Applied Soft Computing最新文献_第5页

An adaptive exploration-exploitation framework for success history based differential evolution 基于差异演化成功史的自适应勘探开发框架

IF 6.6 1区计算机科学

Applied Soft Computing Pub Date : 2026-04-01 Epub Date: 2026-01-21 DOI: 10.1016/j.asoc.2026.114665

Yiwen Zhuo , Qiangda Yang

{"title":"An adaptive exploration-exploitation framework for success history based differential evolution","authors":"Yiwen Zhuo , 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}

引用次数: 0

DualBranch-YDNet: A dual-stream deep segmentation framework for accurate rice chalkiness detection DualBranch-YDNet：用于水稻白垩度精确检测的双流深度分割框架

IF 6.6 1区计算机科学

Applied Soft Computing Pub Date : 2026-04-01 Epub Date: 2026-01-22 DOI: 10.1016/j.asoc.2026.114690

Jinfeng Zhao , Sheng Dai , Xianglong Xia , Guochao Zhao , Jie Qiu , Xin Wei , Xuehui Huang , Yan Ma , Jie Yang

{"title":"DualBranch-YDNet: A dual-stream deep segmentation framework for accurate rice chalkiness detection","authors":"Jinfeng Zhao , Sheng Dai , Xianglong Xia , Guochao Zhao , Jie Qiu , Xin Wei , Xuehui Huang , Yan Ma , 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}

引用次数: 0

Unsupervised sparse temporal knowledge graph entity alignment via joint temporal relational representation learning 基于联合时态关系表示学习的无监督稀疏时态知识图实体对齐

IF 6.6 1区计算机科学

Applied Soft Computing Pub Date : 2026-04-01 Epub Date: 2026-01-23 DOI: 10.1016/j.asoc.2026.114684

Xuan Du, Luyi Bai, Guangchen Feng, Jingfang Li

{"title":"Unsupervised sparse temporal knowledge graph entity alignment via joint temporal relational representation learning","authors":"Xuan Du, Luyi Bai, Guangchen Feng, 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}

引用次数: 0

Triangle centroid search algorithm: A reliable optimization technique for modeling photovoltaic systems 三角质心搜索算法：一种可靠的光伏系统建模优化技术

IF 6.6 1区计算机科学

Applied Soft Computing Pub Date : 2026-04-01 Epub Date: 2026-01-29 DOI: 10.1016/j.asoc.2026.114694

Haonan Zhao, Jing Li

{"title":"Triangle centroid search algorithm: A reliable optimization technique for modeling photovoltaic systems","authors":"Haonan Zhao, 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}

引用次数: 0

GLUE-GAN: Global-local underwater image enhancement generative adversarial network GLUE-GAN：全局-局部水下图像增强生成对抗网络

IF 6.6 1区计算机科学

Applied Soft Computing Pub Date : 2026-04-01 Epub Date: 2026-01-23 DOI: 10.1016/j.asoc.2026.114686

Jun Wang , Daoyi Wei , Wentao Shi , Chenyang Liu , Wenlong Jiang , Juan Wang

{"title":"GLUE-GAN: Global-local underwater image enhancement generative adversarial network","authors":"Jun Wang , Daoyi Wei , Wentao Shi , Chenyang Liu , Wenlong Jiang , Juan Wang","doi":"10.1016/j.asoc.2026.114686","DOIUrl":"10.1016/j.asoc.2026.114686","url":null,"abstract":"<div><div>Underwater images suffer from color cast, blur, and low contrast due to wavelength-dependent absorption, scattering, and suspended particulates. Many enhancement methods restore global appearance while suppressing local features; others operate in the frequency domain or rely on auxiliary regularization but lack an explicit mechanism to fuse global and local evidence. We introduce GLUE-GAN, a generative adversarial network that globally–locally unifies enhancement through explicit cross-scale, cross-domain fusion. The model comprises: (1) an adaptive feature enhanced encoder that marries spatial context modules with grouped channel-wise self-attention to collaboratively model spatial–channel dependencies across diverse scenes; (2) a multichannel feature aggregation enhancement module that performs multi-scale extraction and alignment to uniformly recover global tone and local textures; and (3) a global–local information enhancement module that uses wavelet decomposition to separate low- and high-frequency bands, processing that mitigates local bias during global correction. Evaluations on EUVP, UIEB, and UFO-120 demonstrate consistent gains in color fidelity, contrast, and sharpness, with improved preservation of edges and fine details. By unifying spatial–channel reasoning with frequency-aware processing in a single adversarial framework, GLUE-GAN balances global color correction and local detail preservation for underwater image enhancement.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114686"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080303","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}

引用次数: 0

Diagnosis of autism disorder from rs-fMRI brain images through hierarchical YOLO and mechanism of attention (HYMA) 基于层次YOLO和注意机制的rs-fMRI脑图像诊断自闭症障碍

IF 6.6 1区计算机科学

Applied Soft Computing Pub Date : 2026-04-01 Epub Date: 2026-01-29 DOI: 10.1016/j.asoc.2026.114655

Saba Gholami , Sara Motamed , Elham Askari

{"title":"Diagnosis of autism disorder from rs-fMRI brain images through hierarchical YOLO and mechanism of attention (HYMA)","authors":"Saba Gholami , Sara Motamed , Elham Askari","doi":"10.1016/j.asoc.2026.114655","DOIUrl":"10.1016/j.asoc.2026.114655","url":null,"abstract":"<div><div>Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by atypical communication among brain regions. Early and accurate diagnosis of ASD can significantly improve the effectiveness of behavioral interventions. In this study, we propose a novel deep learning framework called HYMA (Hierarchical YOLO with Mechanism of Attention) for classifying ASD and control subjects using resting-state functional MRI (rs-fMRI) data. The proposed model integrates the YOLO detection architecture with a Bottleneck Attention Module (BAM) to enhance spatial–temporal feature extraction, and employs both dynamic (expert combination) and static (Naïve Bayes) ensemble classifiers for final decision fusion. To address the limited-sample challenge in medical imaging, we adopted extensive data augmentation and subject-wise validation. Experimental results on the ABIDE I and II datasets demonstrate that HYMA achieves superior performance, reaching an accuracy of 99.3%, precision of 98.9%, recall of 99.1%, and F1-score of 99.0%, outperforming existing state-of-the-art methods. These results indicate that the proposed attention-enhanced YOLO ensemble framework provides a robust and generalizable approach for rs-fMRI-based ASD diagnosis.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114655"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080299","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}

引用次数: 0

Accelerating pattern mining on fuzzy data by packing truth values into blocks of bits 通过将真值打包到位块中来加速对模糊数据的模式挖掘

IF 6.6 1区计算机科学

Applied Soft Computing Pub Date : 2026-04-01 Epub Date: 2026-01-14 DOI: 10.1016/j.asoc.2026.114661

Michal Burda

引用次数: 0

A multimodal small sample pipeline leak detection method based on spatio-temporal fusion 基于时空融合的多模态小样本管道泄漏检测方法

IF 6.6 1区计算机科学

Applied Soft Computing Pub Date : 2026-04-01 Epub Date: 2026-01-12 DOI: 10.1016/j.asoc.2026.114628

Jingyi Lu , Yuhang Wang , Jing Chen , Yao Chen , Dongmei Wang , Peng Wang

{"title":"A multimodal small sample pipeline leak detection method based on spatio-temporal fusion","authors":"Jingyi Lu , Yuhang Wang , Jing Chen , Yao Chen , Dongmei Wang , Peng Wang","doi":"10.1016/j.asoc.2026.114628","DOIUrl":"10.1016/j.asoc.2026.114628","url":null,"abstract":"<div><div>In the industry, leak detection is a critical task to ensure the safe operation of oil and gas pipelines. Due to the low frequency and short duration of pipeline leakage in actual operation, and the small leakage is easily drowned by noise, these factors affect the accuracy and generalization of the leakage diagnosis model. This study proposes a new multimodal few-shot leak detection method based on spatio-temporal fusion. First, we combine VQ-VAE and short-time Fourier transform (STFT) to enhance one-dimensional leak signals. We then build a spatio-temporal bidirectional (STB) dataset by combining the enhanced signals with images converted from time-series data, improving data reliability. Next, we design a spatio-temporal fusion (STF) network, which uses the PKO algorithm to optimize key model parameters and integrates an intra-inter module (IIM) attention mechanism to fuse multi-source feature information, thereby boosting model performance. Finally, the proposed method is validated using pipeline data under different working conditions. The results show that this method achieves an accuracy of 98.67 % in identifying various types of leak signals, comparative experiments demonstrate that this method outperforms other models, demonstrating strong robustness.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114628"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039855","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}

引用次数: 0

A hybrid agent based on reinforcement learning and fuzzy computing using q-rung orthopair fuzzy information for electricity purchasing negotiation in smart grids 基于q阶正交模糊信息的强化学习和模糊计算混合智能体用于智能电网购电协商

IF 6.6 1区计算机科学

Applied Soft Computing Pub Date : 2026-04-01 Epub Date: 2026-01-21 DOI: 10.1016/j.asoc.2026.114683

Dimitrios K. Panagiotou, Anastasios Dounis

{"title":"A hybrid agent based on reinforcement learning and fuzzy computing using q-rung orthopair fuzzy information for electricity purchasing negotiation in smart grids","authors":"Dimitrios K. Panagiotou, Anastasios Dounis","doi":"10.1016/j.asoc.2026.114683","DOIUrl":"10.1016/j.asoc.2026.114683","url":null,"abstract":"<div><div>Smart Grid electricity procurement requires efficient multi-issue negotiation under uncertainty, where participants lack complete information about opponents’ preferences and must balance individual utility with social welfare. This paper proposes a hybrid negotiation framework that integrates q-rung orthopair fuzzy sets (q-ROFS) for preference modeling, Q-learning for online estimation of opponent weights, and fuzzy inference systems for adaptive concessions and acceptance decisions. Buyer offers are ranked using a q-ROFS aggregation mechanism that captures preference vagueness, while reinforcement learning dynamically estimates opponent issue weights based on observed negotiation outcomes and Kaldor-Hicks welfare changes. The proposed model is evaluated in a simulated electricity purchasing scenario involving multiple sellers and three negotiation issues across different q values. Results demonstrate that the proposed approach consistently improves joint utility and fairness compared to a Stackelberg-inspired benchmark, achieving an average increase in joint utility of 3.8 % while producing more balanced and robust agreements that approach the Pareto frontier. These findings confirm the effectiveness of combining fuzzy preference modeling, learning-based adaptation, and welfare-oriented rewards in automated energy negotiations.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114683"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039864","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}

引用次数: 0

Using sentiment analysis and CEEMDAN to learn the preferences of consumer groups: A case study of online hotel reviews 利用情感分析和CEEMDAN了解消费者群体的偏好：以在线酒店评论为例

IF 6.6 1区计算机科学

Applied Soft Computing Pub Date : 2026-04-01 Epub Date: 2026-01-12 DOI: 10.1016/j.asoc.2026.114625

Dan Wang , Zaiwu Gong , Guo Wei , María Ángeles Martínez , Enrique Herrera-Viedma

{"title":"Using sentiment analysis and CEEMDAN to learn the preferences of consumer groups: A case study of online hotel reviews","authors":"Dan Wang , Zaiwu Gong , Guo Wei , María Ángeles Martínez , Enrique Herrera-Viedma","doi":"10.1016/j.asoc.2026.114625","DOIUrl":"10.1016/j.asoc.2026.114625","url":null,"abstract":"<div><div>With the rapid development of big data technologies, online reviews have emerged as a crucial platform where consumers can express their opinions. However, the unstructured nature, temporal characteristics, and implicit and ambiguous nature of these preferences pose formidable challenges for researchers. This paper introduces a data-driven model designed to learn and interpret consumer preferences. The approach begins with fine-grained sentiment analysis of online reviews via the deep learning model known as Bidirectional Encoder Representations from Transformers (BERT). The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is then applied to extract the residual trend component from the data. The mean value of this component serves as an approximate representation of group-consistent sentiment features, providing quantitative support for the subsequent preference modeling. Following this, the UTilités Additives (UTA) method is used to construct a preference disaggregation model that aids in inferring the classification value system of consumer groups and identifying the core factors prioritized by different consumer segments. To validate the model, experiments were conducted using real-world hotel industry review data. The results suggest that the proposed model not only enhances the interpretability of consumer group preferences but also refines and provides insights into their classification value system. Moreover, it enables hotel managers to accurately identify consumer needs, optimize resource allocation, enhance market competitiveness, and offer a scientific basis for evidence-based management decisions tailored to consumer preferences.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114625"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039863","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}

引用次数: 0