基于前景理论的模拟植物生长算法研究

Plant Growth Simulation Algorithm Based on Prospect Theory

针对属性值和属性权重均为区间数,决策者权重未知的多属性群决策问题,文章提出了一种基于前景理论的群体最优集结算法。文章首先以决策者可承受的心理临界值为参考点,将属性权重转化为决策者的主观概率权重,计算各决策者的方案综合前景值向量,以各决策者对方案前景值的贡献度确定决策者权重;然后,将各决策者的方案综合前景值映射到平面直角坐标系中,利用最小欧式距离的思想,采用模拟植物生长算法求解群体最优集结信息,进而根据各方案集结前景值的可能度比较实现方案的排序;最后,通过实例验证分析说明了该方法的可行性和有效性。

To solve the multi-attribute group decision-making problem with unknown attribute values and attribute probability,an optimal aggregation algorithm based on prospect theory is proposed.The psychological critical values that decision makers bear can be regarded as reference points and the interval attribute weights can be converted to subjective weights.Then,the comprehensive prospect value vectors of each decision maker are calculated.The prospect value contributions of decision makers are calculated to determine the weights of decision makers.On this basis,the prospect values are mapped to plane coordinate.By applying the plant growth simulation algorithm and using the minimum Euclidean distance,the optimal aggregation information is solved.Then,by comparing the comprehensive prospect value,alternatives are ranked.Finally,by validate experiment analysis,the feasibility and effectiveness of the proposed method is illustrated.