Optimization and simulation of air transportation loading path based on adaptive genetic algorithm

LI Hongwei; WEI Xueqiang; SU Weibo

doi:10.7688/j.issn.1000-1646.2025.03.13

Abstract：[Objective]In the context of the rapid development of the aviation industry,the scale and level of air transportation have significantly improved,air transportation becomes an indispensable mode of transportation in economic activities.However,the issue of cargo loading path planning in air transportation limits the optimization of transportation efficiency and cost.To address the challenges of enhancing operational efficiency and optimizing costs in air transportation,this paper proposed an air transportation loading path optimization algorithm based on an adaptive genetic algorithm.[Methods]To elucidate the loading path optimization algorithm for air transportation,this study analyzed the actual needs of air transportation loading and the computational conditions of the path planning platform and explored the transportation cost factors influencing the optimization of air transportation loading paths.On this basis,an improved genetic algorithm with adaptive capabilities was employed,utilizing adaptive fitness functions,crossover probabilities,and mutation probabilities to circumvent the issues of poor stability and slow convergence speeds inherent in traditional algorithms.The essence of this algorithm was the dynamic adjustment of crossover probabilities and mutation probabilities to align with the evolutionary state of the population,thereby augmenting the algorithm's global search capability and convergence speed.During the research,the encoding method of the adaptive genetic algorithm,the establishment of the fitness function,and the calculation method and control principle of crossover probabilities and mutation probabilities were detailed,along with the specific execution steps of the loading path optimization algorithm.The algorithm was implemented on the MATLAB platform and tested using actual distribution data from an air transportation airport.[Results]The simulation results demonstrate that compared to traditional genetic algorithm,intelligent water drop algorithm,and improved ant colony algorithm,the air loading path optimization algorithm based on the adaptive genetic algorithm exhibits significant advantages in both transportation efficiency and overall transportation cost.In other words,the air loading path optimization algorithm can effectively reduce the average transportation cost and enhance transportation efficiency.However,actual air transportation loading processes are influenced by complex environment factors,such as the size limitation of aircraft cargo hold and the complex road conditions during delivery.These problems have not been deeply considered in the algorithm,which indicates that there is still room for improvement in the algorithm.[Conclusion]In summary,the air transportation loading path planning algorithm based on adaptive genetic algorithm introduces an improved genetic algorithm with adaptive mechanism,which shows better global search ability and convergence speed in solving the air transportation loading path planning problem.This paper provides a new idea for air transportation loading path planning and is also of important theoretical and practical value for the field of air logistics.Future studies will aim to take into account more actual operating environment factors to further optimize algorithm performance.