A Two Stage Hybrid Particle Swarm Optimization Algorithm for Job-Shop Scheduling Problem

A two-stage hybrid particle swarm optimization (TS-HPSO) algorithm is proposed to solve the job-shop scheduling problem. In the first phase, the inertia coefficient w is set bigger and the ability of social learning of particles is removed so that each particle can search the local area fully. In the second phase, the initial particles are initialized according to the best position of each particle searched in the first phase, and at the same time, the mutation operation of genetic algorithm is used to ensure the diversity of particles. A neighborhood based random greedy search is performed on the best particle g_mbest to ensure the optimization of the algorithm. The computational results show the effectiveness of the algorithm.