An Improved Ant Colony Algorithm Combined with Particle Swarm Optimization Algorithm for Multi-objective Flexible Job Shop Scheduling Problem

In this paper an improved ant colony algorithm is presented and an algorithm in combination with particle swarm optimization algorithm and the improved ant colony algorithm for multi-objective flexible job shop scheduling problem are employed. The algorithm proposed in this paper includes two parts. The first part makes use of the fast convergence of PSO to search the particles optimum position and make it as the start position of ants. The second part makes use of the merit of positive feedback and structure of solution set proposed by our improved ACA to search the global optimum scheduling. The algorithm we presented is 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.