Research on Hybrid Flow-shop Scheduling Problem based on improved immune particle swarm optimization

The Hybrid Flow-shop Scheduling Problem (HFSP) is a typical NP-hard problem, the target is to minimize maximum flow time. To solve the problem that the traditional particle swarm optimization algorithm has slow convergence rate and is easy to trap into local optimum, we initially brought forward a method based on improved immune particle swarm optimization algorithm with dynamic disturbance term (IPSO-DDT). The algorithm´s particle coding reference of genetic algorithm matrix coding, changes the speed formula fundamentally and introduces the immune information processing mechanism to this algorithm, both of which combine closely, by keeping the diversity of the individual to avoid premature convergence and improve convergence speed. In the end, the simulation results show that the IPSO-DDT algorithm has good performance in Hybrid Flow-shop Scheduling Problem.