基于新颖蚁群算法的加工中心组成问题研究

针对蚁群算法求解加工中心组成问题易陷入早熟收敛状态的缺点,提出了将听觉信号、记忆矩阵与蚁群算法相融合的一种新颖蚁群算法。在仿真实验中,分别采用蚁群算法、加入听觉信号的蚁群算法、加入记忆矩阵的蚁群算法和新颖蚁群算法对加工中心组成问题进行求解。实验结果表明,新颖蚁群算法能够有效提高蚁群算法的全局寻优能力,收敛速度快,且所求得的组功效优于以上三个策略及以往的混合遗传算法。

A study of the cell formation problems based on a novel ant colony algorithm

The ant colony algorithm for solving cell formation problems tends to fall into early mature convergence status. In order to overcome this defect, we propose a mixed algorithm of the ant colony algorithm, the auditory signal and the memory matrix. In the simulation experiments, the ant colony algorithm, the ant colony algorithm containing auditory signals, the ant colony algorithm containing memory matrixes, and the proposed novel algorithm are adopted respectively to solve cell formation problems. Experimental results show that the proposed algorithm outperforms the other three in terms of enhancing global optimization capacity and convergence speed of the ant colony algorithm. At the same time, the group efficiency obtained by the proposed algorithm is better than the three aforementioned algorithms and the existing hybrid genetic algorithms.