Hongming Mo

A New Aggregating Operator for Linguistic Information Based on D Numbers

How to efficiently make decision under uncertain is a hot research topic. In this paper, a new aggregating operator for linguistic decision making based on D numbers is proposed. D numbers is a new mathematic tool to model uncertainty, since it ignores the condition that elements on the frame must be mutually exclusive. Compared with these existing aggregating operators, the new method is clear, concise and less computational complexity. A numerical example is used to demonstrate the effectiveness of the proposed method.

A new method in failure mode and effects analysis based on evidential reasoning

The traditional failure mode and effects analysis (FMEA) is determined by risk priority number (RPN), which is the product of three risk factors occurrence (O), severity (S), and detection (D). One of the open issues is how to precisely determine and aggregate the risk factors. However, the traditional FMEA has been extensively criticized for various reasons. In this paper, a new method in fuzzy FMEA is proposed using evidential reasoning (ER) and the technique for order preference by similarity to ideal solution (TOPSIS). The ER approach is used to express the experts’ assessment information which may be imprecise and uncertain. Considering the experts’ weights, we construct the group assessment. Weighted average method is then utilized to transform the group assessment value into crisp value. TOPSIS is applied to aggregate the risk factors which are taken to account as the multi-attribute, and used to rank the risk priority. By making full use of attribute information, TOPSIS provides a cardinal ranking of alternatives, and does not require the attribute preferences are independent. A numerical example shows that the proposed method is efficient to its applications.

A generalized evidence distance

Dempster-Shafer theory of evidence (D-S theory) is widely used in uncertain information process. The basic probability assignment(BPA) is a key element in D-S theory. How to measure the distance between two BPAs is an open issue. In this paper, a new method to measure the distance of two BPAs is proposed. The proposed method is a generalized of existing evidence distance. Numerical examples are illustrated that the proposed method can overcome the shortcomings of existing methods.

Evidential method to identify influential nodes in complex networks

Identifying influential nodes in complex networks is still an open issue. In this paper, a new comprehensive centrality mea- sure is proposed based on the Dempster-Shafer evidence theory. The existing measures of degree centrality, betweenness centra- lity and closeness centrality are taken into consideration in the proposed method. Numerical examples are used to illustrate the effectiveness of the proposed method.

Generalized fuzzy cognitive maps: a new extension of fuzzy cognitive maps

A fuzzy cognitive maps (FCM) is a cognitive map within the relations between the elements. FCM has been widely used in many applications such as experts system and knowledge engineering. However, classical FCM is inherently short of sufficient capability of representing and aggregating uncertain information. In this paper, generalized FCM (GFCM) is proposed based on genetic algorithm and interval numbers. An application frame of GFCM is detailed. At last, a numerical example about socio-economic system is used to illustrate the effectiveness of the proposed methodology.

A bio-inspired algorithm for solving bi-objective shortest-path problem

The bi-objective shortest-path problem (BSP) has attracted much attention since its great theoretical significance and wide application. The solution of this problem is to seek for the Pareto-optimal set of paths between two nodes in a graph, in which the edges refer to two attributes. The traditional methods either are highly time-consuming or only find the approximate solutions. In this paper, based on the Physarum Polycephalum model, we propose an algorithm to solve the BSP. By aid of adaptability of the Physarum Polycephalum model, the proposed method can find the multi-shortest paths with a low time consuming. The Pareto-optimal set, obtained from the multi-shortest paths, is the solution of the BSP. To test the performance of the proposed method, we conduct the experiments on some simulative graphs selected from the recently related works. The results show that the proposed method is efficient.