Parallel machine scheduling optimisation based on an improved multi-objective artificial bee colony algorithm

Aiming at the scheduling model of the same kind of machine, considering that low carbon emission is an urgent problem to be solved in the manufacturing industry, a mathematical model containing the maximum completion time and maximum processing energy consumption was established. In order to balance the local development ability and global search ability of an artificial bee colony algorithm, and improve the convergence speed of the algorithm, a scheduling optimisation method of parallel machine based on improved multi-objective ABC algorithm was proposed. Firstly, a chaotic image initialisation method is proposed to ensure the diversity and excellence of the initial population. Then, the individual threshold is used to dynamically adjust the search radius to improve the search accuracy and convergence speed. Finally, considering the development times of the external archive solution, the evolution is guided by selecting the elite solution reasonably. In order to verify the effectiveness of the algorithm, comparative experiments and performance analysis of the algorithm are carried out on several examples. The results show that the proposed algorithm can solve the scheduling problem of the same kind of machine effectively in practical scenarios.