摘要
以总流程时间和最大完工时间的多目标流水车间调度问题在自动化、工业生产等领域具有广泛应用为背景,提出一种基于差分进化与混合采样策略的混合多目标进化算法HMODE(Hybrid Multiobjective Evolutionary Algorithm based on Differential Evolution)来求解此类问题。为提升算法收敛性与分布性,引入新的混合采样方式。区别于传统序列编码方式,采用实数编码,并设计了差分进化的变异、目标个体排序与更新操作。通过Taillard标准测试算例的计算试验,验证了HMODE算法求解多目标流水车间调度问题的有效性。
The multi-objective flow shop scheduling problem with total process time and maximum completion time has a wide application background in automation,industrial production and other fields. A hybrid multi-objective evolutionary algorithm based on differential evolution（ HMODE） algorithm based on differential evolution and mixed sampling strategy was proposed to solve such problems. In order to improve the convergence and distribution of the algorithm,a new mixed sampling method was introduced. Different from the traditional sequence encoding method,we used real-coded and designed the differential evolution mutation, target individual sorting and updating operations. The calculation experiments of the Taillard standard test example verify the effectiveness of the HMODE algorithm in solving multiobjective flow shop scheduling problems.
作者
王宇
张闻强
Wang Yu;Zhang Wenqiang(College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, Henan, China)
出处
《计算机应用与软件》
北大核心
2018年第6期174-180,共7页
Computer Applications and Software
基金
国家自然科学基金项目(U1304609)
河南省科技攻关基金项目(162102210044
152102210068
152102110076)
河南省高校科技创新团队(科技攻关项目)(17IRTSTHN011)
河南省教育厅科技重大项目计划(17A520030)
河南省高校基础研究基金项目(2014YWQQ12
2015XTCX03
2015XTCX04)
河南工业大学粮食信息加工与控制重点实验室研究基金项目(KFJJ-2015-106)
关键词
多目标优化
进化计算
混合采样
差分进化
流水车间调度
Multi-objective optimization
Evolutiona^y calculation
Mixed sampling
Differential evolution
Flow shop scheduling
