Abstract
In order to exert the advantage of ant colony algorithm and particle swarm optimization algorithm respectively, a method combined the two algorithms was designed for solving multi-objective flexible job shop scheduling problem in this paper. The proposed algorithm was composed by two phases. The first phase made use of the fast convergence of PSO to search the particles optimum position and made the position as the start point of ants. In the second phase, the traditional ant colony algorithm was improved and was used to search the global optimum scheduling according to its characters of positive feedback and structure of solution set. The combined algorithm was validated by practical instances. The results obtained have shown the proposed approach is feasible and effective for the multi-objective flexible job shop scheduling problem.
