基于思维进化的蚁群算法在典型生产调度中的应用

摘要

Aiming at solving the NP-hard workshop production scheduling problems,this paper proposed a ant colony algorithm based on mind evolution.The algorithm is established in the traditional ant colony algorithm,and the combination of evolutionary thought and local optimization idea overcomes defects that the basic ant colony algorithm is easy to fall into local optimization,improves state transition rules,defines a pheromone range,improves the pheromone update strategy,and increases neighborhood search.Experimental results show that,for a typical production scheduling problems,ant colony algorithm based on mind evolutionary can obtain the optimal solution in theory,and optimal solution,the solution and average three indicators are better than the basic ant colony algorithm,shows good performance.%针对求解NP-hard的车间生产调度存在的问题,提出了一种基于思维进化的蚁群算法.该算法建立在传统蚁群算法上,并结合思维进化思想和局部寻优思想克服了基本蚁群算法易陷入局部最优的缺陷,改进了状态转移规则,限定了信息素的范围,改进了信息素更新策略,并增加了邻域搜索.实验表明,对于典型生产调度问题,基于思维进化的蚁群算法可以求得理论最优解,在最优解、最差解和平均解3个指标上都优于基本蚁群算法,体现出了较好的性能.