The power Heronian aggregation (PHA) operator can use the advantages of power average and the Heronian mean operator, which together could take into account the interrelationship of the aggregated arguments, and therefore alleviate the effects caused by unreasonable data through considering the support degree between input arguments. However, PHA operators cannot be used to process single-valued neutrosophic numbers (SVNNs), which is significant for extending it to SVNNs. We propose some new PHA operators for SVNNs and introduce a novel MAGDM method on the basis of the proposed operators. Firstly, the definition, properties, comparison method, and operational rules of SVNNs are introduced briefly. Then, some PHA operators are proposed, such as the single-valued neutrosophic power Heronian aggregation (SVNPHA) operator, the single-valued neutrosophic weighted power Heronian aggregation (SVNWPHA) operator, single-valued neutrosophic geometric power Heronian aggregation (SVNGPHA) operator, single-valued neutrosophic weighted geometric power Heronian aggregation (SVNWGPHA) operator. Furthermore, we discuss some properties of these new aggregation operators and several special cases. Moreover, the method to solve the MAGDM problems with SVNNs is proposed, based on the SVNWPHA and SVNWGPHA operators. Lastly, we verified the application and effectiveness of the proposed method by using an example for the MAGDM problem.

Finding the optimal black-start alternative plays an important role in speeding up the restoration process of a power system after a complete blackout. In this article, a clustering method, affinity propagation, is adopted to deal with the black-start decision making problem. A novel black-start decision making method based on affinity propagation and TOPSIS is proposed. The standard deviation method is used to calculate the weights of the indexes, and a weighted normalized decision matrix is constructed. Then affinity propagation is used to cluster the black-start schemes of the weighted normalized decision matrix, and the optimal cluster is determined. Finally, for each black-start scheme in the optimal cluster, the relative closeness value is computed according to the TOPSIS method, and the best black-start scheme is selected. Compared with the existing black-start decision making methods, the proposed method can not only rank the black-start schemes but also determine which grade each black-start scheme should belong to. Based on the data of an actual power system, experiments were carried out to evaluate the performance of the proposed method. The results show that the proposed method is effective.

To address the sparsity problem, a novel collaborative filtering approach based on multiple attribute decision making (MADM-CF) is proposed. In MADM-CF, users in collaborative filtering are treated as decision alternatives, items are treated as attributes. The weight of each item is determined, and the preference similarities between the active user and other users are computed. The preference similarity means that how the users’ preferences are similar on positive ratings and negative ratings. According to the preference similarities, the candidate neighbourhood of the active user is determined. A method to compute overall assessment value is designed, the overall assessment value of each user in the candidate neighbourhood is computed, and users with the smallest overall assessment values are selected as the active user’s nearest neighbours. Finally, the most frequent item recommendation method (MFIR) is used to provide top-N recommendations to the active user. Experimental results based on MovieLens and Netflix datasets show that the proposed approach is superior to existing alternatives.

Black-start decision making plays an important role in power system restoration after a large-area blackout. In this article, information-theoretic co-clustering is employed to deal with the black-start decision-making problem with incomplete information. A novel black-start decision making method based on information-theoretic co-clustering, called ITCC-BSDM, is proposed. In the proposed method ITCC-BSDM, information-theoretic co-clustering is used to simultaneously cluster the black-start schemes and indexes. Based on the scheme clusters and index clusters, the missing values in the decision matrix are predicted, and a complete decision matrix is constructed. Finally, the standard deviation method and the simple additive weighting method are used to determine the best black-start scheme. Based on the data of Guangdong power system of China, experiments were carried out to investigate the effectiveness of the proposed method. The results show that the proposed method even performs better than some black-start decision making methods considering complete information.

KSII Transactions on Internet and Information Systems TIIS Paper Details