The stochastic configuration network (SCN), a type of randomized learning algorithm, can solve the infeasible problem in random vector functional link (RVFL) by establishing a supervisory mechanism. The advantages of fast learning, convergence and not easily falling into local optima make SCN popular. However, the prediction effect of SCN is affected by the parameter settings and the nonstationarity of input data. In this paper, a hybrid model based on variational mode decomposition (VMD), improved whale optimization algorithm (IWOA), and SCN is proposed. The SCN will predict relatively stable data after decomposition by VMD, and parameters of SCN are optimized by IWOA. The IWOA diversifies the initial population by employing logistic chaotic map based on bit reversal and improves the search ability by using Lévy flight. The exploration and exploitation of IWOA are superior to those of other optimization algorithms in multiple benchmark functions and CEC2020. Moreover, the proposed model is applied to the prediction of the nonstationary wind speeds in four seasons. We evaluate the performance of the proposed model using four evaluation indicators. The results show that the R 2 of the proposed model under four seasons are more than 0.999, and the root mean square error, mean absolute error, and symmetric mean absolute percentage error are less than 0.3, 0.17, and 13%, respectively, which are almost 1/10, 1/10, and 1/4 those of SCN, respectively.

Probabilistic linguistic term set (PLTS), an efficient tool to describe decision information, can sufficiently express decision makers' hesitation and preference. Probabilistic linguistic preference relation (PLPR) is based on PLTSs to describe the preference information of experts for paired alternatives. However, in practice, due to the complexity of the problem, the incompleteness of information and the lack of professional knowledge, the incomplete PLPR (InPLPR) with missing information often appears. Therefore, this paper proposes a decision-making method under InPLPR. Firstly, in order to fully consider the specific situation of missing values, missing linguistic term-InPLTS (MLT-InPLTS) is subdivided into missing single linguistic term-InPLTS (MSLT-InPLTS) and missing multiple linguistic terms-InPLTS (MMLT-InPLTS). Then, a two-stage mathematical optimization model of missing information estimation based on additive consistency, fuzzy entropy and hesitation entropy is established. Subsequently, aiming at the unacceptable consistency of complete PLPR (CPLPR) after filling in the missing values, a consistency improvement method based on the idea of gradient descent is proposed. Afterward, probabilistic linguistic weighted averaging (PLWA) operator is used to rank alternatives. Finally, medical supplier selection is taken as an example to verify the effectiveness of the proposed decision-making method, and the robustness and advantages of this method are illustrated by sensitivity analysis and comparison with other methods.

With the attention of people to environmental and health issues, health-care waste (HCW) management has become one of the focus of researchers. The selection of appropriate HCW treatment technology is vital to the survival and development of human beings. In the assessment process of HCW disposal alternative, the evaluation information given by decision makers (DMs) often has uncertainty and ambiguity. The expression, transformation and integration of this information need to be further studied. We develop an applicable decision support framework of HCW treatment technology to provide reference for relevant staff. Firstly, the evaluation information of DMs is represented by interval 2-tuple linguistic term sets (ITLTs). To effectively express qualitative information, the cloud model theory is used to process the linguistic information, a novel concept of interval 2-tuple linguistic integrated cloud (ITLIC) is proposed, and the relevant operations, distance measure and possibility degree of ITLICs are defined. Moreover, a weighted Heronian mean (HM) operator based ITLIC is presented to fuse cloud information. Secondly, the HCW treatment technology decision support model based on the BWM and PROMETHEE is established. Finally, the proposed model is demonstrated through an empirical example, and the effectiveness and feasibility of the model is verified by comparison with extant methods.