摘要
针对联盟决策评价中存在较强的不确定性,提出基于云模型理论面向大数据的协作联盟决策评价方法.首先,构建面向大数据的多任务协作联盟多层决策评价架构,依托大数据处理分析平台获取联盟成员的基本评价指标的评价数据,应用逆向云发生器算法生成相应的评价云,并运用综合云运算产生联盟评价指标的云数字特征.然后,结合联盟评价指标权重和任务权重,运用云加权算术平均数算子进行云集结,分别产生单任务联盟决策评价云和多任务协作联盟决策评价云.再对多任务协作联盟备选方案进行决策评价和选优,以确定最优的联盟方案.最后通过实例与D-S证据理论联盟评价方法进行对比,验证文中方法的有效性.
Aiming at the strong uncertainty of decision-making evaluation for alliance,a multi-level decision-making evaluation method of multi-task collaborative alliance based on cloud model theory orienting to big data is proposed.Firstly,a decision-making evaluation framework for collaborative alliance on the basis of big data is established,and the evaluation data of basic evaluation indexes of alliance members are obtained from the processing and analysis platform of big data.The reverse cloud generator algorithm is applied to create the corresponding evaluation cloud.Meanwhile,the cloud characteristic parameters of alliance evaluation indexes are generated using integrated cloud computing.Then,combining the evaluation index weight and task weight of alliance,the decision-making evaluation cloud of single-task alliance and multi-task collaborative alliance are gained by applying cloud weighted arithmetic averaging operator on the basis of cloud clustering algorithm,respectively.Next,the alternative schemes of multi-task collaborative alliance are evaluated and selected to determine the optimal one.Finally,by comparing with the traditional alliance evaluation method based on the D-S evidence theory,the effectiveness of the proposed method is verified.
作者
尹蕾
蒋建国
张国富
YIN Lei;JIANG Jianguo;ZHANG Guofu(School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230601)
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2019年第2期124-132,共9页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61174170
61573125)资助~~
