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Particle swarm optimization for trust relationship based social network group decision making under a probabilistic linguistic environment

机译：基于信任关系的粒子群优化在概率语言环境下基于信任关系的社交网络组决策

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Group decision making (GDM) problems require consensus reaching processes; however, these can be time consuming and costly. As experts change their evaluations after exchanging opinions and being influenced by others, these influences are spread across the various expert trust relationships. Because of the experts' knowledge limits, the evaluations on the alternatives and the trust relationships are generally described using probabilistic linguistic terms. Therefore, to simplify the decision making process and avoid decision bias, this paper proposes a particle swarm optimization method that incorporates a trust relationship based social network for GDM under a probabilistic linguistic environment. Each expert is regarded as a particle that moves toward the final evaluation and reaches the threshold. A fitness function is built to measure the consensus levels, and the updated function is improved by the trust relationships to derive the new evaluations. A numerical example is then given to illustrate the feasibility of the proposed approach and comparisons given to further elucidate its novelty and validity. (C) 2020 Published by Elsevier B.V.
机译：集团决策（GDM）问题需要共识到期进程;然而，这些可能是耗时和昂贵的。由于专家在交换意见后改变评估并受其他人的影响，这些影响遍布各专家信任关系。由于专家的知识限制，替代方案和信任关系通常使用概率语言术语来描述。因此，为了简化决策过程并避免决策偏置，提出了一种粒子群优化方法，其在概率语言环境下包括基于信任关系的GDM的社交网络。每个专家都被视为一种旨在朝着最终评估和达到阈值而移动的粒子。建立一个健身功能以衡量共识级别，并且通过信任关系改善了更新的函数来导出新的评估。然后给出数值例子以说明所提出的方法和比较进一步阐明其新颖性和有效性的比较的可行性。 （c）2020由elsevier b.v发布。

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