Applied Soft Computing最新文献

{"title":"Node importance identification method for urban rail transit networks based on improved mutual information considering inter-station cross-dependencies","authors":"Yanhui Yin ,&nbsp;Guanghong Hu ,&nbsp;Zhuo Huang ,&nbsp;Jihui Chen ,&nbsp;Wencheng Huang","doi":"10.1016/j.asoc.2026.114755","DOIUrl":"10.1016/j.asoc.2026.114755","url":null,"abstract":"<div><div>Identifying and protecting the key nodes of urban rail transit networks (URTN) is crucial for the normal operation of rail transit. However, most of the previous studies mainly relied on a single topological structure or passenger flow to identify important nodes, ignoring the inter-station cross-dependencies among the stations. In this paper, an Improved Mutual Information (IMI) is introduced to identify the node importance of URTN considering inter-station cross-dependencies. IMI emphasizes that the influence of a node will spread to the adjacent nodes within the effective range, and the importance of a node is the result of the combined influence of the adjacent nodes within that range. The comparative numerical researches have been conducted, the effectiveness and reliability of the IMI algorithm are validated from three aspects: network connectivity, node importance evaluation, and statistical analysis. Furthermore, under random and intentional attacks, we utilize the relative size of the maximal connected subgraph (RSMCS) and the relative network efficiency (RNE) before and after nodes are attacked to study the invulnerability of undirected unweighted networks of URTN. Additionally, we utilize the weighted RSMCS and the weighted RNE to study the invulnerability of bi-directional weighted URTN networks. Finally, A case study is performed with the Chengdu URTN serving as the context, the results show that, the IMI is suitable for networks with strong inter-station cross-dependencies (especially URTN), and can identify nodes with low degree but crucial due to their high inter-station cross-dependencies. It has higher discrimination ability for nodes ranked lower, and can capture stations with lower node degree but closely related to other important stations or important routes. Finally, suggestions for improving the performance and invulnerability of URTN are proposed from four aspects including expanding the capacity of the station, strengthening the connection between urban bus network and URTN, protecting important stations and routes with strong inter-station cross-dependencies through IMI algorithm, improving operation plans and alleviating peak passenger flow pressure.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114755"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191541","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":"Differential-game algorithm for resource optimization of wireless sensor network","authors":"Ning Yao ,&nbsp;Dong Liu ,&nbsp;Bai Chen ,&nbsp;Xiaochen Hao ,&nbsp;Rongrong Yin","doi":"10.1016/j.asoc.2026.114641","DOIUrl":"10.1016/j.asoc.2026.114641","url":null,"abstract":"<div><div>In response to the resource competition optimization problem in wireless sensor network, this paper first uses differential evolution algorithm to transform single-object multi-objective optimization into multi-object single-objective optimization, and combines game theory to design a new differential game algorithm. The algorithm conforms to the differential evolution framework on the outer layer and meets the requirements of game theory on the inner layer. And theoretically proved the low complexity and algorithm convergence. Then, the designed differential game algorithm was applied to the wireless sensor network to design the channel allocation algorithm. The convergence and effectiveness of the algorithm were verified through simulation. Finally, the differential game algorithm was discussed, and it was theoretically proved that the algorithm is also applicable to multi-objective optimization of multiple objects, providing a design idea for low complexity convergence multi-objective optimization algorithms.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114641"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039862","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":"RADSCL: Representation augmentation integrated with dual supervised contrastive learning for low-resource text classification","authors":"Jun Zhang ,&nbsp;Ze Kuang ,&nbsp;Fanfan Shen ,&nbsp;Jianfeng He ,&nbsp;Rui Sun ,&nbsp;Yanxiang He","doi":"10.1016/j.asoc.2026.114646","DOIUrl":"10.1016/j.asoc.2026.114646","url":null,"abstract":"<div><div>Text classification in low-resource regime is a challenging task. Data augmentation techniques can significantly alleviate the issue of insufficient training samples in such environments by generating new samples. However, existing data augmentation methods have not yet effectively solved the problems of hard samples that are hard to classify and insufficient model generalization ability, which makes the performance of text classification in low-resource regime still has room for improvement. To this end, this paper proposes a method that fuses representational data augmentation and dual supervised contrast learning (RADSCL) in low-resource regime. Representational data augmentation first uses dynamic span-cutoff to remove context-independent words, which reduces the parameters needed for mixup and lowers the computational cost. Then the meaningless property of PAD word embeddings is utilized to perform weighted mixing with the cut-off text to reduce the importance of certain words in the text that affect classification. Through the representation augmentation method proposed in this paper, high-quality hard positive samples can be generated to optimize the decision boundary of the model. On this basis, in view of the current contrastive learning has achieved significant results in enhancing text representation, this paper constructs a dual supervised contrastive learning framework. The framework not only uses supervised contrastive learning to focus on the contrastive relationship between positive and negative samples, but also fully learns the potential semantic relationship between different categories of samples under the role of soft labels. Label distribution contrastive learning further utilizes the distribution information of soft labels based on supervised contrastive learning to impose effective constraints on the text representation, which effectively improves the performance of the text classification model. Multiple sets of experimental results show that the performance of the RADSCL model outperforms any model that incorporates other data augmentation and supervised contrastive learning models by a considerable margin on three benchmark datasets. In particular, the average accuracy of RADSCL is improved by 2.76 %, 2.66 %, 0.91 %, and 2.75 %, respectively, relative to the fusion models such as EDA+CE+SCL, Mixup+CE+SCL, ADV+CE+SCL, and AWD+CE+SCL.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114646"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039870","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":"Triangle centroid search algorithm: A reliable optimization technique for modeling photovoltaic systems","authors":"Haonan Zhao,&nbsp;Jing Li","doi":"10.1016/j.asoc.2026.114694","DOIUrl":"10.1016/j.asoc.2026.114694","url":null,"abstract":"<div><div>The rapid global expansion of photovoltaic (PV) installations has heightened the importance of PV system design, control and monitoring. Precise parameter identification is critical for PV systems, yet existing metaheuristics often suffer from high variance and unstable convergence due to model complexity. To address this, we propose the triangle centroid search algorithm (TCSA), a novel geometry-inspired metaheuristic. TCSA dynamically constructs triangular subsystems and employs a fitness-weighted centroid to balance exploration and exploitation. By coordinating both internal and external learning strategies, the TCSA enhances convergence speed and enables the population to break free from local optima. Evaluations on single-, double- and triple-diode models demonstrate the superiority of TCSA. Notably, on the complex DDM and TDM, it reduces the root mean square error (RMSE) to 9.8248E-04 while achieving a remarkable standard deviation in the order of 10E-17. Furthermore, extensive assessments on the IEEE CEC2020 benchmark functions and real-world constrained optimization suites validate the algorithm’s scalability and its potential for general engineering tasks. These results confirm that TCSA provides a dynamic PV model optimization framework with high precision and stability.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114694"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146096116","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":"MSCNRNet: Non-dense residual network with multi-skip connection for image restoration","authors":"Yun Chen ,&nbsp;Huanhuan Luo ,&nbsp;Mengnan Shi ,&nbsp;Xiaowen Ma ,&nbsp;Hongtao Li ,&nbsp;Qiang Yao","doi":"10.1016/j.asoc.2026.114742","DOIUrl":"10.1016/j.asoc.2026.114742","url":null,"abstract":"<div><div>In recent years, residual dense network(RDN)-based models have become increasingly popular in image restoration (IR) due to their enhanced feature propagation and ability to resist overfitting. However, these models still have limitations: 1) Their convolutional blocks rely on dense connections, leading to redundant local feature capture and repetitive features. 2) Each layer in the block is a single convolutional layer, limiting representational power and local feature extraction. 3) Basic connections between blocks hinder effective global feature fusion, resulting in reconstructed images lacking details. To address the limitations of RDN-based models, this paper proposes a novel IR model Non-dense Residual Network with Multi-skip Connection for Image Restoration (MSCNRNet). It replaces traditional dense connections in RDNs with multi-skip connections to form multi-skip connection non-dense residual blocks (MSCNRBs), reducing redundant features. Each MSCNRB comprises two multi-skip connection convolutional layers (MSCNRConv) to enhance local feature extraction, with a feature enhancement layer (FEL) added after each block for better global feature fusion. Based on extensive experiments across 12 synthetic and real-world datasets, our MSCNRNet model outperforms current state-of-the-art (SOTA) models in image restoration (IR) tasks by approximately 0.1% to 5% in the peak signal-to-noise ratio (PSNR) and around 2% in the structural similarity index measure (SSIM). This work provides an efficient solution to the limitations of existing RDN-based models, offering a more effective approach for high-quality IR tasks.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114742"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190764","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":"Quantum-enhanced recurrent models for cognitive–motor assessment","authors":"Basma Jalloul,&nbsp;Bassem Bouaziz,&nbsp;Walid Mahdi","doi":"10.1016/j.asoc.2026.114696","DOIUrl":"10.1016/j.asoc.2026.114696","url":null,"abstract":"<div><div>Accurate assessment of cognitive and motor function is fundamental for the early detection of neurodegenerative and cerebrovascular conditions such as Mild Cognitive Impairment (MCI), stroke, and Parkinson’s disease. While clinical evaluations are often subjective, recent advances in wearable sensing and pose estimation have enabled objective gait analysis across these disorders. However, the inherent noise, artifacts, and inter-subject variability of real-world clinical data remain challenging for conventional deep learning models, which tend to overfit synthetic data and generalize poorly. In this work, we propose a hybrid soft-computing framework that integrates parameterized quantum circuits with recurrent neural networks (QE-RNNs) to enhance feature representation and robustness. Quantum-enhanced embeddings are executed on real quantum hardware and combined with classical temporal modeling to capture complex brain–motor dynamics. Experimental results across synthetic and clinical gait datasets show that, whereas classical RNNs lose significant accuracy in noisy environments, QE-RNNs maintain strong generalization, achieving up to 99.25% accuracy in stroke-related gait analysis and 81.43% in Parkinson’s motor-state classification. These findings highlight the potential of quantum-inspired soft computing for developing resilient, explainable, and clinically relevant tools for motor assessment.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114696"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080041","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 adaptive exploration-exploitation framework for success history based differential evolution","authors":"Yiwen Zhuo ,&nbsp;Qiangda Yang","doi":"10.1016/j.asoc.2026.114665","DOIUrl":"10.1016/j.asoc.2026.114665","url":null,"abstract":"<div><div>Differential evolution (DE) algorithms are widely applied to various optimization problems due to their simplicity and excellent performance. Among these, Success-History based Adaptive DE (SHADE) and its variants have gained significant attention and shown high competitiveness owing to the robustness and superior performance enabled by their parameter adaptation strategies. However, when confronted with multimodal problems, these conventional parameter adaptation strategies often fail to generate appropriate parameters to guide the population's exploration. This readily leads to an imbalance between exploration and exploitation, ultimately degrading performance. To address this limitation, we propose a parameter configuration framework that extends conventional adaptation strategies. Designed for broad applicability, this framework is readily extensible to all major SHADE-based algorithms. It enhances parameter settings in these algorithms via a semi-parameter adaptation strategy that guides population exploration toward promising regions, thereby overcoming the limitations of conventional parameter adaptation in multimodal optimization. Furthermore, we introduce a dual-criterion identification mechanism based on offspring success rate and population diversity metrics. Integrated within the aforementioned strategy, this mechanism facilitates adaptive exploration-exploitation balancing. Full testing across the IEEE Congress on Evolutionary Computation (CEC) 2014, 2015, and 2017 benchmark suites demonstrates that applying the framework comprising the aforementioned strategy and mechanism to both competition-winning and recently published SHADE-based algorithms significantly boosts their performance over the original versions. Finally, we discuss parameter sensitivity and algorithmic complexity, and validate the effectiveness through visualization analysis.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114665"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080100","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":"DualBranch-YDNet: A dual-stream deep segmentation framework for accurate rice chalkiness detection","authors":"Jinfeng Zhao ,&nbsp;Sheng Dai ,&nbsp;Xianglong Xia ,&nbsp;Guochao Zhao ,&nbsp;Jie Qiu ,&nbsp;Xin Wei ,&nbsp;Xuehui Huang ,&nbsp;Yan Ma ,&nbsp;Jie Yang","doi":"10.1016/j.asoc.2026.114690","DOIUrl":"10.1016/j.asoc.2026.114690","url":null,"abstract":"<div><div>Accurate segmentation of chalky regions remains a critical and challenging problem in automated rice quality assessment, due to their low contrast, small size, and complex morphology. Existing methods often rely on grain-level classification without pixel-level localization, limiting their applicability in fine-grained phenotyping. We propose DualBranch-YDNet, a novel dual-branch deep segmentation framework that integrates YOLO-SAM for rapid grain localization and DeepLabv3+ for fine-grained chalkiness segmentation. A lightweight mask fusion strategy aligns outputs from both branches, enabling precise delineation of grain contours and internal defects. To address small-object and class imbalance challenges, we incorporate Reparameterized Large Kernel Convolution (RepLKConv) and a composite WBCE–Dice loss, significantly enhancing accuracy and convergence. We also construct a large-scale, high-resolution rice chalkiness dataset covering diverse varieties and spatial arrangements. Experiments demonstrate that DualBranch-YDNet achieves an average error of 1.0% in chalkiness degree and a maximum deviation of 2.0% in chalky grain rate, surpassing four state-of-the-art baselines. These results confirm its robustness, accuracy, and suitability for laboratory-based and off-line high-throughput rice quality evaluation in real-world phenotyping scenarios.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114690"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080103","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":"Unsupervised sparse temporal knowledge graph entity alignment via joint temporal relational representation learning","authors":"Xuan Du,&nbsp;Luyi Bai,&nbsp;Guangchen Feng,&nbsp;Jingfang Li","doi":"10.1016/j.asoc.2026.114684","DOIUrl":"10.1016/j.asoc.2026.114684","url":null,"abstract":"<div><div>Entity Alignment (EA) aims to find entities from different knowledge graphs (KGs) that point to the same object in the real world. In recent years, Temporal Knowledge Graphs (TKGs) have extended static KGs by introducing timestamps, providing a new perspective for EA. However, the current mainstream TKG EA models are supervised models that rely on pre-aligned seeds and implicitly encode temporal information into the entity embedding space for identifying equivalent entities. In addition, the current mainstream TKGs entity alignment methods do not make full use of temporal information and do not consider the sparsity of information. To solve the above challenges, we propose an Unsupervised Sparse Temporal Knowledge Graph Entity Alignment model via joint temporal relational representation learning, namely USTEA. In this framework, we propose multi-layer convolutional propagation to supplement entities lacking temporal information, improving the quality of unsupervised alignment seed pairs on sparse temporal knowledge graphs (STKGs). Moreover, we introduce temporal relational representation learning that effectively captures the sparse temporal and relational information on sparse temporal knowledge graphs (STKGs). Experimental results on four STKGs demonstrate that the USTEA model outperforms both supervised and unsupervised state-of-the-art TKG EA models.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114684"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080195","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":"Accelerating pattern mining on fuzzy data by packing truth values into blocks of bits","authors":"Michal Burda","doi":"10.1016/j.asoc.2026.114661","DOIUrl":"10.1016/j.asoc.2026.114661","url":null,"abstract":"<div><div>In pattern mining from tabular data using fuzzy logic, a common task involves computing triangular norms (t-norms) to represent conjunctions of fuzzy predicates and summing the resulting truth values to evaluate rule support or other pattern quality measures. Building on previous work, this paper presents an approach that packs multiple fuzzy truth values into a single integer and performs t-norm computations directly on this compact representation. By using 4-, 8-, or 16-bit precision, the method substantially reduces memory consumption and improves computational efficiency. For example, with 8-bit precision—offering two decimal places of accuracy—it requires only one-quarter of the memory and achieves 3–16<span><math><mo>×</mo></math></span> speedup compared to conventional floating-point-based method of computation. The proposed method is also compared with a traditional computation approach optimized using advanced Single-Instruction/Multiple-Data (SIMD) CPU operations, demonstrating its superior performance on modern architectures.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114661"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039728","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}