摘要
蚁群算法是一种仿真蚂蚁群体智能行为的仿生优化算法,具有良好的正反馈、鲁棒性、群体性和并行性等特点。针对基本蚁群算法易陷入局部收敛这一缺点,为提高精确高度,提出了一种改进蚁群算法,采用了新的状态转移规则,当算法陷入局部收敛时调整信息素更新策略,并根据陷入局部收敛的程度动态调整信息素挥发系数和信息素强度,使算法能快速跳出局部收敛得到全局最优解;仿真结果验证了改进蚁群算法求解作业车间调度问题的有效性。
Ant colony algorithm has the characteristics of good positive feedback, robustness and parallel groups, and is an optimized algorithm for simulating ants＇ swarm intelligence behavior. This paper proposed an advanced ant colony algorithm for overcomeing the defect that the basic ant colony algorithm is easy to fall into the local convergence. This proposed method adopted a new state transition rule. The pheromone update strategy would be adjusted when the algorithm falls into the local convergence. And the coefficient and intensity of pheromone volatile would be adjusted dynamically according to the degree of local convergence. This algorithm can quickly jump out of the local convergence and obtain a global optimal solution. The simulation demonstrates the validity of the proposed algorithm for Job Shop Scheduling problem.
出处
《计算机仿真》
CSCD
北大核心
2009年第8期278-282,共5页
Computer Simulation
关键词
作业车间调度
改进蚁群算法
状态转移规则
信息素更新策略
Job- shop scheduling
Improved ant colony optimization algorithm
State transition rule
Pheromone update strategy
