An Improved Ant Colony Optimization for Flexible Job Shop Scheduling Problems

An improved ant colony optimization (IACO) algorithm is proposed to the flexible job shop scheduling problem (FJSSP) in this paper. IACO algorithm provides an effective integration between ant colony optimization (ACO) model and knowledge model. In the IACO algorithm, knowledge model learns some available knowledge from the optimization of ACO, and then employs the existing knowledge to guide the current heuristic searching. The performance of IACO was evaluated by many benchmark instances taken from literature. Final experimental results indicate that the proposed IACO algorithm outperforms some current approaches in the quality of schedules.