王彦兆,殷旅江,沈明辰,张驰.基于混合遗传模拟退火算法的无人机货舱装载优化研究[J].包装工程,2025,(9):250-259.
WANG Yanzhao,YIN Lyujiang,SHEN Mingchen,ZHANG Chi.Cargo Compartment Loading Optimization of Unmanned Aerial Vehicles Based on Hybrid Genetic Simulated Annealing Algorithm[J].Packaging Engineering,2025,(9):250-259. |
| 基于混合遗传模拟退火算法的无人机货舱装载优化研究 |
| Cargo Compartment Loading Optimization of Unmanned Aerial Vehicles Based on Hybrid Genetic Simulated Annealing Algorithm |
| 投稿时间：2025-03-09 |
| DOI：10.19554/j.cnki.1001-3563.2025.09.029 |
| 中文关键词: 无人机 三维装箱 遗传算法 模拟退火算法 |
| 英文关键词:unmanned aerial vehicle three-dimension packing genetic algorithm simulated annealing algorithm |
| 基金项目:国家社会科学基金一般项目(17BGL238);湖北省高等学校哲学社会科学研究重大项目(23ZD241);湖北省科技厅重点研发课题(KJCXQS2022000225) |
| 作者 | 单位 |
| 王彦兆 | 湖北汽车工业学院，湖北 十堰 442002 |
| 殷旅江 | 湖北汽车工业学院，湖北 十堰 442002 |
| 沈明辰 | 湖北汽车工业学院，湖北 十堰 442002 |
| 张驰 | 湖北汽车工业学院，湖北 十堰 442002 |
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| Author | Institution |
| WANG Yanzhao | Hubei University of Automotive Technology, Hubei Shiyan 442002, China |
| YIN Lyujiang | Hubei University of Automotive Technology, Hubei Shiyan 442002, China |
| SHEN Mingchen | Hubei University of Automotive Technology, Hubei Shiyan 442002, China |
| ZHANG Chi | Hubei University of Automotive Technology, Hubei Shiyan 442002, China |
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| 中文摘要: |
| 目的 旨在解决无人机货舱三维装箱空间利用率的优化问题，突破传统经验装箱法的空间利用瓶颈，实现多约束条件下的高效装载。方法 结合无人机装载货物高支撑度的要求，构建高稳定性的数学模型，采用遗传算法和模拟退火算法相结合的混合优化方法，通过混沌映射生成初始种群，然后采用两段式编码，结合动态空间分割法进行装载优化，最终迭代出最优装载方案。结果 采用提出的混合算法对某汽车零部件企业的20种共390件零件进行仿真实验，在满足支撑约束与其他约束的前提下与原算法进行对比，空间利用率提高5%，可见装箱效果显著优于其他算法。结论 改进的算法装载效率高，稳定性强，为无人机货舱装载问题提供一种有效的优化方法;它面对各种货物均能保证较优的装载效果，解决了某企业经验装箱法中存在的空间利用问题，同时为以后研究无人机装箱问题提供了借鉴，具有较好的应用前景。 |
| 英文摘要: |
| The work aims to solve the optimization problem of three-dimensional container loading space utilization in the cargo hold of unmanned aerial vehicles (UAVs), break through the space utilization bottleneck of the traditional empirical container loading method, and achieve efficient loading under multiple constraints. In combination with the requirement of high support degree for loading goods by unmanned aerial vehicles, a highly stable mathematical model was constructed. A hybrid optimization method combining genetic algorithm and simulated annealing algorithm was adopted. The initial population was generated through chaotic mapping. Then, two-stage coding was used, combined with the dynamic spatial segmentation method for loading optimization. Finally, the optimal loading scheme was iterated out. Simulation experiments were conducted on 390 parts of 20 types in an automotive parts enterprise using the proposed hybrid algorithm. Compared with the original algorithm under the premise of meeting the support constraints and other constraints, the space utilization rate was increased by 5%, indicating that the packing effect was significantly better than other algorithms. In conclusion the improved algorithm has high loading efficiency and strong stability, providing an effective optimization method for the cargo hold loading problem of unmanned aerial vehicles. It can ensure an optimal loading effect for all kinds of goods, solve the space utilization problem existing in the empirical packing method of a certain enterprise, and at the same time provide a reference for the future research on the packing problem of unmanned aerial vehicles, and has a good application prospect. |
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