New hybrid particle swarm optimization algorithm for permutation Flow-Shop scheduling problem
一种新的混合粒子群算法求解置换流水车间调度问题

For the problem that the PSO is easy to be trapped in local optimal, this paper put forward a hybrid PSO algorithm which combined the IG algorithm. The algorithm judged the particles' status by the change of particles' individual and global best value in continuous generations, and used destruction and construction operation of IG algorithm to mutate the relating particle and the global best position after discovering that the particle was at a standstill or the particle swarm was trapped in local optimal. The new particles being mutated were accepted according to the simulated annealing theory. The mutation of global best particle could guide the particle swarm to escape from the local best value's limit and increase the diversity of particles, which avoided the particle's premature stagnation. Simultaneously, the algorithm adopted cycle iterative method in order to get or approach the best result quickly. It searched the best solution step by step on the basis of stage optimization. The paper applied the hybrid PSO algorithm to the permutation Flow-Shop scheduling problem, and compared it with the other representative algorithm. The result shows that the hybrid PSO algorithm can avoid the particle's premature stagnation effectively and the algorithm is better than other algorithms in the quality of searching the best solution.