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论文:2012,Vol:30,Issue(6):911-918 |
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引用本文： |
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王润孝, 杨云涛, 李俊亭. 自适应模糊粒子群算法求解IT服务优化选择问题[J]. 西北工业大学 |
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Wang Runxiao, Yang Yuntao, Li Junting. An Effective AFPSO(Adaptive Fuzzy Particle Swarm Optimization) Algorithm for Obtaining Optimal Selection of IT Services[J]. Northwestern polytechnical university |
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| 自适应模糊粒子群算法求解IT服务优化选择问题 |
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| 王润孝1, 杨云涛1,2, 李俊亭1,3 |
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1. 西北工业大学 机电学院, 陕西西安 710072;
2. 陕西省地方电力(集团)有限公司 配电网研究中心, 陕西 西安 710061;
3. 西安石油大学 经济管理学院, 陕西 西安 710065 |
| 摘要: |
| 为了解决IT服务选择问题,提出了一种基于自适应模糊粒子群算法的IT服务优化选择方法。首先,针对业务流程对IT服务的需求,建立了以响应时间、执行费用、可靠性、可用度为目标的IT服务优化选择模型。然后,设计了求解模型的粒子群优化算法,应用模糊推理规则,自适应调整粒子进化过程中的自身学习因子和全局学习因子,以提高粒子收敛速度和全局搜索能力,从而获取了满足业务流程QoS约束的较优IT服务单元。最后以算例验证了模型及算法的可行性和有效性。 |
| 关键词:
IT服务选择
粒子群优化
学习因子
服务质量
隶属函数
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| An Effective AFPSO(Adaptive Fuzzy Particle Swarm Optimization) Algorithm for Obtaining Optimal Selection of IT Services |
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| Wang Runxiao1, Yang Yuntao1,2, Li Junting1,3 |
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1. Department of Computer Aided Manufacturing, Northwestern Polytechnical University, Xi'an 710072, China;
2. Distribution Network Research Center, Shaanxi Regional Electric Power Group Co., Ltd.Xi'an 710061, China;
3. School of Economics and Management, Xi'an Shiyou University, Xi'an 710065, China |
| Abstract: |
| Sections 1 and 2 of the full paper explain our AFPSO algorithm mantioned in the title,which we believeis effective. The core of sections 1 and 2 consists of: (1) according to the requirements of business processes in ITservices,an optimal selection model of IT services was established; response time,execution cost,reliability andavailability were set as its objective functions; (2) an optimization algorithm to solve the model was designed; theself learning factor and global learning factor were adaptively tuned by the fuzzy inference rules so as to improve theconvergence speed and global searching ability; (3) the optimal IT service unit which satisfied the QoS constrai-ning condition of business process was obtained. Section 3 gives an application example; test results,presented inTable 5,and their analysis confirm preliminarily indeed the effectiveness of the proposed model and our AFPSO al-gorithm. |
| Key words:
adaptive algorithms
convergence of numerical methods
decision making
experiments
flowcharting
information technology
membership functions
particle swarm optimization(PSO)
quality of service
reliability
resouece allocation;information processing
learning factor
selection of IT services
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| 收稿日期: 2011-10-18
修回日期:
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| DOI: |
| 基金项目: 陕西省科学技术研究发展计划(2009K08-21)资助 |
| 通讯作者:
Email： |
| 作者简介: 王润孝(1957-)，西北工业大学教授、博士生导师，主要从事管理科学与工程及机械电子工程的研究。
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