面向群体共识的三阶段犹豫模糊型信息融合方法研究

doi:10.12305/j.issn.1001-506X.2021.12.23

摘要/Abstract

摘要：

个体一致性检验、群体偏好集结、群体共识改进是达成共识的三个关键阶段, 高一致性、高共识度、低共识成本分别是群体共识构建三个阶段的重要决策目标。本文通过分析群体共识构建过程中三个核心阶段对共识实现的影响, 建立了各个决策阶段的优先模型。首先, 针对犹豫模糊偏好关系的评价值特征, 设计了融合局部标准化系数的一致性检验和调整模型, 为实施群体信息集结提供可靠的模糊判断; 其次, 以贴近度为诱导值实施犹豫模糊偏好关系群体信息集结, 结合统计推断方法设计共识度阈值, 科学刻画群体共识水平; 然后, 针对群体共识度水平较低的情形, 设计了融合优化集结权重和最小调整距离的共识改进模型, 有效减少群体共识改进中的信息损失与迭代次数。最后, 通过比较分析凸显所提决策方法的可行性和优越性。

关键词: 群体共识, 犹豫模糊偏好关系, 一致性, 偏好调整

Abstract:

Individual consistency check, group preference aggregation and group consensus improvement are three key stages of group consensus reaching process (CRP). High degree of consistency and consensus and low consensus cost are important decision-making goals in the three stages of group CRP. By analysing the relation influence of the three stages of group consensus building process on consensus, we construct optimization models for each decision stage. Firstly, considering the evaluation value characteristics of hesitation fuzzy preference relation (HFPR), we design the consistency check and improvement model with local standardization coefficient to provide reliable fuzzy judgment for group information aggregation. Subsequently, the group information aggregation of hesitating fuzzy preference relationship was implemented with proximity degree as induction value, and the consensus threshold is determined by statistical inference, which depicts group consensus level objectively. And then, when the level of group consensus is low, a consensus improvement model containing optimizing aggregation weight and minimum adjustment distance is designed, which effectively reduces the informationloss and iteration. Finally, a comparison with the existing approach is carried out to show the effectiveness and efficiency of the proposed method.

Key words: group consensus, hesitant fuzzy preference relation, consistency, preference adjustment

中图分类号:

C934

陶希闻, 江文奇. 面向群体共识的三阶段犹豫模糊型信息融合方法研究[J]. 系统工程与电子技术, 2021, 43(12): 3603-3613.

Xiwen TAO, Wenqi JIANG. Research on three-stage hesitant fuzzy information fusion method for group consensus[J]. Systems Engineering and Electronics, 2021, 43(12): 3603-3613.

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专家	h^q₁₂	h^q₁₃	h^q₁₄
e₁	{0.3, 0.4}	{0.5, 0.7}	{0.4}
e₂	{0.3, 0.5}	{0.1, 0.2}	{0.5, 0.6, 0.7}
e₃	{0.3, 0.5}	{0.5, 0.7}	{0.7, 0.8}
e₄	{0.4, 0.5, 0.6}	{0.3, 0.4}	{0.5, 0.7}

专家	h^q₂₃	h^q₂₄	h^q₃₄
e₁	{0.7, 0.9}	{0.8, 0.9, 1.0}	{0.6, 0.7}
e₂	{0.7, 0.8}	{0.1, 0.5}	{0.5, 0.7}
e₃	{0.2, 0.4}	{0.5, 0.6}	{0.7, 0.8, 0.9}
e₄	{0.3}	{0.6, 0.7, 0.8}	{0.8, 0.9}

专家	CI₁₂^q	CI₁₃^q	CI₁₄^q	CI₂₃^q	CI₂₄^q	CI₃₄^q
e₁	0.94	0.96	0.90	0.98	0.95	0.95
e₂	0.96	0.89	0.90	0.86	0.87	0.97
e₃	0.90	0.93	0.96	0.94	0.95	0.96
e₄	0.98	0.97	0.96	0.98	0.97	0.98

专家	${\bar h}$^q₁₂	${\bar h}$^q₁₃	${\bar h}$^q₁₄
e₁	{0.3, 0.3, 0.4}	{0.5, 0.5, 0.7}	{0.4, 0.4, 0.4}
e₂	{0.3, 0.38, 0.5}	{0.1, 0.2, 0.2}	{0.5, 0.6, 0.7}
e₃	{0.3, 0.39, 0.5}	{0.5, 0.5, 0.7}	{0.7, 0.7, 0.8}
e₄	{0.4, 0.5, 0.6}	{0.3, 0.33, 0.4}	{0.5, 0.5, 0.7}

专家	${\bar h}$^q₂₃	${\bar h}$^q₂₄	${\bar h}$^q₃₄
e₁	{0.7, 0.7, 0.9}	{0.8, 0.9, 1.0}	{0.6, 0.6, 0.7}
e₂	{0.7, 0.73, 0.8}	{0.1, 0.5, 0.5}	{0.5, 0.7, 0.7}
e₃	{0.2, 0.29, 0.4}	{0.5, 0.6, 0.6}	{0.7, 0.8, 0.9}
e₄	{0.3, 0.3, 0.3}	{0.6, 0.7, 0.8}	{0.8, 0.9, 0.9}

专家	PI₁₂^q	PI₁₃^q	PI₁₄^q	PI₂₃^q	PI₂₄^q	PI₃₄^q
e₁	0.94	0.89	0.85	0.79	0.80	0.90
e₂	0.96	0.84	0.92	0.83	0.82	0.90
e₃	0.96	0.89	0.87	0.82	0.87	0.91
e₄	0.92	0.89	0.92	0.82	0.88	0.89

专家	ω^q₁₂	ω^q₁₃	ω^q₁₄	ω^q₂₃	ω^q₂₄	ω^q₃₄
e₁	0.18	0.44	0.15	0.15	0.15	0.22
e₂	0.44	0.15	0.44	0.44	0.17	0.18
e₃	0.23	0.23	0.18	0.18	0.23	0.44
e₄	0.15	0.18	0.23	0.23	0.45	0.16