Research on the spatial optimal aggregation method of decision maker preference information based on Steiner-Weber point

The purpose of this paper is to provide a novel approach for the spatial aggregation of decision maker preference information. The optimal aggregation method of decision maker preference information based on spatial SteinerWeber point can effectively aggregate the preference information of group members and achieve the optimization of group preference. The method comprises three key elements: the spatial mapping of the group preference, the spatial optimal aggregation model of the group preference, and the plant growth simulation algorithm (PGSA) is used to find the optimal aggregation points. Firstly, the group preference are mapped into a set of spatial multidimensional coordinates by using spatial mapping rules. Secondly, the spatial Steiner-Weber point is used as the prototype to construct the spatial aggregation model. Thirdly, the PGSA algorithm is used to find the spatial aggregation points, whose spatial weighted Euclidean distance to all the decision makers' preference points is minimal. The optimal aggregation matrix is composed of these optimal aggregation points, which can accurately reflect the decision makers' comprehensive opinions. Finally, the effectiveness and rationality of this method are verified by comparing with the classical group preference aggregation methods.

Automated summary

This paper aims to provide a novel approach for spatially aggregating decision maker preference information. The optimal aggregation method, based on spatial Steiner-Weber point, effectively aggregates the preference information of group members and optimizes group preference. The method consists of three key elements: the spatial mapping of group preference, the spatial optimal aggregation model of group preference, and the use of the plant growth simulation algorithm to find optimal aggregation points. By comparing with classical group preference aggregation methods, the effectiveness and rationality of this approach are verified.