Multi-objective flexible job shop schedule based on ant colony algorithm

In this paper, an improved ant colony algorithm is proposed to solve solving multi-objective flexible shop scheduling problem. Limitations of the traditional ant colony algorithm weighting coefficient method will result in a greater impact on the results because the determination of the weighting factor has greater subjective factors. Proposed algorithm adds a set of BPs to save all the Pareto set ant appear after iteration, the algorithm improves the search capabilities of the ant colony. The convergence speed is improved on ameliorating the pheromone update rule based on the global optimal experience to guide the optimization way. Thus, multi-objective Flexible Job Shop Scheduling Problems Pareto optimal solution was conducted. Finally, the proposed theory in this paper is proved to solve the multi-objective flexible job shop scheduling optimization problems by examples.