Consensus reaching process (CRP) is important and present in a wide range of application areas. In practical CRP, the managers (e.g., enterprise) often hire some informed individuals (e.g., persuaders) to promote the efficiency of consensus reaching. This article proposes a CRP with minimum cost of informed individuals and time constraint in large-scale group decision-making (LSGDM) with bounded confidence effects. The consensus model with bounded confidence effects (CBC model) is formulated. Then, desirable properties of the CBC model are discussed to facilitate its resolution. Next, an extended particle swarm optimization algorithm is designed to solve the CBC model. Finally, a numerical analysis, a comparison analysis, and a simulation analysis are provided to illustrate the feasibility and effectiveness of the proposed approach.

中文翻译：

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大规模群体决策中以最小知情者成本和时间限制达成共识


共识达成过程（CRP）很重要，并且存在于广泛的应用领域。在实际的CRP中，管理者（如企业）经常聘请一些知情人士（如说服者）来提高达成共识的效率。本文提出了一种在具有有限置信效应的大规模群体决策（LSGDM）中具有最小知情个体成本和时间约束的 CRP。制定了具有有限置信效应的共识模型（CBC模型）。然后，讨论 CBC 模型的理想属性以促进其解决。接下来，设计了扩展的粒子群优化算法来求解CBC模型。最后，通过数值分析、比较分析和仿真分析来说明该方法的可行性和有效性。