An anode effect often occurs during the process of aluminum electrolysis that will cause large energy consumption and low efficiency in aluminum production, thus how to identify the anode effect in advance has become an important issue. However, traditional approaches ignore the common incomplete information problem existing in the acquired data, and only consider a single predicting time, resulting in an unreliable result in anode effect prediction. In this paper, a hybrid prediction approach based on a singular value thresholding and extreme gradient boosting (SVT-XGBoost) approach is proposed to identify the anode effect in the aluminum electrolysis process. The SVT is used for data filling by the whole-features transformation, and the XGBoost is utilized for classification of the anode effect. The predicting time is set to 10 min by the comparison. The experimental results show that the proposed approach has an effective ability for anode effect classification using the SVT-XGBoost compared to the previous approaches. Here, the effect of the training sample number is also investigated. The proposed approach could be applied in real-time anode effect prediction in the future.

Path planning is an important problem in the field of unmanned aerial vehicles (UAVs). However, it is inefficient for many rapidly-exploring random tree (RRT) based methods to rapidly find a feasible solution in a complex environment. To solve the path planning problem for the UVA in a complex environment, an improved dynamic step size RRT algorithm combined with a new path length control strategy is proposed. Firstly, the algorithm adopts a biased-goal sampling strategy to guide the growth of the tree, and an expansion direction constraint strategy is adopted to limit the expansion direction of the tree. Secondly, an improved dynamic step size strategy is proposed to speed up the path searching process. Thirdly, a path length constraint strategy is designed, which is used to constrain the path length during the searching process for a solution. Finally, simulation results demonstrate that the proposed method achieves improvement in both computational time and the path quality.

No abstract

No abstract

Two-sided assembly line balancing problem (TALBP) is a vital design problem for the industries. In real production process, some complex constraints should be considered in the two-sided assembly line. To solve the practical TALBP, this paper proposes a hybrid algorithm (HABC) that combines the artificial bee colony (ABC) algorithm and late acceptance hill-climbing (LAHC) algorithm. In the proposed algorithm, a well-designed decoding scheme is embedded to tackle multiple assignment constraints. Moreover, two neighborhood search strategies are implemented by employed bee and onlooker bee to explore and exploit the new solution. A set of computational experiments is performed on benchmark problems. The comparison results, best solutions, standard deviation and relative percentage index demonstrate that the HABC algorithm outperforms other algorithms published in the literature and finds 5 brand new solutions for 15 instances.