Changyong Liang

Combining QoS prediction and customer satisfaction estimation to solve cloud service trustworthiness evaluation problems

The collection and combination of assessment data in trustworthiness evaluation of cloud service is challenging, notably because QoS value may be missing in offline evaluation situation due to the time-consuming and costly cloud service invocation. Considering the fact that many trustworthiness evaluation problems require not only objective measurement but also subjective perception, this paper designs a novel framework named CSTrust for conducting cloud service trustworthiness evaluation by combining QoS prediction and customer satisfaction estimation. The proposed framework considers how to improve the accuracy of QoS value prediction on quantitative trustworthy attributes, as well as how to estimate the customer satisfaction of target cloud service by taking advantages of the perception ratings on qualitative attributes. The proposed methods are validated through simulations, demonstrating that CSTrust can effectively predict assessment data and release evaluation results of trustworthiness.

Generalized cross-entropy based group decision making with unknown expert and attribute weights under interval-valued intuitionistic fuzzy environment

Abstract This paper focuses on the multiple attribute decision making problems widespread in industry engineering, typically the supplier selection problems, and investigates effective methods utilizing preference information objectively for multiple attribute group decision making (MAGDM) with unknown attribute weights and expert weights under interval-valued intuitionistic fuzzy environments (IVIFEs). Firstly, a novel generalized cross-entropy measure for interval-valued intuitionistic fuzzy sets (IVIFSs) is proposed to enable decision makers to express attitudinal characteristic information of hesitation degree rather than arbitrary equal assignment by other cross-entropy methods. Further, based on the generalized cross-entropy measure, simultaneously considering the divergence of attribute assessments from the most fuzzy number in IVIFSs and the deviation between attribute assessments, a maximizing optimization model is proposed for objectively obtaining unknown attribute weights, which is then extended to accommodate decision situations with incomplete attribute weighting information. And also based on the generalized cross-entropy measure, an integrated algorithm is developed for ensurement of unknown expert weights by fusing two optimization models: one is to maximize divergence of decision matrices from positive or negative ideal decision matrix, and the other is to minimize similarity degree between individual decision matrices. Then based on the aforeproposed models, an approach is constructed for MAGDM problems with unknown attribute and expert weights under IVIFEs. Finally, case study on a simplified but representative supplier selection problem is carried out, and comparative experiments indicate the practicality and effectiveness of proposed methods.

A trust induced recommendation mechanism for reaching consensus in group decision making

The visual trust relationship is constructed.A trust induced recommendation mechanism is investigated.It arrives at the threshold value with the high harmony degree simultaneously.An interval-valued trust decision making space is developed to model uncertainty. This article addresses the inconsistency problem in group decision making caused by disparate opinions of multiple experts. To do so, a trust induced recommendation mechanism is investigated to generate personalised advices for the inconsistent experts to reach higher consensus level. The concept of trust degree (TD) is defined to identify the trusted opinion from group experts, and then the visual trust relationship is built to help experts see their own trust preferences within the group. Consequently, trust based personalised advices are generated for the inconsistent experts to revisit their opinions. To model the uncertainty of experts, an interval-valued trust decision making space is defined. It includes the novel concepts of interval-valued trust functions, interval-valued trust score (IVTS) and interval-valued knowledge degree (IVKD). The concepts of consensus degree (CD) between an expert and the rest of experts in the group as well as the harmony degree (HD) between the original opinion and the revised opinion are developed for interval-valued trust functions. Combining HD and CD, a more reasonable policy for group consensus is proposed as it should arrive at the threshold value with the maximum value of harmony and consensus degrees simultaneously. Furthermore, because the trust induced recommendation mechanism focuses on changing inconsistent opinions using only opinions from the trusted experts and not from the distrusted ones, the HD based changes cost to reach the threshold value of consensus is lower than previous mechanisms based on the average of the opinion of all experts. Finally, once consensus has been achieved, a ranking order relation for interval-valued trust functions is constructed to select the most appropriate alternative.

A linear programming model for determining ordered weighted averaging operator weights with maximal Yager's entropy

It has a wide attention about the methods for determining OWA operator weights. At the beginning of this dissertation, we provide a briefly overview of the main approaches for obtaining the OWA weights with a predefined degree of orness. Along this line, we next make an important generalization of these approaches as a special case of the well-known and more general problem of calculation of the probability distribution in the presence of uncertainty. All these existed methods for dealing these kinds of problems are quite complex. In order to simplify the process of computation, we introduce Yagers entropy based on Minkowski metric. By analyzing its desirable properties and utilizing this measure of entropy, a linear programming (LP) model for the problem of OWA weight calculation with a predefined degree of orness has been built and can be calculated much easier. Then, this result is further extended to the more realistic case of only having partial information on the range of OWA weights except a predefined degree of orness. In the end, two numerical examples are provided to illustrate the application of the proposed approach.

An effective multiagent evolutionary algorithm integrating a novel roulette inversion operator for engineering optimization

Multiagent systems have been studied and widely used in the field of artificial intelligence and computer science to catalyze computation intelligence. In this paper, a multiagent evolutionary algorithm called RAER based on the ERA multiagent modeling pattern is proposed, where ERA has the same architecture as Swarm including three parts of Environment, Reactive rules and Agents. RAER integrates a novel roulette inversion operator (RIO) proposed in this paper and theoretically proved to conquer the irrationality of the inversion operator (IO) designed by John Holland when used for real code stochastic optimization algorithms. Experiments for numerical optimization of 4 benchmark functions show that the RIO operator bears better functioning than IO operator. And experiments for numerical optimization of 12 benchmark functions are used to examine the performance and scalability of RAER along the problem dimensions ranging 20-10000, results indicate that RAER outperforms other comparative algorithms significantly. Also, two engineering optimization problems of a stable linear system approximation and a welded beam design are used to examine the applicability of RAER. Results show that RAER has better search ability and faster convergence speed. Especially for the approximation problem, REAR can find the proper optima belonging to different fixed search areas, which is significantly better than other algorithms and shows that RAER can search the problem domains more thoroughly than other algorithms. Hence, RAER is efficient and practical.

Aggregation Operators on Triangular Intuitionistic Fuzzy Numbers and its Application to Multi-Criteria Decision Making Problems

Abstract The aim of this work is to present some aggregation operators with triangular intuitionistic fuzzy numbers and study their desirable properties. Firstly, the score function and the accuracy function of triangular intuitionistic fuzzy number are given, the method for ranking triangular intuitionistic fuzzy numbers are developed. Then, some geometric aggregation operators for aggregating triangular intuitionistic fuzzy numbers are developed, such as triangular intuitionistic fuzzy weighted geometric (TIFWG) operator, the triangular intuitionistic fuzzy ordered weighted geometric (TIFOWG) operator and the triangular intuitionistic fuzzy hybrid geometric (TIFHG) operator. Moreover, an application of the new approach to multi-criteria decision making method was proposed based on the geometric average operator of TIFNs, and the new ranking method for TIFNs is used to rank the alternatives. Finally, an example analysis is given to verify and demonstrate the practicality and effectiveness of the proposed method.

Multiple attribute group decision making based on generalized power aggregation operators under interval-valued dual hesitant fuzzy linguistic environment

This paper aims to investigate the type of fuzzy multiple attribute group decision making (MAGDM) where arguments being aggregated are allowed to support each other. In order to enable decision makers to express their preferences more comprehensively, we firstly put forward a hybrid tool, an interval-valued dual hesitant fuzzy linguistic set (IVDHFLS), which employs interval-valued hesitant membership and nonmembership degrees to assess linguistic terms. Basic operational laws for IVDHFLS are discussed, also a distance measure is designed to overcome irrationality in traditional methodology for hesitant fuzzy sets, i.e., artificially adding values to mismatching membership or nonmembership degrees. We next develop fundamental generalized power average aggregation operators for IVDHFLS, including power average operator, power geometric average operator, power ordered weighted average operator and power ordered weighted geometric average operator. Desirable properties and special cases of these aggregation operators are further analyzed. Furthermore, based on the generalized operators above, we construct two approaches for MAGDM with mutually supportive arguments being aggregated under interval-valued dual hesitant fuzzy linguistic environments. Finally, case studies are conducted to verify effectiveness and practicality of the developed approaches.

Attitudinal ranking and correlated aggregating methods for multiple attribute group decision making with triangular intuitionistic fuzzy Choquet integral

Purpose – The purpose of this paper is to propose novel attitudinal prioritization and correlated aggregating methods for multiple attribute group decision making (MAGDM) with triangular intuitionistic fuzzy Choquet integral. Design/methodology/approach – Based on the continuous ordered weighted average (COWA) operator, the triangular fuzzy COWA (TF-COWA) operator is defined, and then a novel attitudinal expected score function for triangular intuitionistic fuzzy numbers (TIFNs) is investigated. The novelty of this function is that it allows the prioritization of TIFNs by taking account of the expert’s attitudinal character. When the ranking order of TIFNs is determined, the triangular intuitionistic fuzzy correlated geometric (TIFCG) operator and the induced TIFCG (I-TIFCG) operator are developed. Findings – Their use is twofold: first, the TIFCG operator is used to aggregate the correlative attribute value; and second, the I-TIFCG operator is designed to aggregate the preferences of experts with some de...

Some Generalized Dependent Aggregation Operators with Interval-Valued Intuitionistic Fuzzy Information and Their Application to Exploitation Investment Evaluation

We investigate multiple attribute group decision making (MAGDM) problems with arguments taking the form of interval-valued intuitionistic fuzzy numbers. In order to relieve influence of unfair arguments, a Gaussian distribution-based argument-dependent weighting method and a hybrid support-function-based argument-dependent weighting method are devised by, respectively, measuring support degrees of arguments indirectly and directly, based on which the Gaussian generalized interval-valued intuitionistic fuzzy ordered weighted averaging operator (Gaussian-GIIFOWA) and geometric operator (Gaussian-GIIFOWG), the power generalized interval-valued intuitionistic fuzzy ordered weighted averaging (P-GIIFOWA) operator and geometric (P-GIIFOWA) operator are proposed to generalize a wide range of aggregation operators for decision makers to flexibly choose in decision modelling. And some desirable properties of the proposed operators are also analyzed. Further, application of an approach integrating proposed operators to exploitation investment evaluation of tourist spots has shown the effectiveness and practicality of developed methods; experimental results also verify the properties of proposed operators.

Multi-criteria group decision making method based on generalized intuitionistic trapezoidal fuzzy prioritized aggregation operators

In this paper, we investigate the intuitionistic trapezoidal fuzzy multi-criteria group decision making problems in which decision criteria and the decision makers are in different priority levels. Motivated by the idea of generalized aggregation operators and prioritized aggregation operators, we propose some new prioritized aggregation operator called generalized intuitionistic trapezoidal fuzzy prioritized weighted average operator and generalized intuitionistic trapezoidal fuzzy prioritized weighted geometric operator. Then some desired properties of the new aggregation operators are studied and their special cases are also examined. Further, we apply the proposed operators to construct an approach for multi-criteria group decision making under intuitionistic trapezoidal fuzzy environment. Finally, a practical study on introduction of talents is carried out to verify the developed methods.