In terms of the concepts of state and state transition, a new heuristic random search algorithm named state transition algorithm is proposed. For continuous function optimization problems, four special transformation operators called rotation, translation, expansion and axesion are designed. Adjusting measures of the transformations are mainly studied to keep the balance of exploration and exploitation. Convergence analysis is also discussed about the algorithm based on random search theory. In the meanwhile, to strengthen the search ability in high dimensional space, communication strategy is introduced into the basic algorithm and intermittent exchange is presented to prevent premature convergence. Finally, experiments are carried out for the algorithms. With 10 common benchmark unconstrained continuous functions used to test the performance, the results show that state transition algorithms are promising algorithms due to their good global search capability and convergence property when compared with some popular algorithms.

Feature selection problems often appear in the application of data mining, which have been difficult to handle due to the NP-hard property of these problems. In this study, a simple but efficient hybrid feature selection method is proposed based on binary state transition algorithm (BSTA) and ReliefF, called ReliefF-BSTA. This method contains two phases: the filter phase and the wrapper phase. There are three aspects of advantages in this method. First, an initialization approach based on feature ranking is designed to make sure that the initial solution is not easy to get tapped into local optimum. Then a probability substitute operator based on feature weights is developed to update the current solution according to the different mutation probabilities of the features. Finally, a new selection strategy based on relative dominance is presented to find the current best solution. The simple and efficient algorithm $k$ -Nearest Neighborhood ( $k$ -NN) with the leave-one-out cross validation is used as a classifier to evaluate feature subset candidates. The experimental results indicate that the proposed method is more efficient in terms of the classification accuracy through a comparison to other feature selection methods using seven public datasets and several real biomedical datasets. For public datasets, the proposed method improved the classification average accuracy by about 2.5% compared with the filter method. For a specific biomedical dataset AID1284, the classification accuracy significantly increased from 77.24% to 85.25% by using the proposed method.

In recent years, machine learning, especially deep learning, has developed rapidly and has shown remarkable performance in many tasks of the smart grid field. The representation ability of machine learning algorithms is greatly improved, but with the increase of model complexity, the interpretability of machine learning algorithms is worse. The smart grid is a critical infrastructure area, so machine learning models involving it must be interpretable in order to increase user trust and improve system reliability. Unfortunately, the black-box nature of most machine learning models remains unresolved, and many decisions of intelligent systems still lack explanation. In this paper, we elaborate on the definition, motivations, properties, and classification of interpretability. In addition, we review the relevant literature addressing interpretability for smart grid applications. Finally, we discuss the future research directions of interpretable machine learning in the smart grid.

The state transition algorithm (STA) has been emerging as a novel metaheuristic method for global optimization over the past few years. In our previous study, the parameter of transformation operator in continuous STA is kept constant or decreasing itself in a periodical way. In this paper, the optimal parameter selection of operators in continuous STA is taken into consideration. First, a statistical study with four benchmark 2-D functions is conducted to show how these parameters affect the search ability of the STA. Based on the experience gained from the statistical study, then, a new continuous STA with optimal parameter strategy is proposed to accelerate its search process. The proposed STA is successfully applied to 12 benchmarks with 20-D, 30-D, and 50-D space. A comparison with other metaheuristics has also demonstrated the effectiveness of the proposed method.