Cloud-based Adaptive Quantum Genetic Algorithm for Solving Flexible Job Shop Scheduling Problem

The quantum genetic algorithm is improved for the flexible shop scheduling problem. In this paper, a quantum genetic algorithm adaptively adjusting the rotation angle is designed to solve the flexible shop scheduling problem. The randomness and stability tendency of the cloud model are used to adaptively adjust the rotation angle to improve the optimization ability of the algorithm. At the same time, quantum crossover and quantum variability are used to improve population diversity and prevent premature convergence. Numerical experiments show that the algorithm has good performance.