Applied Soft Computing最新文献

{"title":"Adaptive multi-region prediction strategy for dynamic multi-objective optimization","authors":"Tao Zhang ,&nbsp;LinJun Yu ,&nbsp;HuiWen Yu","doi":"10.1016/j.asoc.2025.113072","DOIUrl":"10.1016/j.asoc.2025.113072","url":null,"abstract":"<div><div>This paper proposes a novel adaptive multi-region prediction strategy for dynamic multi-objective optimization problems (DMOPs), which aims to efficiently generate diverse populations in response to environmental changes and facilitate the exploration of the new Pareto front. The strategy consists of two main phases: predictive population initialization and elite-guided resampling. In the predictive population initialization phase, the strategy integrates global exploration and local exploitation. Global exploration divides the population into <span><math><mi>N</mi></math></span> subregions based on population distribution characteristics. For each subregion, the historical information of its center point is used to predict its new position in the next environment, and then a Gaussian mixture model (GMM) is used to sample new individuals based on the position information of all new center points. Local exploitation employs the K-Medoids method to cluster historical Pareto fronts and selects individuals corresponding to the medoids in the decision space as representative individuals. These representative individuals are then used to predict their new locations, followed by Gaussian sampling to generate individuals. The initial predicted population is formed by combining the individuals from global exploration, local exploitation, and randomly generated individuals. In the elite-guided resampling phase, the initial predicted population is evaluated, and top-ranked elite individuals are selected. These elites guide the generation of the final population through Gaussian sampling and Latin Hypercube Sampling (LHS), enhancing solution quality and diversity. The proposed strategy is validated on 14 benchmark problems using MIGD, MHV, R(IGD), and DMIGD metrics. Results demonstrate its better comprehensive performance under varying environmental change intensities (mild, moderate, and severe) compared to existing approaches. Furthermore, its application to a real-world PID controller tuning problem highlights the strategy’s practical potential, showcasing superior performance.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"176 ","pages":"Article 113072"},"PeriodicalIF":7.2,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Jointly leveraging 1D and 2D convolution on diachronic entity embedding for temporal knowledge graph completion","authors":"Mingsheng He,&nbsp;Lin Zhu,&nbsp;Luyi Bai","doi":"10.1016/j.asoc.2025.113144","DOIUrl":"10.1016/j.asoc.2025.113144","url":null,"abstract":"<div><div>Temporal knowledge graphs (TKGs) model knowledge that dynamically changes over time in the real world, providing effective support for temporal-aware artificial intelligence (AI) applications. However, existing TKGs are far from complete, and their incompleteness significantly affects the performance of downstream applications. Therefore, Temporal Knowledge Graph Completion (TKGC) has become a current research hotspot, which aims to reason potential missing facts based on existing ones. In the widely studied TKGC methods with the implicit representation of temporal information, existing methods that embed temporal information into entity representations can capture the temporal evolution of entities. However, they fail to take the behavioral characteristics of entities across different time units into account, making them challenging to precisely model the fine-grained dynamics of entities. Furthermore, given the powerful expressiveness of Convolutional Neural Networks (CNNs), some TKGC methods have employed the 1D convolution operation to capture global relationships within the embedded quadruple, enabling the learning of explicit knowledge in TKGs and attaining competitive performance for TKGC. Nevertheless, the non-linear and deep features embedded in the entity-relation interaction have not been insufficiently explored. To address these challenges, this paper proposes JointDE, a TKGC model that applies both 1D and 2D convolution operations to the generated diachronic entity embedding, which simultaneously learns the explicit and implicit knowledge in TKGs. The new diachronic entity embedding method explicitly models the inherent attributes of entities and integrates temporal features across different time units, thereby possessing the ability to capture fine-grained entity evolution. More importantly, we construct feature matrices and filters using diachronic entity embeddings and relation embeddings, leveraging an internal 2D convolution mechanism to expand their interactions. This is the first work to learn implicit knowledge embedded in TKGs from a local relationship perspective for TKGC. Experimental results demonstrate that JointDE surpasses several TKGC baseline methods and achieves state-of-the-art performance on three event-based benchmark datasets: ICEWS14, ICEWS05–15, and GDELT. Specifically, JointDE improves Mean Reciprocal Rank (MRR) by 3.17 % and Hits@1 by 5.87 % over the state-of-the-art baseline for entity reasoning.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"176 ","pages":"Article 113144"},"PeriodicalIF":7.2,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-supervised Transformer for 3D point clouds completion and morphology evaluation of granular particle","authors":"Haoran Zhang,&nbsp;Zhen-Yu Yin,&nbsp;Ning Zhang,&nbsp;Xiang Wang","doi":"10.1016/j.asoc.2025.113161","DOIUrl":"10.1016/j.asoc.2025.113161","url":null,"abstract":"<div><div>Determining the morphology characteristics of particles using 3D point cloud is promising and crucial for the quality inspection of granular materials. However, it remains challenging due to the cumbersome process and incomplete 3D point clouds obtained from laser scanning of particles. In this study, a novel intelligent method, named self-supervised transformer-based encoder and decoder model for granular materials (SSPoinTr-GM), is developed for the automatic completion of partially occluded 3D point clouds and morphology characteristics evaluation. The complete cloud points of 100 cobble and 100 gravel particles are first scanned to establish a benchmark 3D point cloud dataset. To form partial point clouds for training, the complete point cloud is divided into global seed points by the farthest point sampling (FPS) method and the local cloud points around each seed point by the k-nearest neighbor method. Then, the seed points and their local cloud points are randomly removed to generate partial cloud points as input, training the encoder and decoder in a self-supervised way with the original complete point cloud as ground truth. Experiments are conducted to validate the effectiveness of the novel method compared with four existing completion baselines based on the 3D point cloud dataset. The results indicate that the CD1 loss of the completed particles by the proposed method is, on average, 49.05 % lower than that of existing baselines. Additionally, the error rate of the calculated morphology characteristics of the completed particles is, on average, 66.06 % lower than that of the partial point clouds.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"176 ","pages":"Article 113161"},"PeriodicalIF":7.2,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An intelligent feature selection-based fake news detection model for pandemic situation with optimal attention based multiscale densenet with long short-term memory layer","authors":"V Rathinapriya ,&nbsp;J. Kalaivani","doi":"10.1016/j.asoc.2025.113158","DOIUrl":"10.1016/j.asoc.2025.113158","url":null,"abstract":"<div><div>Fake news has recently used the strength and scope of online networking sites to efficiently propagate misinformation, eroding confidence in the press and journalism while also manipulating public perceptions and emotions. However, much information appearing on the Internet is dubious and even intended to mislead. Some fake news is so similar to the real ones that it is difficult for humans to identify them. Therefore, Fake News Detection (FND) needs to develop effectual models to overcome the existing challenges. So, in this paper, a novel deep-learning approach is developed for the recognition of fake news in pandemic situations. Initially, text data are collected from benchmark resources related to the pandemic situation and provided to the pre-processing stage. Then, the obtained pre-processed data is inputted into the feature extraction process. Here, the features are extracted using glove embedding, Bidirectional Encoder Representations from Transformers (BERT), and Term Frequency Inverse Document Frequency (TFIDF). Later, the extracted features are taken to the fused optimal weighted feature selection, and the weights are optimized using the Updated Random Variable-based Artificial Rabbits Optimization (URV-ARO), leveraging the Artificial Rabbits Optimization (ARO). The attained optimal weighted features are then given to the classification process. In the classification phase, the fake news is classified with the help of Optimal Attention-based Multiscale Densenet with Long Short-Term Memory layer (OAMDNet-LSTM). Moreover, parameters in DenseNetand LSTM are tuned by developed URV-ARO. Optimizing parameters in the DenseNetand LSTM helps fine-tune the model to achieve higher accuracy in distinguishing between genuine and fake news. The effectiveness of the proposed model is validated with conventional approaches to showcase the effectiveness of others.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"176 ","pages":"Article 113158"},"PeriodicalIF":7.2,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A modified neutrosophic fuzzy approach for managing electronic waste considering sustainability and resilience dimensions","authors":"Muhammad Salman Habib ,&nbsp;Seung-June Hwang","doi":"10.1016/j.asoc.2025.113097","DOIUrl":"10.1016/j.asoc.2025.113097","url":null,"abstract":"<div><div>The rising problem of electronic waste (e-waste) demands management strategies that minimize environmental impact and prioritize resilience and sustainability, especially amid global disruptions and pressure on manufacturers to adopt extended producer responsibility policies. Existing literature on e-waste management primarily addresses either operational efficiency or sustainability, leaving a research gap in understanding the relationship between sustainability and resilience. To bridge this gap, this study proposes a framework for building resilient and sustainable e-waste management systems in dynamic environments. This framework utilizes a multi-objective optimization model that balances cost, environmental impact, and social factors (sustainability dimensions) while incorporating non-resilience vulnerabilities for optimal decision-making. The model addresses parameter uncertainties through a fuzzy programming approach based on the Me-measure, further enhanced by proposing variants of novel neutrosophic fuzzy programming techniques. The proposed framework is validated by implementing it in a real-world case problem. Key findings show that enhancing e-waste management network resilience relies on strategically reinforcing critical facilities with redundancy. Allocating 100 % priority to resilience achieves a resilience goal of 100 % and a sustainability goal of 52 %, while prioritizing sustainability at 100 % results in a sustainability goal of 73.7 % and resilience of 71.4 %, suggesting that sustainable practices often inherently enhance resilience. Research offers valuable insights for policymakers, regulators, and stakeholders through managerial recommendations, visualizations, and sensitivity analyses.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"176 ","pages":"Article 113097"},"PeriodicalIF":7.2,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards energy-efficient Robotic Mobile Fulfillment System: Hybrid agent-based simulation with DEA-based surrogate machine learning","authors":"Zakka Ugih Rizqi ,&nbsp;Shuo-Yan Chou ,&nbsp;Adi Dharma Oscar","doi":"10.1016/j.asoc.2025.113141","DOIUrl":"10.1016/j.asoc.2025.113141","url":null,"abstract":"<div><div>The rapid growth of retail e-commerce has increased demand for warehouses to handle large volumes and diverse SKUs. To meet these demands, Robotic Mobile Fulfillment System (RMFS) is widely adopted. However, the automation in RMFS significantly raises energy consumption. The challenge is that the dynamic complexity of RMFS operations poses a major challenge in improving energy efficiency. This research proposes a hybrid optimization model to optimize traffic policy, routing strategy, number of robots, and robot’s max speed for reducing energy consumption while maintaining throughput rate. We first formulated a realistic RMFS energy consumption. A new priority rule for traffic policy was then proposed to reduce unnecessary stoppages. Two routing strategies namely Aisles Only and Underneath Pod were evaluated. Agent-based model was finally developed. Simulation experiment shows that the proposed priority rule reduces energy consumption by 3.41 % and increases the throughput by 26.07 % compared to FCFS. Further, global optimization was performed by first unifying conflicting objectives into a single-efficiency objective using Data Envelopment Analysis. Surrogate-based machine learning was then fitted and optimized via metaheuristic algorithm. The near-optimal configuration for RMFS was achieved by implementing the Priority Rule as traffic policy, Underneath Pod as routing strategy, 26 as number of robots, and 1.372 m/s as max speed. ANOVA reveals that the number of robots is the most influential factors to overall RMFS performance.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"176 ","pages":"Article 113141"},"PeriodicalIF":7.2,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A local and global multi-head relation self-attention network for fault diagnosis of rotating machinery under noisy environments","authors":"Yiwei Cheng ,&nbsp;Xinnuo Lin ,&nbsp;Wenwei Liu ,&nbsp;Ming Zeng ,&nbsp;Pengfei Liang","doi":"10.1016/j.asoc.2025.113138","DOIUrl":"10.1016/j.asoc.2025.113138","url":null,"abstract":"<div><div>Fault diagnosis under noisy environments (FDUNE) for rotating machinery is a highly challenging task. In recent years, deep learning models have become research hotspots in the field of FDUNE. However, the existing FDUNE approaches suffer from a limitation that insufficient consideration of both local and global features in the feature extraction process leads to unsatisfactory diagnostic performance. In this paper, a local and global multi-head relation self-attention network (LGMHRSANet) is proposed to improve the diagnostic accuracy of rotating machinery under noisy environments, which integrates convolution and self-attention into the transformer form, enabling it to capture local features and global long-range temporal features from vibration signals. Two experimental cases on rolling bearings and gearboxes are implemented to verify the effectiveness of LGMHRSANet under noisy environments. Experimental results demonstrate that LGMHRSANet has superior diagnostic performance compared to other deep learning models, regardless of whether it is in a non-noise environment, or a strong noise environment. In addition, the adaptive performance analysis in the variable noise domain indicates that LGMHRSANet has good robustness in noisy environments.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"176 ","pages":"Article 113138"},"PeriodicalIF":7.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parallel graph neural architecture search optimization with incomplete features","authors":"Haitao Yang ,&nbsp;Zhaowei Liu ,&nbsp;Dong Yang ,&nbsp;Lihong Wang","doi":"10.1016/j.asoc.2025.113068","DOIUrl":"10.1016/j.asoc.2025.113068","url":null,"abstract":"<div><div>Graph neural networks (GNNs) have shown remarkable success in many fields. However, the results of different model architectures for different scenarios can be very different. Designing effective neural architectures requires a great deal of specialized knowledge, which limits the application of GNNs models. In recent years, graph neural architecture search (GNAS) has attracted widespread attention. GNAS selects the GNNs structure in predefined search space using a suitable search algorithm. The search direction is constrained based on the evaluation made by the estimation strategy. Traditional GNAS methods suffer from long search times, difficulty in parameter selection, and high sensitivity to data quality. When feature information is missing, the candidate architectures explored during the search process cannot obtain complete feature information, which significantly reduces the accuracy of GNAS. To tackle these challenges, we propose a novel optimization framework for parallel graph neural architecture search, named AutoPGO. In AutoPGO, we complement the features based on a feature propagation algorithm generated by minimizing the Dirichlet energy function, improve the search algorithm using the mutation decay strategy and complete the optimization of the parameters using the Bayesian optimization method. Experimental results show that AutoPGO has good performance and some degree of robustness.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"176 ","pages":"Article 113068"},"PeriodicalIF":7.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Oscillating activation functions can improve the performance of convolutional neural networks","authors":"Mathew Mithra Noel ,&nbsp;Arunkumar L. ,&nbsp;Advait Trivedi ,&nbsp;Praneet Dutta","doi":"10.1016/j.asoc.2025.113077","DOIUrl":"10.1016/j.asoc.2025.113077","url":null,"abstract":"<div><div>Convolutional neural networks have been successful in solving many socially important and economically significant problems. Their ability to learn complex high-dimensional functions hierarchically can be attributed to the use of nonlinear activation functions. A key discovery that made training deep networks feasible was the adoption of the Rectified Linear Unit (ReLU) activation function to alleviate the vanishing gradient problem caused by using saturating activation functions. Since then, many improved variants of the ReLU activation have been proposed. However, a majority of activation functions used today are non-oscillatory and monotonically increasing due to their biological plausibility. This paper demonstrates that oscillatory activation functions can improve gradient flow and reduce network size. Two theorems on limits of non-oscillatory activation functions are presented. A new oscillatory activation function called Growing Cosine Unit(GCU) defined as <span><math><mrow><mi>C</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow><mo>=</mo><mi>z</mi><mi>⋅</mi><mo>cos</mo><mi>z</mi></mrow></math></span> that outperforms Sigmoids, Swish, Mish and ReLU on a variety of architectures and benchmarks is presented. The GCU activation has multiple zeros enabling single GCU neurons to have multiple hyperplanes in the decision boundary. This allows single GCU neurons to learn the XOR function without feature engineering. Extensive experimental comparison with 16 popular activation functions indicate that the GCU activation function significantly improves performance on CIFAR-10, CIFAR-100, Imagenette and the 1000 class ImageNet benchmarks.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113077"},"PeriodicalIF":7.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Selecting a health emergency strategy through large-scale multi-criteria decision-making based on intuitionistic fuzzy self-confidence data","authors":"Priya Sharma ,&nbsp;Mukesh Kumar Mehlawat ,&nbsp;Pankaj Gupta ,&nbsp;Shilpi Verma","doi":"10.1016/j.asoc.2025.113085","DOIUrl":"10.1016/j.asoc.2025.113085","url":null,"abstract":"<div><div>In complex decision-making scenarios involving multiple stakeholders, the uncertainty and individual confidence of decision-makers (DMs) are crucial in determining the outcomes. A novel approach is proposed in this paper to improve decision-making processes within a large group of DMs operating under an “Intuitionistic Fuzzy Self-Confidence (IFN-SC)” setting. The research presents a hybrid clustering algorithm to categorize DMs based on their numerical similarities and psychological factors. A multi-objective nonlinear optimization problem is employed to determine the criteria weights in the IFN-SC environment when the weight vector is either partially or fully unknown. Using the max operator, we derive a single-objective nonlinear optimization problem, which is solved by the “Particle Swarm Optimization (PSO)” algorithm. Furthermore, extending the “Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)” for the IFN-SC environment significantly enhances the model’s effectiveness in ranking alternatives. The study exemplified its capability in managing a large-scale decision-making problem based on health emergency strategy selection and presented various analyses highlighting its utility, adaptability, and robustness in practical situations.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"176 ","pages":"Article 113085"},"PeriodicalIF":7.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}