Applied Soft Computing最新文献

{"title":"A three-way decision method based on jensen-shannon divergence and behavior perception under the probabilistic linguistic term environment","authors":"Yizhu Cairang ,&nbsp;Haidong Zhang ,&nbsp;Yanping He","doi":"10.1016/j.asoc.2026.114724","DOIUrl":"10.1016/j.asoc.2026.114724","url":null,"abstract":"<div><div>Multi-attribute decision-making (MADM) problems are among the central challenges in the field of decision-making, aiming to help decision- makers (DMs) select the optimal alternative from a set of alternatives when faced with multiple mutually conflicting evaluation criteria. In real-world complex situations, DMs often prefer to use more natural and intuitive linguistic terms for evaluation rather than precise numerical values. At the same time, traditional MADM methods frequently neglect the DMs’ intrinsic psychological and behavioral factors when constructing decision models. To effectively address these challenges, this paper proposes a novel decision method that integrates regret theory (RT) with the multi-objective optimization by ratio analysis (MOORA) method under a probabilistic linguistic term set (PLTS) framework. Compared with existing methods, the main contributions of this paper can be summarized in four aspects: (1) To address deficiencies in existing distance formulas for PLTS, we innovatively introduce the Jensen-Shannon (JS) divergence to construct a more reasonable and accurate distance calculation formula. Based on this, a new similarity is derived, and a distance based attribute weights method is proposed. (2) By introducing <span><math><mi>θ</mi></math></span>-level similarity classes, this paper proposes a new method for calculating conditional probabilities of alternatives. Combining the aforementioned attribute weights, weighted conditional probabilities are constructed. (3) To more accurately capture DMs’ psychological behavior, this paper integrates RT with the MOORA method to construct a relative utility function that effectively reflects DMs’ behavioral perceptions. (4) By combining weighted conditional probabilities with the relative utility function, a novel three-way decision (TWD) method is formed; this method not only flexibly allows for deferred decisions when evaluation information is insufficient but also comprehensively accounts for DMs’ risk preferences and regret psychology, thereby producing decisions more aligned with real-world contexts. Finally, through analysis of practical case studies and comparative experiments with several classical methods, the proposed method is thoroughly validated for its significant advantages and practical value in decision performance, stability, and adaptability. We believe this method offers a more scientific and human-centered solution for MADM in complex uncertain environments.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114724"},"PeriodicalIF":6.6,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191520","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":"Model predictive control for 3D multi-UAV conflict resolution based on velocity obstacle and dynamic window triggering","authors":"Yingxue Yu ,&nbsp;Qingwei Zhong ,&nbsp;Yongxiang Zhang ,&nbsp;Weijun Pan ,&nbsp;Yi Ai","doi":"10.1016/j.asoc.2026.114726","DOIUrl":"10.1016/j.asoc.2026.114726","url":null,"abstract":"<div><div>The urban low-altitude airspace is characterized by high density, narrow spacing, and strong dynamics, posing significant challenges to existing conflict management approaches. Traditional methods based on rule-based buffers, geometric feasible domains, and rolling optimization struggle to achieve a balance among real-time performance, system stability, and scalability. To address these limitations, this study proposes a trigger-based fusion framework integrating Model Predictive Control (MPC) for conflict resolution in multi-UAV operations. The framework introduces a dynamic time window mechanism to initiate single-step optimization only when predicted conflicts enter the effective response horizon, thereby reducing unnecessary computational overhead. A local feasible velocity domain is constructed based on an improved Velocity Obstacle (VO) method to define the searchable decision space. At the decision layer, a bi-objective principle minimizes velocity deviations and suppresses trajectory disturbances, while explicitly penalizing potential secondary conflicts to enhance separation stability. At the execution layer, optimal velocity decisions are mapped to real-time state updates. At the system level, periodic rolling scheduling is employed to reconstruct Unmanned Aerial Vehicle (UAV) fleet states and evolving conflict scenarios, enabling long-term continuous operation. Simulation results demonstrate an average response delay of 139 seconds, significantly reducing false alarms. The dynamic time window constrains disengagement initiation to within 200 s prior to conflict onset, ensuring timely response and maneuver feasibility. On average, 37.83 % of trajectories need to be adjusted to avoid all secondary conflicts, confirming the method’s comprehensive advantages in safety, efficiency, and stability.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114726"},"PeriodicalIF":6.6,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080198","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 learning-driven artificial bee colony algorithm for mobile robot multi-objective path planning","authors":"Fan Ye ,&nbsp;Peng Duan ,&nbsp;Leilei Meng ,&nbsp;Hongyan Sang ,&nbsp;Maodi Ru ,&nbsp;Kaizhou Gao","doi":"10.1016/j.asoc.2026.114692","DOIUrl":"10.1016/j.asoc.2026.114692","url":null,"abstract":"<div><div>Path planning is a critical component enabling mobile robots to perform diverse tasks, with complexity increasing significantly when simultaneously optimizing multiple objectives. In this study, a learning-driven artificial bee colony algorithm (LBABC) is proposed to address multi-objective path planning problems, where three objectives are optimized, i.e., path length, path safety, and path smoothness. In the initialization phase, a competition-based initialization strategy is investigated to generate a diverse set of initial solutions. In the employed bee phase, a differential evolution strategy is developed to enhance the algorithm’s global exploration ability, with differential mutation and crossing operators redesigned to suit the specific characteristics of the path planning problems. In the onlooker bee phase, a Q-learning-based evolutionary operator selection strategy is designed to improve the algorithm’s local search ability, utilizing six problem-specific evolutionary operators. In the scout bee phase, an adaptive evolutionary restart strategy is presented to replace those stagnant solutions with promising ones to enhance population activity. Finally, the proposed LBABC was compared with seven state-of-the-art algorithms across 16 instances from four respective environments. Additionally, all components proposed by the LBABC were examined through ablation testing. Experimental results showed that the proposed LBABC obtained average improvements of 1.37% and 40.74% on the hypervolume and inverted generational distance metrics, respectively, compared with the results obtained by the corresponding algorithm with the second-best performance. These results demonstrated the effectiveness of the designed components and the superior performance of the proposed LBABC in solving multi-objective path planning problems.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114692"},"PeriodicalIF":6.6,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146096144","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":"Spectrum sparsity-driven learnable wavelet packet transform for denoising and its application in fault diagnosis","authors":"Xin Kang,&nbsp;Jianxin Cui,&nbsp;Junsheng Cheng,&nbsp;Yu Yang,&nbsp;Qiyao Liu","doi":"10.1016/j.asoc.2026.114713","DOIUrl":"10.1016/j.asoc.2026.114713","url":null,"abstract":"<div><div>Domain-specific noise is a key cause of cross-domain data distribution shifts and a major obstacle in cross-domain fault diagnosis. Most existing denoising methods are built on the assumptions that noise is weak and Gaussian, which often do not hold in the industrial context, thus limiting their effectiveness. To address this, we propose a spectrum sparsity-driven learnable wavelet packet transform (SSLWPT) model for unsupervised, small-sample mechanical signal denoising and feature enhancement. First, based on the vibration properties of rotating machinery, where stationary and cyclostationary components carry critical health-related information, we propose a spectrum sparsity-driven mechanism to actively capture these components for adaptive feature enhancement. Next, an autoencoder mimicking wavelet packet decomposition and reconstruction is proposed, with learnable wavelet bases and denoising parameters. Under the joint constraints of reconstruction loss, parameter regularization, and spectrum sparsity loss, the model achieves effective signal denoising. We further integrate the proposed SSLWPT as a preprocessing tool into a two-stage framework for cross-domain fault diagnosis. Both theoretical analysis and experimental results validate the superiority of the proposed model in denoising. Moreover, cross-domain diagnosis experiments conducted on bearing, gear and motor datasets confirm that our method achieves significantly better generalization than related denoising methods and SOTA domain generalization methods.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114713"},"PeriodicalIF":6.6,"publicationDate":"2026-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080107","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":"FAHFD: Fractional-order adaptive Higuchi fractal dimension for signal recognition","authors":"Yuxing Li,&nbsp;Jingyi Li,&nbsp;Yonghong Zhao","doi":"10.1016/j.asoc.2026.114721","DOIUrl":"10.1016/j.asoc.2026.114721","url":null,"abstract":"<div><div>Fractal dimension, as a classic nonlinear dynamic metric, can effectively characterize the complexity of signals and has been widely applied to signal recognition. However, existing fractal dimensions are limited to first-order information and suffer from the issue of parameter selection. This paper proposed the fractional-order adaptive Higuchi fractal dimension, where fractional-order calculus endows the metric with the ability to capture fractional-order information, and the design of a parameter-adaptive selection mechanism addresses the parameter selection problem. Simulation experiments show that the proposed fractional-order adaptive Higuchi fractal dimension achieves excellent classification performance on three simulation datasets involving colored noise discrimination, chaotic series analysis, and simulated bearing vibration; experimental tests on real-world datasets demonstrate that fractional-order adaptive Higuchi fractal dimension also exhibits strong generalizability, fully validating its reliability and effectiveness in addressing complex real-world engineering diagnosis tasks across different scenarios on three real-world engineering datasets including faulty bearings, faulty gears, and ship radiated noise signal.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114721"},"PeriodicalIF":6.6,"publicationDate":"2026-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080202","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 differential evolutionary algorithm improvement framework based on artificial potential fields","authors":"Zhe Yang ,&nbsp;Libao Deng ,&nbsp;Xianxin Mao ,&nbsp;Lili Zhang","doi":"10.1016/j.asoc.2026.114714","DOIUrl":"10.1016/j.asoc.2026.114714","url":null,"abstract":"<div><div>The Artificial Potential Field (APF) method, widely utilized in path planning, uses virtual environmental forces derived from potential field gradients for guidance. While Differential Evolution (DE) algorithms are recognized for their simplicity and robust global optimization capabilities, they often suffer from premature convergence and reduced search efficiency in complex scenarios. Motivated by the similarity between APF pathfinding and DE optimization, this study proposes an APF-inspired framework that utilizes virtual forces to guide the population. Attractive forces guide individuals toward potential optima, while repulsive forces generated by treating inferior solutions as virtual obstacles mitigate the risk of stagnation. This mechanism effectively steers the population toward promising areas, thereby enhancing search efficiency. Furthermore, an integrated adaptive strategy enhances performance by dynamically adjusting key parameters based on problem characteristics. Comprehensive experiments on the CEC2020 benchmark suite and 36 real-world engineering problems validate the effectiveness of the framework. The results indicate performance improvements over baseline algorithms and competitiveness in comparative studies.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114714"},"PeriodicalIF":6.6,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146096146","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":"2026-01-24","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":"Reinforcement learning-driven nonsingular fast terminal sliding mode control for pipe crack sealing manipulator","authors":"Santosh Kumar,&nbsp;S.K. Dwivedy","doi":"10.1016/j.asoc.2026.114708","DOIUrl":"10.1016/j.asoc.2026.114708","url":null,"abstract":"<div><div>This study explores control challenges associated with the Pipe Crack Sealing Manipulator (PCSM), a tree-shaped robot designed to seal cracks within concrete pipes. Equipped with five specialized branches, the PCSM adeptly navigates both vertical and horizontal pipe environments. A detailed CAD model was developed in SolidWorks, and data were meticulously exported to Simulink for refined control operations. SolidWorks simulations revealed that the PCSM’s fifth branch excelled in extensive crack repairs. The control of pipe crack sealing manipulators presents significant challenges due to uncertain dynamics, random disturbances, signal delay, and sensor noise inherent in confined environments. To overcome these issues, this study proposes a reinforcement learning–driven nonsingular fast terminal sliding mode control (RLNFTSMC) framework that enhances robustness, precision, and adaptability for crack sealing operations. The implemented RLNFTSMC strategy, includes an equivalent part utilizing actor-critic neural networks for dynamic estimation and a switching part that treats the mass matrix as a lumped value. Comparative analysis with nonsingular fast terminal sliding mode control (NFTSMC), adaptive NFTSMC, radial basis function neural network control (RBFNNC), and neural network-enhanced nonsingular fast terminal sliding mode control (NENFTSMC) shows that RLNFTSMC significantly improves the PCSM’s reliability and effectiveness in environments marked by random disturbances and unknown dynamics.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114708"},"PeriodicalIF":6.6,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080293","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":"Saturation and pressure change estimation from time-lapse seismic data using vision transformers","authors":"Marcos Cirne ,&nbsp;Masoud Maleki ,&nbsp;Michael Diniz ,&nbsp;Denis Schiozer ,&nbsp;Alessandra Davolio ,&nbsp;Anderson Rocha","doi":"10.1016/j.asoc.2026.114715","DOIUrl":"10.1016/j.asoc.2026.114715","url":null,"abstract":"<div><div>Deep learning-based algorithms have been used in oil and gas industry applications to optimize the performance of four-dimensional (4D) seismic inversion methods. These pose an important problem in reservoir management since they convey substantial information about fluid recovery and distribution on the reservoir over time. In this work, we propose a novel deep learning architecture based in Vision Transformers (ViT) to estimate saturation and pressure changes from 4D seismic data using data collected from a pre-salt oil field. The proposed method is first trained with an ensemble of simulation models (synthetic domain) and the resulting architecture is used to predict the actual changes using real 4D seismic data (observed domain). Due to the absence of data points around the reservoir area conveyed in our dataset, we redesigned the ViT architecture so that it only processes the existing data points in a 1-D fashion, making the architecture capable of processing different reservoir shapes other than rectangular ones. The quantitative results against recent deep learning methods corroborate the effectiveness of our proposed architecture. Finally, a qualitative analysis by a specialist and the use of an Explainable Artificial Intelligence (XAI) method helped assess the key differences in the decision-making processes taken by each analyzed algorithm.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114715"},"PeriodicalIF":6.6,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080101","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":"Incorporating mixed-effects into Adaptive time relation multi-task learning with longitudinal data for Alzheimer’s disease progression prediction","authors":"Linting Miao ,&nbsp;Hongmei Chen ,&nbsp;Biao Xiang ,&nbsp;Zhong Yuan ,&nbsp;Chuan Luo ,&nbsp;Tianrui Li","doi":"10.1016/j.asoc.2026.114716","DOIUrl":"10.1016/j.asoc.2026.114716","url":null,"abstract":"<div><div>Alzheimer’s disease (AD) is a slowly progressing neurodegenerative disorder and poses significant challenges for early diagnosis and longitudinal prognosis in the medical sector. Accurate prediction of cognitive decline is crucial for timely clinical intervention, disease monitoring, and treatment planning. Multi-task learning (MTL) has been extensively applied in AD prediction tasks, as it effectively captures shared patterns across multiple objectives and improves generalization. However, most existing MTL-based approaches focus on cross-sectional settings and lack the ability to explicitly model disease progression over time. To address this limitation, we propose a longitudinal multi-task learning framework that jointly models disease progression and adaptive temporal relationships using multi-timepoint neuroimaging data. The proposed method incorporates two task-specific projection matrices within a mixed-effects modeling framework to disentangle baseline-invariant representations from change-sensitive features, thereby capturing distinct patterns attributable to AD pathology and normal aging. Temporal relationships among tasks are learned directly from data via a task relationship matrix, while temporal asymmetry is enforced through directional regularization. Structured regularization is further introduced to enhance the sparsity and robustness of the learned model. The proposed framework is evaluated on real-world datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) using standard regression metrics, including root mean squared error (rMSE). Compared with the best-performing baselines, our model achieves an average rMSE reduction of approximately 6%–10% across three widely used cognitive measures at multiple time points, with improvements validated by statistical significance testing, indicating more accurate and reliable prediction of cognitive decline. Beyond predictive accuracy, the model also provides enhanced interpretability through brain-region-level visualization, which facilitates a clearer understanding of disease-related progression patterns and age-related effects, and supports clinical analysis and decision-making.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114716"},"PeriodicalIF":6.6,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146096143","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}