A Multi-attribute Decision-making Optimization Algorithm Based on Conflict Evidence Fusion and Cloud Model

Abstract

To eliminate fuzziness and uncertainty of linguistic comments in multi-attribute decision-making, this paper introduces D-S evidence theory into cloud model assessment. Golden section method is employed to convert experts’ linguistic comments into cloud decision-making matrix, and then criterion clouds of different levels in comment set are taken as the reference to determine the membership to each level of assessment, so as to construct the basic probability allocation function (mass function) for different experts in respect of different attributes in different schemes. Thereafter, conflict coefficient, Jousselme distance, and Pignistic probability distance are introduced on the basis of D-S evidence theory to define evidence conflict measure. By calculating evidence reliability and relative expert weight, mass functions for experts are modified and fused. In the end, mass functions of different schemes are fused on the basis of attribute weight, and compared with the mass functions of ideal cloud and negative ideal cloud. Therefore, optimal scheme will be determined by comparing average closeness.