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为克服蚁群算法存在收敛速度慢、容易陷入局部最优解的问题,通过研究记忆曲线模型和蚁群算法信息素更新规则的特点,提出了一种基于生物记忆曲线模型的信息素更新规则对蚁群算法进行改进,并通过实验确定改进后的蚁群算法各参数的合理取值。以最短加工时间为目标函数,建立柔性作业车间调度的目标函数,结合实际算例借助MATLAB求解。通过与其他改进蚁群算法的对比,对6个Job-Shop Benchmark的基准问题进行仿真,通过仿真结果发现,无论是最优解的质量还是求解速度上改进的蚁群算法较基本蚁群算法都有较大提升。最终得出本文提出的基于生物记忆曲线模型的信息素更新规则具有良好的求解能力和收敛能力。
In order to overcome the problem that the ant colony algorithm is slow to converge and fall into the local optimal solution easily, this paper proposes a pheromone updating rule based on the biomemory curve model through studying the memory curve model and the characteristics of the ant colony algorithm pheromone updating rule Ant colony algorithm to improve, and through experiments to determine the reasonable value of the improved ant colony algorithm parameters. Taking the shortest processing time as the objective function, the objective function of flexible job shop scheduling is established, and solved with MATLAB according to the actual example. Compared with other improved ant colony algorithm, the benchmark of six Job-Shop Benchmark is simulated. The simulation results show that both the quality of the optimal solution and the improved ant colony algorithm are better than the basic ant colony algorithm There is a big improvement. Finally, the pheromone update rule based on the biomemory curve model proposed in this paper has good solver ability and convergence ability.