Large-scale group consensus hybrid strategies with three-dimensional clustering optimization based on normal cloud models

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dc.cclicenceCC-BY-NC-NDen
dc.contributor.authorLiu, Weiqiao
dc.contributor.authorZhu, Jianjun
dc.contributor.authorChiclana, Francisco
dc.date.acceptance2023-01-17
dc.date.accessioned2023-01-23T14:49:06Z
dc.date.available2023-01-23T14:49:06Z
dc.date.issued2023-01-20
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractLarge-scale group decision-making (LSGDM) is characterised by a large number of experts and a complex consensus reaching process. Clustering is used to divide the large group into a number of manageable subgroups; however, the simultaneously presence of all subgroup members at the negotiation process is rare. Thus, the selection of subgroup representatives for a smooth negotiation is necessary. Few LSGDM consensus recommendation optimisation models truly consider the problems of subgroup representative selection in their strategy to reach consensus. This article proposes a LSGDM consensus hybrid strategy framework with three-dimension clustering optimisation based on normal cloud models (NCMs) whose aims are threefold: (1) the use of NCMs to represent the imprecision of linguistic preferences provided in real complex decision scenarios with large number of experts; (2) to establish a clustering optimisation method to choose subgroup representatives using three sensible criteria: preference similarity level within the subgroup, preference precision level, and preference consistency level; and (3) to establish two consensus recommendation optimisation strategies for individual negotiation-guided and moderator-guided consensus reaching, respectively. The feasibility and applicability of the proposed method are illustrated via a power curtailment policy assessment example, then some sensitive and comparative analyses are conducted to explicit the effectiveness and advantages of the proposed consensus hybrid strategies.en
dc.funderNo external funderen
dc.identifier.citationLiu, W., Zhu, J. and Chiclana, F. (2023) Large-scale group consensus hybrid strategies with three-dimensional clustering optimization based on normal cloud models. Information Fusion, 94, pp. 66-91en
dc.identifier.doihttps://doi.org/10.1016/j.inffus.2023.01.017
dc.identifier.urihttps://hdl.handle.net/2086/22457
dc.language.isoenen
dc.peerreviewedYesen
dc.publisherElsevieren
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectNormal cloud modelen
dc.subjectThree-dimensional clustering optimisationen
dc.subjectConsensus hybrid strategies,en
dc.subjectPreference compromise limiten
dc.subjectConsistencyen
dc.titleLarge-scale group consensus hybrid strategies with three-dimensional clustering optimization based on normal cloud modelsen
dc.typeArticleen

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