关键词: 蚁群算法, 动态平衡, 自适应, 作业车间调度

Abstract: Aiming at the defect that ant colony optimization easily fell into local optimal solution and had long search time,a Dynamic Balance Adaptive Colony Algorithm(DB-ACA)was proposed to solve Job-Shop scheduling problem.An adaptive adjustment strategy of volatility coefficient was introduced to overcome premature convergence,which adjust the evaporation coefficient proactively according to the tendencies of the intermediate solution towards to a local optimum.A dynamic equilibrium mechanism was also put forward to improve the global search capability and the search speed of the algorithm,which changed the distribution of the solution dynamically according to the concentration of solutions distribution when the convergence coefficient was greater than the set threshold.100 simulation tests on the classic benchmark scheduling problems were run separately,and compared with other four typical algorithms from literatures.The simulation results showed that the proposed algorithm had better performance than others in the convergence speed,the solution quality and the solution stability.

Key words: ant colony optimal algorithms, dynamic balance, self-adaption, Job-Shop scheduling