q-Rung orthopair fuzzy decision-making framework for integrating mobile edge caching scheme preferences
q-Rung orthopair fuzzy decision-making framework for integrating mobile edge caching scheme preferences
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DOI：
10.1002/int.22377
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发表时间：
2021-02-05
影响因子：
7
通讯作者：
Luo, Zhigang
中科院分区：
文献类型：
--
作者：
Peng, Xindong;Huang, Haihui;Luo, Zhigang
移动边缘缓存方案(MECS)可以通过使用自己的存储来确定在用户设备上缓存的位置、方式和内容。在考虑MECS的表现时，往往充满了不确定性。Q-Rung正交模糊集(Q-ROFS)以隶属度和非隶属度为特征，参数q可调，是获取不确定性的一种非常高效的方法。本文首先研究了q阶正交模糊(Q-ROF)环境下基于信息度量(信息熵、距离度量和相似度量)的区域差异，并给出了详细的证明。在此基础上，提出了一种综合赋权方法，将客观赋权(用熵确定)和主观赋权(专家赋权)相结合，有效地缓解了极端数据对评价结果的不合理影响，同时反映了客观数据和主观情感。此外，还提出了一种基于Q-ROF得分函数的距离度量方法来处理一个值比较问题。然后介绍了Q-ROF多准则决策方法--基于正交向量的总面积决策方法(TAOV)。并以MECS选择问题为例说明了该算法的可行性。最后，将已有的MCDM方法与所提出的方法进行了比较，以显示其有效性。该方法可以有效地避免反直觉现象，以负零问题消除逆对数，且不存在零除问题。
Mobile edge caching scheme (MECS) can determine where, how, and what to cache on user equipment by employing its own storage. When considering the performance of MECS, it is often full of uncertainty. The q-rung orthopair fuzzy set (q-ROFS), characterized by membership and nonmembership degrees with adjustable parameter q, is quite a high-efficiency way to capture uncertainty. In this paper, first, information measure (entropy, distance measure, and similarity measure)-based area difference under the q-rung orthopair fuzzy (q-ROF) circumstance is studied along with their detailed proofs. Then, we present a comprehensive weight-determination method by combining objective weights (determining by entropy) and subjective weights (given by experts) as combined weights, which can effectually alleviate the unconscionable influence of extreme data on evaluation results and simultaneously reflect objective data and subjective emotion. Moreover, q-ROF score function-based distance measure is presented for dealing with a value comparison problem. Later, q-ROF multicriteria decision-making (MCDM) method called total area based on orthogonal vector (TAOV) is introduced. Moreover, its feasibility is illustrated by MECS selection problem. Finally, a comparison of some existing MCDM methods and the proposed method is constructed for displaying their effectiveness. This proposed method can effectively avoid counterintuitive phenomena, eliminate antilogarithm by negative and zero issue, and has no division by zero issue.