计算机技术与发展2018，Vol.28Issue(1)：127-130,4.DOI:10.3969/j.issn.1673-629X.2018.01.027
基于动态参数蚁群算法的云制造服务组合
Cloud Manufacturing Service Composition Based on Dynamic Parameters Ant Colony Algorithm
摘要
Abstract
In order to make the cloud manufacturing resources more effectively allocated to each manufacturing task,a Dynamic Parameter Ant Colony Optimization ( DPACO) is proposed. It is on the basis of QoS ( Quality of Service) evaluation model which gets a fitness function F by combining cost,time,quality function and satisfaction. The smaller the F,the better the result. DPACO can accelerate its convergence by changing the parameters in different stages and better jump out of local optimal solution for global optimal solution by adding a special ant algorithm. Finally,DPACO is compared with ACO,PSO and DE through cloud manufacturing resources optimization of steel forging task. The experiment shows that DPACO can be able to obtain the global optimal solution in solving the questions of cloud manufacturing service portfolio with higher convergence.关键词
云制造服务组合/动态参数蚁群算法/QoS评估模型/适应度函数Key words
cloud manufacturing service composition/DPACO/QoE evaluation model/fitness function分类
信息技术与安全科学引用本文复制引用
张严凯,周井泉,李强..基于动态参数蚁群算法的云制造服务组合[J].计算机技术与发展,2018,28(1):127-130,4.基金项目
国家自然科学基金资助项目(61401225) （61401225）
中国博士后科学基金资助项目(2015M571790) （2015M571790）
