Yajuan Zhang

Short communication: Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment

A common algorithm to solve the shortest path problem (SPP) is the Dijkstra algorithm. In this paper, a generalized Dijkstra algorithm is proposed to handle SPP in an uncertain environment. Two key issues need to be addressed in SPP with fuzzy parameters. One is how to determine the addition of two edges. The other is how to compare the distance between two different paths with their edge lengths represented by fuzzy numbers. To solve these problems, the graded mean integration representation of fuzzy numbers is adopted to improve the classical Dijkstra algorithm. A numerical example of a transportation network is used to illustrate the efficiency of the proposed method.

Assessment of E-Commerce security using AHP and evidential reasoning

In the development of E-Commerce, security has always been the core and key issue. In this paper, a new model is proposed to assist E-Commerce practitioners in the assessment of E-Commerce security. The proposed model is based on Analytical Hierarchy Process (AHP) and Dempster-Shafer (DS) theory of evidence. First, according to the characteristics of E-Commerce, a hierarchical structure of E-Commerce security is established to calculate the weights of relevant issues using AHP. Then Dempster-Shafer theory of evidence is applied to combine all the issues, regarded as evidences, in order to derive a consensus decision for the degree of E-Commerce security. An illustrative example is given to show the efficiency of our model.

Short Communication: A new optimal consensus method with minimum cost in fuzzy group decision

Finding group consensus plays a very important role in group decision making (GDM). In this short communication, a new optimal consensus method with minimum cost in fuzzy GDM is proposed. The main contribution of our work is that the limit of each experts compromise is under consideration in the process of reaching group consensus. The numerical example shows the efficiency of the proposed method.

A biologically inspired solution for fuzzy shortest path problems

By considering the uncertainty that exists in the edge weights of the network, fuzzy shortest path problems, as one of the derivative problems of shortest path problems, emerge from various practical applications in different areas. A path finding model, inspired by an amoeboid organism, Physarum polycephalum, has been shown as an effective approach for deterministic shortest path problems. In this paper, a biologically inspired algorithm called Fuzzy Physarum Algorithm (FPA) is proposed for fuzzy shortest path problems. FPA is developed based on the path finding model, while utilizing fuzzy arithmetic and fuzzy distance to deal with fuzzy issues. As a result, FPA can represent and handle the fuzzy shortest path problem flexibly and effectively. Distinct from many existing methods, no order relation has been assumed in the proposed FPA. Several examples, including a tourist problem, are given to illustrate the effectiveness and flexibility of the proposed method and the results are compared with existing methods.

A note on ranking generalized fuzzy numbers

Ranking fuzzy numbers plays an important role in decision making under uncertain environment. Recently, Chen and Sanguansat (2011) [Chen, S. M. & Sanguansat, K. (2011). Analyzing fuzzy risk based on a new fuzzy ranking method between generalized fuzzy numbers. Expert Systems with Applications, 38(3), (pp. 2163-2171)] proposed a method for ranking generalized fuzzy numbers. It considers the areas on the positive side, the areas on the negative side and the heights of the generalized fuzzy numbers to evaluate ranking scores of the generalized fuzzy numbers. Chen and Sanguansats method (2011) can overcome the drawbacks of some existing methods for ranking generalized fuzzy numbers. However, in the situation when the score is zero, the results of the Chen and Sanguansats ranking method (2011) ranking method are unreasonable. The aim of this short note is to give a modification on Chen and Sanguansats method (2011) to make the method more reasonable.

Degree centrality based on the weighted network

Node centrality has been widely studied in the complex networks. In 2010, the model of node centrality under the weighted network was obtained by Tore Opashl et al. Tie weights and the number of ties were connected with certain proportion by tuning parameter in the model. However, the proportion is random measure. In this paper, the selection standard of the optimal turning parameters is proposed. In the proposed method, the maximum degree centrality of node can be emphasized. The numerical example of weighted network on optimal value selection is used to show the efficiency of the method.

An application of genetic algorithm for university course timetabling problem

Timetabling problems are a process of assigning a given set of events and resources to the limited space and time under hard constraints which are rigidly enforced and soft constraints which are satisfied as nearly as possible. As a kind of timetabling problems, university course timetabling is a very important administrative activity for a wide variety of schools. Genetic algorithm is an advanced heuristic method which is very effective in many fields. In this paper, genetic algorithm is used to solve university course timetabling problem. At first, a model of problem to be solved is defined. Then, the genetic representation is determined and a fitness function is established according to the constraints. Finally, a case of university course timetabling from real-world is discussed and solved. It is demonstrated that the method proposed in this paper is feasible and efficient.

An improved bio-inspired algorithm for the directed shortest path problem.

Because most networks are intrinsically directed, the directed shortest path problem has been one of the fundamental issues in network optimization. In this paper, a novel algorithm for finding the shortest path in directed networks is proposed. It extends a bio-inspired path finding model of Physarum polycephalum, which is designed only for undirected networks, by adopting analog circuit analysis. Illustrative examples are given to show the effectiveness of the proposed algorithm in finding the directed shortest path.

An amoeboid algorithm for shortest path in fuzzy weighted networks

Taking the uncertainty existing in edge weights of networks into consideration, finding shortest path in such fuzzy weighted networks has been widely studied in various practical applications. In this paper, an amoeboid algorithm is proposed, combing fuzzy sets theory with a path finding model inspired by an amoeboid organism, Physarum polycephalum. With the help of fuzzy numbers, uncertainty is well represented and handled in our algorithm. Whats more, biological intelligence of Physarum polycephalum has been incorporate into the algorithm. A numerical example on a transportation network is demonstrated to show the efficiency and flexibility of our proposed amoeboid algorithm.

Uncertain information clustering based on distance between BPAs

It is necessary to cluster the information according to their sources when analyzing multi-source information. In this paper, a new evidential clustering method is proposed. In the proposed method, pairwise distance between BPAs have been introduced to form a matrix for clustering. The clustering method is based on vector which is transformed from distance matrix. Illustrative example with several sets demonstrate the validity of the proposed method as compared to other methods.