摘要
基本蚁群算法中信息挥发系数的存在,导致那些从未被搜索过的路径上的信息素逐渐消失,被选择的概率降低,易陷入局部最优。MMAS模型中在进行信息素更新时采取本次迭代最优解的策略,在获取信息素边的数目增加的同时减少了搜索的导向性。如果只使用至今最优解来进行信息素更新易于陷入局部最优的困境。在MMAS基础上,通过采取在迭代过程中信息素的自适应调整策略提高了解的质量,实现了对MMSA算法的改进。通过数值仿真实验证明通过改进MMAS增强了算法的性能。利用改进MMAS对某露天矿运输系统网络进行路径优化,取得较好效果。
The basic ant colony algorithm for the existence of information in volatile factor,leading to the pheromone on the path those which have never been searched gradual disappearance,reduce the probability of being selected,easy to fall into local opti-mum. MMAS pheromone updated during the model to take this time iteration the optimal solution strategy,access to information while increasing the number of elements while reducing the search-oriented. If you only use the optimal solution has to be easily caught pheromone update into the plight of local optimum. On the basis of the MMAS,by taking in the iteration process pheromone adaptive strategies to improve the quality of solution,realize MMSA algorithm improvements. The numerical simulation results show that the performance of the algorithm enhanced by MMAS improvement. Using improved MMAS Optimized an open-pit transport system in the network path,achieved better results.
出处
《微计算机信息》
2010年第36期220-221,208,共3页
Control & Automation
关键词
蚁群算法
MMAS
迭代
路径优化
ant colony algorithm
MMAS
iteration
path optimization
