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.

小组决策（GDM）问题需要达成共识的流程；然而，这些可能是耗时且昂贵的。当专家在交换意见并受到他人影响后更改评估时，这些影响分散在各种专家信任关系中。由于专家的知识限制，通常使用概率语言术语来描述对替代方案和信任关系的评估。因此，为简化决策过程并避免决策偏差，本文提出了一种粒子群优化方法，该方法结合了概率语言环境下基于信任关系的GDM社交网络。每个专家都被视为朝着最终评估并达到阈值的方向发展。构建适应性函数以测量共识级别，并且通过信任关系来改进更新的函数以得出新的评估。然后给出一个数值示例，以说明该方法的可行性，并进行比较以进一步阐明其新颖性和有效性。