摘要
改进了粒子群优化算法,并将其应用到柔性流水车间调度问题中。首先,算法将禁忌搜索和粒子群优化算法相结合,提高算法的收敛速度;其次,初始种群采用NEH启发式算法产生,该方法能够使粒子更具多样性;最后,为了增强算法的全局寻优能力,自适应惯性权重采用非线性自适应的更新方式;此外,将扰动因子引入速度更新公式,避免算法陷入局部最优解。将改进前后的粒子群算法在车间调度基准数据集上进行了实验对比,仿真结果验证了该算法的有效性。
An improved particle swarm optimization algorithm is proposed to solve the flexible flow shop scheduling problem.Firstly,the algorithm combines tabu search and particle swarm optimization algorithm to improve the convergence speed of the algorithm;secondly,the initial population is generated by the NEH heuristic algorithm,which can make the particles more diverse;finally,in order to enhance the global optimization of the algorithm.The adaptive inertia weight adopts a nonlinear adaptive update method;in addition,a disturbance factor is introduced into the velocity update formula to avoid the algorithm falling into a local optimal solution.The particle swarm algorithm before and after the improvement is compared on the workshop scheduling benchmark data set,and the simulation results verify the effectiveness of the algorithm.
作者
范雅男
逄焕利
FAN Yanan;PANG Huanli(School of Computer Science&Engineering,Changchun University of Technology,Changchun 130102,China)
出处
《长春工业大学学报》
CAS
2022年第3期258-264,共7页
Journal of Changchun University of Technology
基金
吉林省科技厅重点科技研发基金项目资助(20180201129GX)。
关键词
粒子群算法
柔性流水车间调度
禁忌搜索
自适应权重
particle swarm optimization algorithm
flexible flow shop scheduling
tabu search
adaptive weight
作者简介
范雅男(1997-),女,汉族,吉林白山人,长春工业大学硕士研究生,主要从事人工智能及其应用方向研究,E-mail:1278811657@qq.com;通信作者:逄焕利(1970-),男,汉族,吉林白山人,长春工业大学副教授,硕士,主要从事数据挖掘和人工智能方向研究,E-mail:panghuanli@ccut.edu.cn.
