基于高度残缺信息的分布式语言信任网络群决策方法

doi:10.12305/j.issn.1001-506X.2022.06.17

摘要/Abstract

摘要：

针对缺失的分布式语言信任社会矩阵以及高度残缺的评价信息, 提出一种新的分布式语言信任网络群决策方法。首先, 提出了一种分布式语言信任传递模型以填充分布式语言信任社会矩阵中的缺失值。其次, 定义了基于分布式语言信任社会矩阵的个体信任中心度和群体信任中心度, 并在此基础上提出了确定评价网络中专家权重的方法。再者, 鉴于决策矩阵中评价信息存在高度残缺的情形, 利用K-近邻算法评估残缺的评价信息, 并基于分布式语言信任函数的距离测度提出一种改进的逼近理想解排序(technique for order preference by similarity to an ideal solution, TOPSIS)法。最后, 通过外卖服务平台性价比的综合评估验证该群决策方法的可行性和有效性。

关键词: 分布式语言信任网络, 信任传递模型, 高度残缺, K-近邻算法, 逼近理想解排序法

Abstract:

Aiming at the missing distributed linguistic trust social matrix and highly incomplete evaluation information, a new group decision-making method for distributed linguistic trust network is proposed. Firstly, a distributed linguistic trust propagation model is proposed to fill the missing values in the distributed linguistic trust social matrix. Secondly, the individual trust centrality and the group trust centrality based on the distributed linguistic trust social matrix are defined, and a method to determine the weight of experts in the evaluation network is proposed. Furthermore, in view of the highly incomplete evaluation information in the decision matrix, the K-nearest neighbor algorithm is used to evaluate the incomplete evaluation information, and an improved technique for order preference by similarity to an ideal solution (TOPSIS) method is proposed based on the distance measure of distributed linguistic trust function. Finally, the feasibility and the effectiveness of the group decision making method are verified by the comprehensive evaluation of the cost performance of the takeout service platform.

Key words: distributed linguistic trust network, trust propagation model, highly incomplete ness, K-nearest neighbor algorithm, technique for order preference by similarity to an ideal solution (TOPSIS) method

中图分类号:

C934

曾守桢, 胡英杰. 基于高度残缺信息的分布式语言信任网络群决策方法[J]. 系统工程与电子技术, 2022, 44(6): 1907-1919.

Shouzhen ZENG, Yingjie HU. Group decision making method for distributed linguistic trust network based on highly incomplete information[J]. Systems Engineering and Electronics, 2022, 44(6): 1907-1919.

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参考文献 26

1	PEI F , HE Y W , YAN A , et al. A consensus model for intuitionistic fuzzy group decision-making problems based on the construction and propagation of trust/distrust relationships in social networks[J]. International Journal of Fuzzy Systems, 2020, 22 (8): 2664- 2679. doi: 10.1007/s40815-020-00980-0
2	ZHENG Y F , XU J . A trust transitivity model for group decision making in social network with intuitionistic fuzzy information[J]. Filomat, 2018, 32 (5): 1937- 1945. doi: 10.2298/FIL1805937Z
3	WU J , XIONG R Y , CHICLANA F . Uninorm trust propagation and aggregation methods for group decision making in social network with four tuple information[J]. Knowledge-Based Systems, 2016, 96, 29- 39. doi: 10.1016/j.knosys.2016.01.004
4	向南, 豆亚杰, 姜江, 等. 基于专家信任网络的不完全信息武器选择决策[J]. 系统工程理论与实践, 2021, 41 (3): 759- 770.
	XIANG N , DOU Y J , JIANG J , et al. Weapon selection decision-making based on expert trust network under incomplete information[J]. Systems Engineering-Theory & Practice, 2021, 41 (3): 759- 770.
5	YUE Z L . A method for group decision-making based on determining weights of decision makers using TOPSIS[J]. Applied Mathematical Modelling, 2011, 35 (4): 1926- 1936. doi: 10.1016/j.apm.2010.11.001
6	KIM Y A , SONG H S . Strategies for predicting local trust based on trust propagation in social networks[J]. Knowledge-Based Systems, 2011, 24 (8): 1360- 1371. doi: 10.1016/j.knosys.2011.06.009
7	GUHA R, KUMAR R, RAGHAVAN P, et al. Propagation of trust and distrust[C]//Proc. of the 13th International Conference on World Wide Web, 2004: 403-412.
8	YAN L S , WEI Y , ZHU H , et al. Information theoretic framework of trust modeling and evaluation for ad hoc networks[J]. IEEE Journal on Selected Areas in Communications, 2006, 24 (2): 305- 317. doi: 10.1109/JSAC.2005.861389
9	LIU X , XU Y J , MONTES R , et al. Social network group decision making: managing self-confidence-based consensus model with the dynamic importance degree of experts and trust-based feedback mechanism[J]. Information Sciences, 2019, 505, 215- 232. doi: 10.1016/j.ins.2019.07.050
10	WU J , DAI L F , CHICLANA F , et al. A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust[J]. Information Fusion, 2017, 41, 232- 242.
11	WU J , CHICLANA F , FUJITA H , et al. A visual interaction consensus model for social network group decision making with trust propagation[J]. Knowledge-Based Systems, 2017, 122, 39- 50. doi: 10.1016/j.knosys.2017.01.031
12	WU J , CHICLANA F , HERRERA-VIEDMA E . Trust based consensus model for social network in an incomplete linguistic information context[J]. Applied Soft Computing, 2015, 35, 827- 839. doi: 10.1016/j.asoc.2015.02.023
13	TIAN Z P , NIE R X , WANG J Q , et al. A two-fold feedback mechanism to support consensus-reaching in social network group decision-making[J]. Knowledge-Based Systems, 2018, 162, 74- 91. doi: 10.1016/j.knosys.2018.09.030
14	曹清玮, 戴丽芳, 孙琪, 等. 社会网络环境下基于分布式信任的在线评价方法[J]. 控制与决策, 2020, 35 (7): 1697- 1702.
	CAO Q W , DAI L F , SUN Q , et al. A distributed trust based online evaluation under social network[J]. Control and Decision, 2020, 35 (7): 1697- 1702.
15	戴丽芳, 吴坚, 曹清玮, 等. 分布式信任网络下基于最小协调成本的群体共识方法[C]//第十九届中国管理科学学术年会, 2017.
	DAI L F, WU J, CAO Q W, et al. A group consensus approach based on minimum coordination cost in distributed trust network[C]//Proc. of the 19th Chinese Annual Conference on Management Science, 2017.
16	WU J , CHANG J L , CAO Q W , et al. A trust propagation and collaborative filtering based method for incomplete information in social network group decision making with type-2 linguistic trust[J]. Computers & Industrial Engineering, 2019, 127, 853- 864.
17	CHU J F , WANG Y M , LIU X W , et al. Social network community analysis based large-scale group decision making approach with incomplete fuzzy preference relations[J]. Information Fusion, 2020, 60, 98- 120. doi: 10.1016/j.inffus.2020.02.005
18	熊若云. 基于社会网络信任的网商信用评价[D]. 金华: 浙江师范大学, 2016.
	XIONG R Y. Credit evaluation of E-enterprise based on trust social network[D]. Jinhua: Zhejiang Normal University, 2016.
19	GAO H X , JU Y B , ZENG X J , et al. Satisfaction-driven consensus model for social network MCGDM with incomplete information under probabilistic linguistic trust[J]. Computers & Industrial Engineering, 2021, 154, 107099.
20	XU Y J , LI C Y , WEN X W . Missing values estimation and consensus building for incomeplete hesitant fuzzy preference relations with multiplicative consistency[J]. International Journal of Computational IntelligenceSystems, 2018, 11 (1): 101- 119.
21	TAGHAVI A , ESLAMI E , HERRERA-VIEDMA E , et al. Trust based group decision making in environments with extreme uncertainty[J]. Knowledge-Based Systems, 2020, 191, 105168. doi: 10.1016/j.knosys.2019.105168
22	XU Y J , MA F , TAO F F , et al. Some methods to deal with unacceptable incomplete 2-tuple fuzzy linguistic preference relations in group decision making[J]. Knowledge-Based Systems, 2014, 56, 179- 190. doi: 10.1016/j.knosys.2013.11.008
23	URENA R , CHICLANA F , MELANCON G , et al. A social network based approach for consensus achievement in multiperson decision making[J]. Information Fusion, 2019, 47, 72- 87. doi: 10.1016/j.inffus.2018.07.006
24	KLEMENT E P , MESIAR R , PAP E . Triangular norms. Position paper Ⅰ: basic analytical and algebraic properties[J]. Fuzzy Sets and Systems, 2004, 143 (1): 5- 26. doi: 10.1016/j.fss.2003.06.007
25	匡继昌. 常用不等式[M]. 山东: 山东科学技术出版社, 2004.
	KUANG J C . Applied inequalities[M]. Shandong: Shandong Science and Technology Press, 2004.
26	CHEN H P . Hesitant fuzzy multi-attribute group decision making method based on weighted power operators in social network and their application[J]. Journal of Intelligent & Fuzzy Systems, 2021, 40 (5): 9383- 9401.

e₁	C₁	C₂	C₃	C₄
X₁	{(D₁, 0.1), (D₂, 0.7), (D₃, 0.2)}	{(D₁, 0.2), (D₂, 0.5), (D₃, 0.3)}	{(D₁, 0.2), (D₂, 0.5), (D₃, 0.3)}	{(D₁, 0.2), (D₂, 0.1), (D₃, 0.7)}
X₂	-	-	-	-
X₃	{(D₁, 0.5), (D₂, 0.2), (D₃, 0.3)}	{(D₁, 0.6), (D₂, 0.3), (D₃, 0.1)}	{(D₁, 0.4), (D₂, 0.5), (D₃, 0.1)}	{(D₁, 0.1), (D₂, 0.8), (D₃, 0.1)}

e₂	C₁	C₂	C₃	C₄
X₁	{(D₁, 0.1), (D₂, 0.0), (D₃, 0.9)}	{(D₁, 0.3), (D₂, 0.6), (D₃, 0.1)}	{(D₁, 0.0), (D₂, 0.2), (D₃, 0.8)}	{(D₁, 0.7), (D₂, 0.3), (D₃, 0.0)}
X₂	{(D₁, 0.5), (D₂, 0.3), (D₃, 0.2)}	{(D₁, 0.3), (D₂, 0.6), (D₃, 0.1)}	{(D₁, 0.8), (D₂, 0.2), (D₃, 0.0)}	{(D₁, 0.3), (D₂, 0.7), (D₃, 0.0)}
X₃	{(D₁, 0.4), (D₂, 0.6), (D₃, 0.0)}	{(D₁, 0.2), (D₂, 0.4), (D₃, 0.4)}	{(D₁, 0.6), (D₂, 0.4), (D₃, 0.0)}	{(D₁, 0.0), (D₂, 0.5), (D₃, 0.5)}

e₃	C₁	C₂	C₃	C₄
X₁	{(D₁, 0.0), (D₂, 0.7), (D₃, 0.3)}	{(D₁, 0.2), (D₂, 0.8), (D₃, 0.0)}	{(D₁, 0.2), (D₂, 0.4), (D₃, 0.4)}	{(D₁, 0.1), (D₂, 0.1), (D₃, 0.8)}
X₂	{(D₁, 0.5), (D₂, 0.3), (D₃, 0.2)}	{(D₁, 0.7), (D₂, 0.2), (D₃, 0.1)}	{(D₁, 0.6), (D₂, 0.4), (D₃, 0.0)}	{(D₁, 0.4), (D₂, 0.4), (D₃, 0.2)}
X₃	{(D₁, 0.2), (D₂, 0.3), (D₃, 0.5)}	{(D₁, 0.2), (D₂, 0.4), (D₃, 0.4)}	{(D₁, 0.0), (D₂, 0.9), (D₃, 0.1)}	{(D₁, 0.4), (D₂, 0.5), (D₃, 0.1)}

e₄	C₁	C₂	C₃	C₄
X₁	{(D₁, 0.0), (D₂, 0.4), (D₃, 0.6)}	{(D₁, 0.2), (D₂, 0.3), (D₃, 0.5)}	{(D₁, 0.0), (D₂, 0.7), (D₃, 0.3)}	{(D₁, 0.3), (D₂, 0.4), (D₃, 0.3)}
X₂	{(D₁, 0.8), (D₂, 0.2), (D₃, 0.0)}	{(D₁, 0.5), (D₂, 0.4), (D₃, 0.1)}	{(D₁, 0.6), (D₂, 0.3), (D₃, 0.1)}	{(D₁, 0.2), (D₂, 0.7), (D₃, 0.1)}
X₃	{(D₁, 0.2), (D₂, 0.6), (D₃, 0.2)}	{(D₁, 0.2), (D₂, 0.3), (D₃, 0.5)}	{(D₁, 0.2), (D₂, 0.7), (D₃, 0.1)}	{(D₁, 0.1), (D₂, 0.9), (D₃, 0.0)}

e₅	C₁	C₂	C₃	C₄
X₁	{(D₁, 0.4), (D₂, 0.5), (D₃, 0.1)}	{(D₁, 0.3), (D₂, 0.4), (D₃, 0.3)}	{(D₁, 0.5), (D₂, 0.3), (D₃, 0.2)}	{(D₁, 0.5), (D₂, 0.0), (D₃, 0.5)}
X₂	{(D₁, 0.5), (D₂, 0.4), (D₃, 0.1)}	{(D₁, 0.3), (D₂, 0.5), (D₃, 0.2)}	{(D₁, 0.3), (D₂, 0.6), (D₃, 0.1)}	{(D₁, 0.6), (D₂, 0.4), (D₃, 0.0)}
X₃	{(D₁, 0.3), (D₂, 0.7), (D₃, 0.0)}	{(D₁, 0.0), (D₂, 0.8), (D₃, 0.2)}	{(D₁, 0.5), (D₂, 0.4), (D₃, 0.1)}	{(D₁, 0.4), (D₂, 0.6), (D₃, 0.0)}