This work investigates the space-limited aircraft assembly scheduling problem (SAASP) based on real-world cases. A computational model, minimizing the makespan, is developed to formulate the complex operational relationships and space constraints of the workstations in SAASP. To address SAASP, we propose a reinforcement and statistical learning-assisted evolutionary algorithm (RSLEA). First, hierarchical encoding and queue decoding methods are designed to capture the intricate sequencing and workstation space constraints. Second, a statistical learning strategy is implemented to accelerate the convergence of early exploration. Next, a reinforcement learning strategy is introduced to control the sampling size of the statistical learning process. Additionally, another reinforcement evolutionary learning strategy is developed for exploitation. Three crossover operators are employed, with their computational resources adaptively allocated by the agent. To validate the performance of the proposed algorithm, RSLEA is tested on 520 instances and three real-world cases from a Chinese aircraft manufacturing factory. These cases are the assembly workstations of wings, partial bodies, and tails of aircraft, which are critical and complex. The RSLEA is compared with state-of-the-art algorithms and obtains better performance on 89% instances, and reduces the average gap to the baseline by 2.72%. Moreover, RSLEA achieves the best solution and overall performance in real-world cases.

This study addresses the lot-streaming hybrid job shop scheduling problem with variable sublots (LHJSV), inspired by a real-world aircraft tooling shop. A computational model is developed to represent the complex scheduling processes of the tooling shop. To solve this problem, we propose an automatic memetic algorithm enhanced by a heuristic designed with the assistance of a large language model (LLM). The approach is designed as follows: first, a memetic computing framework with automated algorithmic design is proposed for LHJSV. Second, a cooperative evolutionary heuristic framework based on problem decomposition is introduced, enabling the LLM to comprehend the LHJSV characteristics and generate feasible algorithms. Third, problem-specific prompts for LHJSV are carefully designed to guide the LLM. To evaluate the effectiveness of the proposed method, 20 benchmark instances derived from the Taillard dataset and a real-world case involving 575 operations are utilized. The proposed algorithm is compared against three swarm-based algorithms, an end-to-end method, and an LLM-based algorithm. Experimental results demonstrate that our method outperforms the compared algorithms on 85% of benchmark instances and exhibits significant superiority in real-world scenarios.

Detecting leaks in natural gas pipelines using acoustic signals typically requires extensive prior knowledge and complex parameter designs, making it challenging to handle background noise and data distribution disparities simultaneously. This article proposes a dual-feature drift framework utilizing a nonparametric design approach for acoustic signal-based leak detection. This framework consists of two core technologies: first, feature backward normalization. Low-dimensional drift factors are designed based on transformed acoustic signals to exponentially normalize the time-periodic features of the signal feature matrix, thereby eliminating strong background noise. Second, constructing the feature drift layer within a one-dimensional convolutional neural network. Weighted parameters constrain a high-dimensional feature matrix, developing drift factors that perform exponential drift on each feature, thus enhancing gradient constraints and eliminating data distribution differences during model training. This framework achieves a fault identification accuracy of 95.46% for natural gas pipeline leaks, outperforming competing methods and representing a novel approach to intelligent pipeline leak detection.

Predicting the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is critical for early intervention. Towards this end, various deep learning models have been applied in this domain, typically relying on structural magnetic resonance imaging (sMRI) data from a single time point whereas neglecting the dynamic changes in brain structure over time. Current longitudinal studies inadequately explore disease evolution dynamics and are burdened by high computational complexity. This paper introduces a novel lightweight 3D convolutional neural network specifically designed to capture the evolution of brain diseases for modeling the progression of MCI. First, a longitudinal lesion feature selection strategy is proposed to extract core features from temporal data, facilitating the detection of subtle differences in brain structure between two time points. Next, to refine the model for a more concentrated emphasis on lesion features, a disease trend attention mechanism is introduced to learn the dependencies between overall disease trends and local variation features. Finally, disease prediction visualization techniques are employed to improve the interpretability of the final predictions. Extensive experiments demonstrate that the proposed model achieves state-of-the-art performance in terms of area under the curve (AUC), accuracy, specificity, precision, and F1 score. This study confirms the efficacy of our early diagnostic method, utilizing only two follow-up sMRI scans to predict the disease status of MCI patients 24 months later with an AUC of 79.03%.

This work addresses the flexible job shop scheduling with finite skilled workers, extending classical flexible job shop scheduling by incorporating operation decomposition, finite worker, and worker transfer. These new problem features significantly increase the complexity of solving, as several operations requiring multiple workers can lead to worker competition, causing delays in other operations that depend on the same workers. Previous studies focused on either operation decomposition or worker transfer but did not address the issue of worker competition. To tackle this challenging optimization problem, we propose a knowledge-guided hierarchical evolutionary algorithm (KHEA) with multiview cooperative neighborhood search. The key contributions of this work are as follows: 1) a hierarchical solving framework is proposed to reduce the solving difficulty. This problem is decomposed into three levels. The first level ignores the worker assignment and the second level starts optimizing it. The final level then refines the global solution; 2) a knowledge-guided crossover operator with a feedback schema is designed to improve the efficiency of crossover operations; and 3) a multiview cooperative neighborhood search strategy is proposed to reduce the idle time caused by worker competition. This involves designing a new disjunctive graph that accounts for worker competition to identify the critical path. The information from both machine-view and worker-view Gantt charts is cooperatively utilized to minimize idle time. Our method, KHEA, was tested on two benchmarks across 28 instances and 16 large-scale instances, with equal running time for comparisons. Compared to state-of-the-arts, KHEA obtains significant superiority.

Collaboratively optimizing operational parameters through leveraging accumulated production experience is an innovative approach to reducing energy consumption in aluminum electrolysis cells (AECs). Due to the dynamic heterogeneity of various AECs, an explicit evolutionary multitasking (EMT) framework capable of incorporating different optimizers, has the potential to tackle this challenge effectively. However, there is a notable gap in theoretical research on multitasking collaborative evolutionary algorithms specifically applied to AECs. Meanwhile, existing explicit EMT algorithms often overlook the intertask correlation of feature information extracted in isolation from individual tasks. These issues significantly limit the development of synergistic effects in multitasking optimization for addressing parameter design in AECs. To address these limitations, this work proposes an explicit evolutionary framework with multitasking feature fusion (EMFF). This framework thoroughly considers the potential connections among feature information from different tasks. It achieves effective knowledge transfer by the design of a unique multitasking feature fusion mechanism, which enhances the information value of source tasks for target tasks. Furthermore, a transfer individual derivation (TID) strategy is introduced to ensure the rapid evolution of critical knowledge. Finally, comprehensive components and designed process are presented. Experimental results demonstrate EMFF's exceptional performance in various benchmark tests and real-world AEC parameter optimization cases.

Abstract Improving current efficiency and reducing energy consumption are two important technical goals of the electrolytic aluminum process (EAP). However, because the process involves complex noise characteristics (i.e., unknown types, redundant distributions and variable forms), it is very difficult to accurately develop a multiobjective prediction model. To overcome this problem, in this paper, a novel framework of multiobjective incremental learning based on a multi-source filter neural network (MSFNN) is presented. The proposed framework first presents a “multi-source filter” (MSF) technique that utilizes the mean and variance in the unscented Kalman filter (UKF) to guide the importance function of the particle filter (PF) based on a density kernel estimation method. Then, the MSF is embedded in the mutated neural network to adjust weights in real time. Third, weights are calculated and normalized by a modified importance function, which is the basis for further optimizing a secondary sampling based on sampling importance resampling (SIR). Finally, the incremental learning model with two objectives (i.e., process power consumption and current efficiency) based on the MSFNN in the EAP is established. The presented framework has been verified by the real-world EAP and some closely related methods. All test results indicate that the MSFNN’s relative prediction errors of the above two objectives are controlled within 0.51% and 0.38%, respectively and prove that MSFNN has significant competitive advantages over other recent filtering network models. Successfully establishment of the proposed framework provides a model foundation for multiobjective optimization problems in the EAP.

In multiobjective optimization, it is generally known that the boom in computational complexity and search spaces came with a rise in the number of objectives, and this leads to a decrease in selection pressure and the deterioration of the evolutionary process. It follows then that the many-objective optimization problem (MaOP) has become one of the most challenging topics in the field of intelligent optimization. Recently, the multifactorial evolutionary algorithm (MFEA) and its variations, which have shown excellent performance in knowledge transfer across related problems, may provide a new and effective way for solving MaOPs. In this article, a novel MFEA based on improved dynamical decomposition (MFEA/IDD), which integrates the advantages of multitasking optimization and decomposition-based evolutionary algorithms, is proposed. Specifically, in the improved dynamical decomposition strategy (IDD) method, the bi-pivot strategy is designed to provide a good mechanism for balancing between convergence and diversity instead of the single-pivot strategy. Furthermore, a novel MFEA-based approach embedding the IDD strategy is developed to reduce the total running time for solving multiple MaOPs simultaneously. Compared with seven state-of-the-art algorithms, the efficacy of our proposed method is validated experimentally on the benchmarks WFG, DTLZ, and MAF with three to ten objectives, along with a series of real-world cases. The results reveal that the MFEA/IDD is well placed in balancing convergence and diversity while reducing the total number of function evaluations for solving MaOPs.

As a newly emerging multivariate statistical process monitoring method, non-negative matrix factorization (NMF) and its variants avoid the positive and negative cancellations between the extracted features because of the purely additive combination of non-negative components. Thus, they make a good match with this reality that the negative values of both observations and decomposed components are physically meaningless in many kinds of industrial processes. However, these methods are effective only for linearly separable problems and are not suitable for dealing with nonlinear process monitoring. In this article, the kernel-based method is integrated into the projective NMF (KPNMF) to improve the accuracy of fault detection, and the appropriate multiplicative update method is proven to be convergent. Furthermore, inspired by the false nearest neighbors (FNNs) method, a new dimensionality reduction approach (KPNMF-FNN) is presented to further reduce the original variables for determining the smallest dimension regression vector needed. Compared with the traditional methods, the proposed approach can greatly reduce the time and storage space required on the premise of maintaining a high fault detection rate and low false alarm rate. The experimental results on the Tennessee Eastman benchmark process and the pumping unit system show that the proposed algorithms have excellent performance and can effectively detect faults under the circumstances of retaining only the top 60% of the original variables.

Various real-world applications with interval uncertainty, such as the path planning of mobile robot, layout of radio frequency identification readers and solar desalination, can be formulated as an interval multiobjective optimization problem (IMOOP), which is usually transformed into one or a series of certain problems to solve by using evolutionary algorithms. However, a definite characteristic among them is that only a single optimization task can be catched up at a time. Inspired by the multifactorial evolutionary algorithm (MFEA), a novel interval MFEA (IMFEA) is proposed to solve IMOOPs simultaneously using a single population of evolving individuals. In the proposed method, the potential interdependency across related problems can be explored in the unified genotype space, and multitasks of multiobjective interval optimization problems are solved at once by promoting knowledge transfer for the greater synergistic search to improve the convergence speed and the quality of the optimal solution set. Specifically, an interval crowding distance based on shape evaluation is calculated to evaluate the interval solutions more comprehensively. In addition, an interval dominance relationship based on the evolutionary state of the population is designed to obtain the interval confidence level, which considers the difference of average convergence levels and the relative size of the potential possibility between individuals. Correspondingly, the strict transitivity proof of the presented dominance relationship is given. The efficacy of the associated evolutionary algorithm is validated on a series of benchmark test functions, as well as a real-world case of robot path planning with many terrains that provides insight into the performance of the method in the face of IMOOPs.

Improvements in the production and energy consumption of the aluminum electrolysis process (AEP) directly depend on the operating parameters of the electrolytic cell. To balance the conflicting goals of efficiency and productivity with reduced energy consumption and emissions, AEP operating parameter optimization is formulated as a constrained multiobjective optimization problem with competing objectives of current efficiency and cell voltage. Then, the improved multiobjective quantum-behaved particle swarm optimization (IMQPSO) algorithm is proposed. The application of an adaptive opposition-based learning strategy and a piecewise Gauss mutation operator can increase the diversity of the population and enhance the global search ability of the IMQPSO. To expand the creativity of the particles, two iterative methods of the mean best position with weighting and the attractor position are redesigned. Experimental analyses are conducted for the benchmark problems and a real case to verify the effectiveness of the proposed method.