Dynamic Group Decision Making Consistence Convergence Rate Analysis Based on Inertia Particle Swarm Optimization Algorithm

The traditional group decision-making method mostly is static aggregation method which scientifically defines the decision weighted vector of each decision makers. In fact, the group decision making convergence is always a dynamic process. The preferences of decision maker are dynamic changing in this process, it is more realistic to apply the dynamic model to consider the consistence convergence process. This article defines the consistence convergence coefficient according to the difference between the average order of decision evaluation matrix and total order. And it proposes an inertia particle swarm optimization model to discuss the influence of knowledge transfer to group decision consistence, specifically to analyze the convergence process. The model applies bionics principle to describe and compute the knowledge transfer and preference convergence process in group decision. Then we discuss and analyze the decision consistence convergence rate and the reason on the study case that decision making individual has the innate knowledge and knowledge transfer in group. In addition, the inertia Pso model proposed in this article is reasonable and effective in solving the project choice plan, and it will provide the basis for consistence convergence analysis in dynamic group decision making.