Improved Genetic Algorithm Enhanced with Generative Adversarial Networks for Logistics Distribution Path Optimization

Abstract

This paper proposes an innovative logistics distribution path planning algorithm, which aims to combine the generative adversarial network (GAN) with the genetic algorithm (GA) to solve the path optimization problem in large-scale distribution networks. The GA-GAN algorithm intelligently improves the mutation operation of the genetic algorithm through GAN, which not only outperforms the traditional genetic algorithm and other classic heuristic algorithms in terms of solution quality, operation efficiency, convergence speed and solution stability, but also provides quantitative data of specific improvements. Experimental results show that when GA-GAN processes a data set of 500 customer points, the average running time is 160 seconds, the optimal solution cost is 9500 units, the average solution cost is 10500 units, and it can reach the optimal solution within 180 iterations, which is significantly better than the baseline genetic algorithm (average running time is 150 seconds, the optimal solution cost is 10000 units, the average solution cost is 12000 units, and the average number of iterations required to reach the optimal solution is 300 times). In addition, GA-GAN has good responsiveness to the size of the data set and has a wide range of adaptability to different distribution scenarios, providing an efficient, stable and flexible distribution path planning solution for the logistics industry