Improved Q-learning Bee Colony Algorithm to Solve the Scheduling Problem of the Permutation Flow Shop
For the scheduling problem in permutation flow shop,an artificial bee colony algorithm based on an improved Q-learning algorithm is proposed.This algorithm designs an improved reward function as the environment for the artificial bee colony algorithm.The quality of the reward function is used to determine the optimization strategy for the next generation population.Through Q-learning,intelligent selection of the dimensionality size for updating the artificial bee colony algorithm's food sources is achieved.The selected dimensionality size is used to update the encoding,thereby improving the convergence speed and accuracy.Finally,instances of permutation flow shop scheduling problems of different scales are used to validate the performance of the proposed algorithm.Through computation on standard instances and comparison with other algorithms,the accuracy of the algorithm is demonstrated.
万方数据
维普
