基于量子蚁群算法的智能制造调度问题研究
Research on Intelligent Manufacturing Scheduling Problem Based on Quantum Ant Colony Algorithm
吴昌钱 1黄锐 2罗志伟3
作者信息
- 1. 闽南科技学院计算机信息学院,福建泉州 366200
- 2. 北京理工大学计算机学院,北京 100081
- 3. 厦门大学机电学院,福建厦门 361000
- 折叠
摘要
近年来,工业互联网技术逐渐得到普及,复杂构件生产车间的制造环境逐渐复杂化,提出一种基于量子蚁群算法的智能制造调度方案(QACA-AMJSP).首先,结合智能制造车间的特点,构建了相应的车间调度数学模型.然后,将量子计算与模拟自然界蚁群行为的蚁群算法相结合求解智能制造车间调度问题,利用量子比特表示信息素并以量子旋转门更新,保留了量子计算的高效性,提高了蚁群全局寻优能力,避免了蚂蚁易陷局部最优解问题.实验结果表明,相比粒子群优化算法和遗传算法,量子蚁群算法对解决智能制造车间调度问题具有较高的搜索效率和较快的收敛速度.
Abstract
In recent years,the industrial Internet technology has been gradually popularized,and the manufacturing environment of complex component production workshop has gradually become complicated.This paper proposes an intelligent manufacturing scheduling scheme based on quantum ant colony algorithm(QACA-AMJSP).Firstly,according to the characteristics of aviation manufacturing workshop,the corresponding workshop scheduling mathematical model is constructed.Then,quantum computing and ant colony algorithm,which simulates the behavior of ant colony in nature,are combined to solve the scheduling problem of aviation manufacturing workshop.Quantum bits are used to represent pheromones and are updated by quantum revolving doors,which keeps the efficiency of quantum computing,improves the global optimization ability of ant colony,and avoids the problem that ants are easily trapped in local optimal solutions.The experimental results show that,compared with particle swarm optimization algorithm and genetic algorithm,quantum ant colony algorithm has higher search efficiency and faster convergence speed for solving the aviation manufacturing workshop scheduling problem.
关键词
车间调度/智能制造/量子计算/蚁群算法/全局搜索Key words
workshop scheduling/smart manufacturing/quantum computing/ant colony algorithm/global search引用本文复制引用
基金项目
国家自然科学基金面上项目(61871204)
福建省自然科学基金面上项目(2019J01863)
福建省教育科学"十三五"规划项目(FJJKCG20-014)
福建省本科教育教学改革研究项目(FBJG20200327)
新工科重点建设项目(MKXGK-2021-02)
出版年
2023
国家科技期刊平台
NSTL
万方数据