基于决策者偏好投影寻踪模型的多属性决策法

针对现有主观赋权法和客观赋权法的不足,提出了一种新的综合赋权方法——基于决策者偏好及投影寻踪聚类模型的综合赋权法。该方法运用投影寻踪聚类模型,把多属性决策问题中的高维数据转化到低维子空间,同时用自适应粒子群优化算法来优化投影指标函数和模型参数,获得了决策属性体系最佳投影方向和投影值,揭示了高维数据的结构特征。同时,也考虑了决策者对不同属性的偏好,使对属性的赋权达到主观与客观的统一。最后通过一个仿真实例说明了该方法的可行性与有效性。

Multiple Attribute Decision-making Method Based on Preference Information and Projecting Pursuit Classification Model

In view of the shortage of the present subjective and objective assigning weight methods,a new combination assigning weight approach based on the decision-maker's preference and projecting pursuit classification model was proposed. Through applying projecting pursuit classification model based on adaptive particle swarm optimization algorithm in multiple attribute decision-making problems,the multi-dimension data of decision-making problem were easily changed into low dimension space and the multi-dimension data's structure feature could be discovered. Accordingly the optimum projection direction and the value of project function could be obtained. At the same time,this approach considered the decision-maker's preference information,too. The simulation results show that the proposed approach is effective and feasible.