareto ordering is implemented. Additionally, the pheromone update method of the ACO algorithm is adjusted to account for the impact of distance on pheromone increments. Upon employing the HACO algorithm, simulation results indicate a 31% improvement in average convergence value and 30% boost in stability over the conventional ACO algorithm. These enhancements are instrumental in fortifying the planning of evacuation routes in the event of nuclear accidents.

关键词

核事故路径规划混合蚁群算法动态模糊网络累积辐射剂量模型

Keywords

Nuclear accidentsPath planningHybrid ant colony algorithmDynamic fuzzy networkCumulative radiation dose model

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