摘要
工作车间调度问题是一个经典优化问题,已经在许多工业生产中得到研究。随着问题复杂度的提升,问题越来越呈现出非线性化的特点。现有工作往往对决策空间的多样性缺少考虑,导致算法极容易陷入局部最优解。该文提出了一种粒子群和蚁群的混合算法,该算法结合了蚁群算法局部搜索和粒子群的全局搜索能力,可以大大加快收敛速度并帮助算法跳出局部最优解。通过与其他代表性算法进行对比,该算法收敛性和种群多样性的保持能力较强,在柔性工作车间的调度问题中表现良好。
For job⁃shop scheduling or job⁃shop problems,existing work often lacks consideration of the diversity of decision spaces,which makes it easy for algorithms to fall into local optimal solutions.This paper proposes a hybrid algorithm of particle swarm and ant colony,which combines the local search and global search capabilities of the ant colony algorithm,which can greatly speed up the convergence speed and help the algorithm jump out of the local optimal solution.Compared with other representative algorithms,the algorithm has strong ability to maintain convergence and population diversity,and performs well in the flexible job⁃shop scheduling problem.
作者
王昱钦
WANG Yuqin(Department of Information Technology,Jiangsu Automation Research Institude,Lianyungang 222006,China)
出处
《电子设计工程》
2023年第17期65-69,共5页
Electronic Design Engineering
关键词
柔性作业车间问题
进化算法
蚁群算法
粒子群算法
flexible job⁃shop problem
evolutionary algorithm
ant colony algorithm
particle swarm algorithm
