Vehicle Path Planning Based on Gradient Statistical Mutation Quantum Genetic Algorithm

In the field of vehicle path planning, traditional intelligent optimization algorithms have the disadvantages of slow convergence, poor stability and a tendency to fall into local extremes. Therefore, a gradient statistical mutation quantum genetic algorithm (GSM-QGA) is proposed. Based on the dynamic rotation angle adjustment by the chromosome fitness value, the quantum rotation gate adjustment strategy is improved by introducing the idea of gradient descent. According to the statistical properties of chromosomal change trends, the gradient-based mutation operator is designed to realize the mutation operation. The shortest path is used as the metric to build the vehicle path planning model, and the effectiveness of the modified algorithm in vehicle path planning is demonstrated by simulation experiments. Compared with other optimization algorithms, the path length planned by the improved algorithm is shorter and the search stability is better. The algorithm can be effectively controlled to fall into local optimums.

@article{Li2023,
  title     = {Vehicle Path Planning Based on Gradient Statistical Mutation Quantum Genetic Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {6},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Hui Li and Huiping Qin and Zi’ao Han and Kai Lu},
  doi       = {10.14569/IJACSA.2023.0140664},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140664}
}