摘要
蚁群算法求解流水车间调度问题(FSP)容易陷入局部最优,为避免误差较大,提出一种改进的蚁群算法(IAACA).该算法融合最大最小蚂蚁系统的思想,改进了蚂蚁信息素挥发方式,在搜索初期,信息素挥发系数从较大的值呈线性递减趋势,利于算法跳出局部最优,在迭代后期,信息素挥发系数减小为较小的值,有利于精细寻优.对基准算例的仿真结果表明改进的蚁群算法的有效性.
An improved adaptive ant colony algorithm （IAACA） model,which aim at solving slow convergence speed and local optimal of flow shop scheduling problem （FSP）,is proposed to improve the efficiency of flow shop scheduling.The new adaptive ant colony algorithm,which introduces a new adaptive system to search quickly at the early iteration stage and to search accurately at the late stage,finds the solution to the problems of slow constringency,and the problems of searching speed and accuracy in the traditional ant colony algorithm.The simulation based on standard benchmark FSP examples indicates that the new adaptive ant colony algorithm with fast constringency and high precision and good scheduling efficiency can find out the optimal solutions or satisfactory solutions.The new algorithm is proved to be robust and effective.
出处
《华中师范大学学报（自然科学版）》
CAS
北大核心
2014年第3期330-334,共5页
Journal of Central China Normal University：Natural Sciences
基金
国家自然科学基金项目(61262027)
广西高等学校科研项目(201203YB161)
关键词
流水车间调度
蚁群算法
最大最小蚂蚁系统
收敛速度
优化解
最优相对误差
flow shop scheduling problem
ant colony algorithm
max min ant system
rate of convergence
the optimal solution
the best relative error
