Research into Self-Adaptive Hybrid Ant Colony Algorithm Based on Flow Control

A hybrid ant colony algorithm with self-adaptive parameters has been researched in this paper. Two schemes of adjusting parameters have been put forward according to the simulation analysis on different affect of different parameter sets of TACOSA algorithm when solving the dual resource constrained job shop scheduling problem with heterogeneous workers which based on decreasing production cost. Based on the analysis of performances of both schemes, a self-adaptive routing choice mechanism based on ant flow control has been introduced to improve the global search ability and convergence performance. According to the comparing experiments of different algorithms, the advantage of the hybrid ant colony algorithm and the optimized capability of the control mechanism based on ant flow have been validated.