The complex industrial environment and poor data quality inherent in electrolytic copper foil production often lead to inaccurate energy consumption predictors, and consequently unreliable energy optimization. Given the difficulty in further improving the accuracy of energy consumption predictors, this article proposes a high-reliability stochastic prediction-based ensemble optimization (SPEO) method for energy saving of copper foil electrodeposition. The approach begins by introducing stochastic prediction to establish a standard stochastic prediction optimization framework, wherein filtering operations are applied to mitigate the adverse effects of prediction errors on the optimization process. Subsequently, reliability analysis methods are employed to derive reliability metrics based on optimization results generated by energy consumption predictors of varying accuracy. Finally, these metrics are comprehensively incorporated to integrate multiple optimizers, along with their respective optimal result feedback, thereby enhancing the generalizability and stability of the method. Experimental evaluations using real-world data from an industrial electrodeposition process demonstrate the superiority of the proposed SPEO method.

As a typical combinatorial optimization problem, the 3-path vertex cover problem has wide applications in practice. To solve the 3-path vertex cover problem from the perspective of distributed optimization, we treat each vertex as an agent (i.e., player) with computation, and decision-making capabilities. First, we establish a 3-player symmetric game model to describe the 3-path vertex cover problem, and design the corresponding cost function for each player. Then, we prove that under the established game model, strict Nash equilibriums (SNEs) act as the basis of the connection between 3-path vertex cover states and minimum 3-path vertex cover states. Next, we propose a novel memory-based synchronous learning (MSL) algorithm, where the initial profile strategy generation of players relies on the designed degree preference rule, and each player has a memory length for recording strategies and independently update their strategies concurrently based on the accessed local information. After that, we prove that our proposed MSL algorithm can guarantee that any strategy profile converges to an SNE, and provide a theoretical analysis of the algorithm’s complexity. Finally, we present numerous numerical simulations to demonstrate the performance of our proposed algorithm on various networks. Moreover, we find that increasing the memory length and adopting the degree preference initialization can yield a better SNE.

Feature selection is a key step in machine learning by eliminating features that are not related to the modeling target to create reliable and interpretable models. By exploring the potential complex correlations among features of unlabeled data, recently introduced self-supervision-enhanced feature selection greatly reduces the reliance on the labeled samples. However, they are generally based on the autoencoder with sample-wise self-supervision, which can hardly exploit the relations among samples. To address this limitation, this article proposes graph representation learning enhanced semi-supervised feature selection (G-FS) which performs feature selection based on the discovery and exploitation of the non-Euclidean relations among features and samples by translating unlabeled “plain” tabular data into a bipartite graph. A self-supervised edge prediction task is designed to distill rich information on the graph into low-dimensional embeddings, which remove redundant features and noise. Guided by the condensed graph representation, we propose a batch attention feature weight generation mechanism that generates more robust weights according to batch-based selection patterns rather than individual samples. The results show that G-FS achieves significant performance edges in 14 datasets compared to twelve state-of-the-art baselines, including two recent self-supervised baselines. The source code is public available at https://github.com/Icannotnamemyselff/G-FS_Graph_enhacned_feature_selection .

Understanding the environments through interactions has been one of the most important human intellectual activities in mastering unknown systems. Deep reinforcement learning (DRL) has already been known to achieve effective control through human-like exploration and exploitation in many applications. However, the opaque nature of deep neural network (DNN) often hides critical information about feature relevance to control, which is essential for understanding the target systems. In this article, a novel online feature selection framework, namely, the dual-world-based attentive feature selection (D-AFS), is first proposed to identify the contribution of the inputs over the whole control process. Rather than the one world used in most DRL, D-AFS has both the real world and its virtual peer with twisted features. The newly introduced attention-based evaluation (AR) module performs the dynamic mapping from the real world to the virtual world. The existing DRL algorithms, with slight modification, can learn in the dual world. By analyzing the DRL's response in the two worlds, D-AFS can quantitatively identify respective features' importance toward control. A set of experiments is performed on four classical control systems in OpenAI Gym. Results show that D-AFS can generate the same or even better feature combinations than the solutions provided by human experts and seven recent feature selection baselines. In all cases, the selected feature representations are closely correlated with the ones used by underlying system dynamic models.

Multiagent dynamic task assignment of forest fires is a complicated optimization problem because it requires the consideration of multiple factors, such as the spread speed of fires, firefighting speed of agents, the movement speed of agents, and the number of deployed agents. In this article, we investigate multiagent dynamic task assignment based on a forest fire point model, the objective of which is to minimize task completion time. First, we establish a model for the spread of fire and dynamic task assignments. Second, we prove that the optimal static task assignment always makes all task completion times the same under certain assumptions. Furthermore, we calculate the optimal solution to the static task assignment problem assuming no travel time for the agents, which provides the theoretical basis for the initial deployment and dynamic deployment. Third, we propose a dynamic task assignment scheme based on the global information, which ensures that every reassignment reduces the task completion time and makes all task completion times close to each other. Finally, the simulation is carried out on the MATLAB platform to verify the performance of the proposed dynamic task assignment scheme by comparing with a multistage global auction algorithm. We hope that this work provides insight for decision-makers designing reasonable assignment strategies based on the model and solving assignment optimization problem in different situations.Note to practitioners—The forest firefighting problem considered in this article is a typical multitask and multistage optimization problem. Many searching algorithms for multistage optimization problem are available in the existing literature. However, one of the main challenges is that the time of searching increases exponentially with the number of stages. This work first proves that the tasks are completed in the minimum amount of time, under the constraint of one-shot assignment. This finding helps us to evaluate the gap between the searching algorithm and the optimal solution. In addition, in practice, if the underlying dynamic process can be modeled or partially modeled, then we can predict the behavior of future stages and reduce the searching domain. If a model is available, then we can also adjust the assignment scheme dynamically based on the principle that each adjustment would reduce the total time of tasks completion. In this article, we establish a dynamical fire-spreading model and propose a model-based solution to the multistage optimization problems. The findings in this work can serve as a supplement to the existing optimization algorithms.

Electric vehicles (EVs) have experienced a rapid growth due to the economic and environmental benefits. However, the substantial charging load brings challenging issues to the power grid. Modern technological advances and the huge number of high-rise buildings have promoted the development of distributed energy resources, such as building integrated/mounted wind turbines. The issue to coordinate EV charging with locally generated wind power of buildings can potentially reduce the impacts of EV charging demand on the power grid. As a result, this paper investigates this important problem and three contributions are made. First, the real-time scheduling of EV charging is addressed in a centralized framework based on the ideas of model predictive control, which incorporates the volatile wind power supply of buildings and the random daily driving cycles of EVs among different buildings. Second, an EV-based decentralized charging algorithm (EBDC) is developed to overcome the difficulties due to: 1) the possible lack of global information regarding the charging requirements of all EVs and 2) the computational burden with the increasing number of EVs. Third, we prove that the EBDC method can converge to the optimal solution of the centralized problem over each planning horizon. Moreover, the performance of the EBDC method is assessed through numeric comparisons with an optimal and two heuristic charging strategies (i.e., myopic and greedy). The results demonstrate that the EBDC method can achieve a satisfactory performance in improving the scalability and the balance between the EV charging demand and wind power supply of buildings.Note to Practitioners—This paper is motivated by the challenging problem due to the substantial charging load of electric vehicles (EVs) on the power grid. Nowadays, modern technological advances and the rapid increase of high-rise buildings have promoted the development of building integrated/mounted wind turbines. As the EVs are usually parked in buildings for a large proportion of time every day, the issue to best utilize locally generated wind power of buildings to suffice EV travelling requirements shows vital significance in reducing their dependence on the power grid. However, there exist two main challenges including: 1) the multiple uncertainties regarding the uncertain wind power generation and the random driving behaviors of EVs and 2) the scalability of the solution method. To tackle the first challenge, the idea of model predictive control is introduced to make charging decisions at each stage based on a short-term prediction of the on-site wind power and the current collection of EVs parked there. To consider the scalability and overcome the lack of global charging information of all EVs in practical deployment, an iterative EV-based decentralized charging algorithm (EBDC) is derived, in which each EV can dynamically update its own charging decisions according to a dynamic charging “price” announced by the buildings. Alternatively, the buildings dynamically adjust the charging “price” to motivate the EVs to get charged during the time periods with sufficient wind power supply. Numeric results demonstrate that the EBDC method is scalable and performs well in improving the balance between the EV charging demand and the wind power supply of buildings.