Applied Soft Computing最新文献

{"title":"A federated learning-based method for personalized manufacturing service recommendation with collaborative relationships","authors":"Lei Wang ,&nbsp;Jun Wang ,&nbsp;Feng Xiang ,&nbsp;Tongshun Li ,&nbsp;Yang Xu ,&nbsp;Yibing Li","doi":"10.1016/j.asoc.2025.113940","DOIUrl":"10.1016/j.asoc.2025.113940","url":null,"abstract":"<div><div>In the industrial Internet environment, the increasing complexity of manufacturing tasks has rendered them no longer accomplishable by independent manufacturing services. Meanwhile, current recommendation systems predominantly face challenges in maintaining data privacy and security during client parameter exchanges. To address these issues, this paper proposes CoFedSVD+ +, a federated learning-based method for personalized manufacturing service recommendation that integrates an enhanced SVD+ + algorithm with homomorphic encryption. First, we devise an enhanced similarity calculation method to analyze collaborative relationships among manufacturing services. Second, we implement a homomorphic encryption protocol within the federated learning framework to resolve data isolation challenges. Third, the improved SVD+ + algorithm is employed to capture implicit feedback information and predict missing Quality of Service (QoS) metrics. Fourth, a Top-N service composition recommendation list is generated through synergistic analysis of collaborative relationships and QoS predictions. Finally, we validate our approach using real-world case data from an industrial Internet platform. Experimental comparisons with existing recommendation algorithms demonstrate superior recommendation effectiveness of the proposed method.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"185 ","pages":"Article 113940"},"PeriodicalIF":6.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119756","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 collaborative competition multitasking framework for constrained multi-objective optimization","authors":"Xinyu Feng ,&nbsp;Qianlong Dang ,&nbsp;Xiaochuan Gao ,&nbsp;Yanghui Wu ,&nbsp;Lifei Zheng","doi":"10.1016/j.asoc.2025.113900","DOIUrl":"10.1016/j.asoc.2025.113900","url":null,"abstract":"<div><div>Constrained multi-objective optimization problems (CMOPs) are common in the real world. Constrained multi-objective evolutionary algorithms (CMOEAs) based on evolutionary multi-tasking show excellent performance in solving CMOPs. However, not all tasks can find useful information during the process of evolution, which inevitably results in a waste of computing resources. In this paper, a CMOEA based on collaborative competition multitasking (TCCMT) is proposed, in which two auxiliary tasks are constructed to co-evolve with the main task in a collaborative competition manner. During the process of evolution, only the dominant auxiliary task is selected to help the main task evolve, which reduces the resource consumption to evolve the invalid tasks. Meanwhile, the evolutionary process is divided into three stages in order to balance exploration and exploitation. The auxiliary tasks customize the constrained adaptive regression strategy and double angle enhancement strategy respectively to improve the ability to solve different problems. Compared with the nine most advanced CMOEAs on 33 benchmark problems and 7 real-world engineering problems, the Friedman test results show that TCCMT achieves the best rank on all test problems and exhibits a statistically significant difference.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"185 ","pages":"Article 113900"},"PeriodicalIF":6.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221560","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":"Representing online reviews using interval type-2 fuzzy Z-numbers for ranking energy-saving appliances","authors":"Yue Xiao ,&nbsp;Ming Li ,&nbsp;Ying Li ,&nbsp;Hongde Liu","doi":"10.1016/j.asoc.2025.113961","DOIUrl":"10.1016/j.asoc.2025.113961","url":null,"abstract":"<div><div>As e-commerce develops and the green consumption concept gains popularity, consumers are increasingly inclined to purchase energy-saving home appliances through e-commerce platforms. However, they often face technical complexities related to specialized energy-saving attributes and an overwhelming number of online reviews. To address these challenges, we have integrated energy-saving features into our online review analysis, incorporating weight calculations. Notably, we propose and prove a method that transforms online review information into interval type-2 fuzzy Z-numbers (IT2FZNs), which comprehensively represent the information to support product ranking. First, based on the energy-saving attributes of energy-saving appliances and their online review data, we extract energy-saving features and online review features, respectively. We then use a combination of TF-IDF-based text mining and BWM-based expert evaluation to determine the weight of each feature. Next, we convert the energy-saving feature data into IT2FZNs according to specific rules. The online review feature data is converted into interval type-2 fuzzy sets (IT2Fs) by considering the sentiment classification results and the accuracy and robustness of the model, and is further combined with the reliability of online reviews to form IT2FZNs. Finally, the alternative products are ranked based on the constructed decision matrix, and the final ranking results are determined. The method's effectiveness and practicality have been demonstrated using real data from energy-saving refrigerators on the JingDong (JD.com) platform, and its robustness and superiority have been further substantiated through comparative experiments.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"185 ","pages":"Article 113961"},"PeriodicalIF":6.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183686","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":"FFNN: Fractional order basis function multi-step neural network method for fractional partial differential equations","authors":"Jianke Zhang,&nbsp;Xudong Tian,&nbsp;Chang Zhou","doi":"10.1016/j.asoc.2025.113907","DOIUrl":"10.1016/j.asoc.2025.113907","url":null,"abstract":"<div><div>With the advancement in artificial intelligence technology, the increasing number of researchers utilize it to address complex equations in ocean engineering. So the technology of artificial intelligence has become a practical area of research. In this paper, we design a novel method to solve the fractional order long water wave equation, which is called the fractional order basis function multi-step neural network. Firstly, a power series is constructed based on a fractional order basis function, which serves as the approximate solution. Secondly, neural networks and the initial conditions of differential equations are integrated into the construction of approximate solutions. Furthermore, the solution is discretized, and a multi-step unfolding strategy is employed on the resulting discrete solution. This approach ensures that each point in the solution is influenced by its predecessor. By means of repeated applications of the optimization algorithm, the residuals are successively diminished, thereby yielding approximate solutions to the equations. Finally, the efficacy and versatility of the proposed strategy were validated through a series of numerical experiments. Compared with the method of fractional physics-informed neural networks, there are up to <span><math><mn>18.7</mn></math></span>-fold and <span><math><mn>22.8</mn></math></span>-fold increases in stability of average and maximum residuals. Simultaneously, initial conditions are retained in new solutions.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"185 ","pages":"Article 113907"},"PeriodicalIF":6.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159665","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":"ORESTE methodology within a circular intuitionistic fuzzy framework for preferential outranking in hybrid cloud service selection","authors":"Ting-Yu Chen","doi":"10.1016/j.asoc.2025.113864","DOIUrl":"10.1016/j.asoc.2025.113864","url":null,"abstract":"<div><div>This paper advances the ORESTE (Organísation, Rangement Et Synthèse de Données Relarionnelles) methodology within the Circular Intuitionistic Fuzzy (CIF) framework, highlighting its potential in practical decision analytics. The study first enhances CIF aggregation by employing the generalized mean technique, offering a flexible way to combine evaluative ratings and significance weights. Through modulation of the averaging parameter, decision-makers are able to accentuate either lower or higher values, thereby overcoming the constraints associated with conventional arithmetic means. The framework further improves decision precision through CIF similarity-driven appraisal indices, which utilize refined similarity metrics grounded in axiomatic properties such as symmetry, boundedness, identity, and monotonicity. These indices quantify the similarity between evaluative ratings and anchor references, while also revealing indifference and incomparability—thus equipping decision-makers with a comprehensive toolset for handling uncertainty. The CIF ORESTE framework comprises two methodologies. CIF ORESTE I delivers a global weak ranking using similarity-driven indices and generalized projection-related distances. CIF ORESTE II addresses the limitations of weak rankings by incorporating an Indifference-Preference-Incomparability (I-P-R) structure, which uses mean and net preference intensities to establish thresholds and clarify outranking relations. Applied to the evaluation of hybrid cloud services for a technology corporation, the CIF ORESTE framework demonstrates its effectiveness in resolving group decisions, managing uncertainty, and structuring preferences. Comparative analyses further underscore its robustness in handling CIF-based data and delivering reliable results.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"185 ","pages":"Article 113864"},"PeriodicalIF":6.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183692","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 unsupervised framework for drift-aware anomaly detection in streaming time series","authors":"Danlei Li ,&nbsp;Nirmal-Kumar C. Nair,&nbsp;Kevin I-Kai Wang","doi":"10.1016/j.asoc.2025.113903","DOIUrl":"10.1016/j.asoc.2025.113903","url":null,"abstract":"<div><div>This paper presents an unsupervised adaptive drift-aware anomaly detection framework (ADA-ADF) designed to address the challenges of concept drift in time series data streams. ADA-ADF integrates a hybrid drift detection mechanism, combining statistical tests with performance-based metrics to accurately identify and distinguish between sudden and incremental drifts. To ensure effective adaptation, it employs a replay-based model update strategy that adjusts replay ratios in a drift-specific manner and incorporates representative historical data based on reconstruction errors. This approach allows the model to seamlessly adapt to evolving data distributions while maintaining high stability and accuracy. Extensive experiments on four diverse datasets demonstrate ADA-ADF’s superior performance in managing various drift and application scenarios. It consistently outperforms state-of-the-art methods, particularly in environments characterized by incremental or sudden drifts. With robust adaptability to changing data patterns and accurate anomaly detection capabilities, ADA-ADF provides a reliable solution for real-world applications, such as IoT and environmental monitoring.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"185 ","pages":"Article 113903"},"PeriodicalIF":6.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221558","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 MADM model using Frank operations based power aggregation operator under p,q-quasirung orthopair fuzzy sets for highway selection in war-plane landing","authors":"Sanjita Giri ,&nbsp;Sankar Kumar Roy ,&nbsp;Muhammet Deveci","doi":"10.1016/j.asoc.2025.113918","DOIUrl":"10.1016/j.asoc.2025.113918","url":null,"abstract":"<div><div>In military logistics and operational planning, selecting an optimal highway for war-plane landings and take-offs is a critical and strategic decision. This process involves several key factors that directly affect mission success, operational safety, and public security. Among the most important attributes are the highway’s straight and long stretch with sufficient width to accommodate war-plane landing distances, and its surface condition, which must be free from obstacles, debris, and damage. Low traffic density is crucial to avoid the risk of collisions during landing. Additionally, favourable weather conditions, proximity to military camps, availability of emergency services and fuel, and a secure and hazard-free surrounding terrain are essential for safe and efficient operations. These factors collectively form the backbone of a reliable and tactical approach to highway selection for military air operations. Thus, in order to assess and rank various options for the landing and take-off of war planes, a strong and trustworthy procedure for making decisions is required. The purpose of this experiment is to build a comprehensive structure in multi-attribute decision making environment, using suggested <span><math><mi>p</mi><mo>,</mo><mi>q</mi></math></span>-quasirung orthopair fuzzy Frank power averaging as well as <span><math><mi>p</mi><mo>,</mo><mi>q</mi></math></span>-quasirung orthopair fuzzy Frank power geometric operators to capture ambiguity and uncertainty in highway selection. Furthermore, <span><math><mi>p</mi><mo>,</mo><mi>q</mi></math></span>-quasirung orthopair fuzzy Frank power weighted aggregation along with <span><math><mi>p</mi><mo>,</mo><mi>q</mi></math></span>-quasirung orthopair fuzzy Frank power weighted geometric operators are implemented for integrating the distance as well as similarity measures. Finally, sensitivity analysis and a comparison with the present technique are included to further demonstrate the superiority and validity of the technique that is suggested.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"185 ","pages":"Article 113918"},"PeriodicalIF":6.6,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159675","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":"Interval-valued Pythagorean fuzzy distance-based extended inferior ratio method for multiattribute decision-making: Application to green supplier selection in manufacturing industry","authors":"Zhe Liu ,&nbsp;Donglai Wang ,&nbsp;Muhammet Deveci ,&nbsp;Sukumar Letchmunan","doi":"10.1016/j.asoc.2025.113935","DOIUrl":"10.1016/j.asoc.2025.113935","url":null,"abstract":"<div><div>Interval-valued Pythagorean fuzzy sets (IVPFSs) have emerged as a powerful tool for handling uncertainty and vagueness in multiattribute decision-making (MADM). In this paper, we first propose a novel distance measure for IVPFSs based on triangular divergence, which satisfies all core distance axioms and significantly improves discrimination ability compared to existing measures. Building on this, we introduce a maximizing deviation strategy with a new loss function to objectively determine attribute weights. Furthermore, we develop an extended inferior ratio (EIR) method that incorporates a dynamic weight parameter to flexibly balance the influence of positive and negative ideal solutions. The performance of the proposed method is demonstrated through a case study on green supplier selection in the manufacturing industry. The results indicate that, among the seven criteria evaluated, the most suitable suppliers are ranked as follows: <span><math><mi>β</mi></math></span> (1.0000), <span><math><mi>α</mi></math></span> (0.6471), <span><math><mi>δ</mi></math></span> (0.3500), <span><math><mi>ϵ</mi></math></span> (0.0690), and <span><math><mi>θ</mi></math></span> (0.0000). In addition, sensitivity and comparative analyses confirm the robustness and consistency of the proposed method, reflecting its effectiveness and practical value for sustainable decision-making in real-world scenarios.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"185 ","pages":"Article 113935"},"PeriodicalIF":6.6,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159715","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":"Deep cross-visual semantic hashing with self-calibrated collaborative attention","authors":"Hao Feng,&nbsp;Xiangbo Zhou,&nbsp;Yue Wu,&nbsp;Jian Zhou,&nbsp;Banglei Zhao","doi":"10.1016/j.asoc.2025.113937","DOIUrl":"10.1016/j.asoc.2025.113937","url":null,"abstract":"<div><div>Deep hashing has garnered considerable attention due to its remarkable retrieval efficiency and low storage cost, particularly in visual retrieval scenarios. However, current deep hashing methods generally integrate hash coding into a single-stream architecture, which limits the discriminative power of learned visual features and yields suboptimal hash codes. Additionally, over-reliance on semantic labels shared across samples fails to fully exploit the intrinsic semantic correlations between labels and corresponding visual features. To address these issues, we propose a deep cross-visual semantic hashing (DCvSH) method for image retrieval. First, we develop a visual image feature decoupling encoding network that leverages a self-calibrated collaborative attention mechanism to disentangle common and specific semantics across related images. These decoupled features are fed into a shared decoder for image reconstruction, yielding discriminative visual feature representations. Second, we construct a cross-visual semantic representation learning network with a two-level multi-layer perceptron to capture the underlying relationships between semantic label encodings and visual feature embeddings, while a hypergraph structure is introduced to preserve pairwise similarity relationships. Experimental results on the CIFAR-10, NUS-WIDE, and MIRFLICKR datasets demonstrate consistent improvements, with average mean average precision (mAP) scores reaching 0.895, 0.874, and 0.881 at different code lengths, respectively. Notably, DCvSH outperforms other baselines across all evaluation metrics.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"185 ","pages":"Article 113937"},"PeriodicalIF":6.6,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119772","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":"Fusing local density and approximate distance for nonparametric outlier detection","authors":"Zhiyu Chen ,&nbsp;Can Gao ,&nbsp;Jie Zhou ,&nbsp;Ying Yu","doi":"10.1016/j.asoc.2025.113898","DOIUrl":"10.1016/j.asoc.2025.113898","url":null,"abstract":"<div><div>Outlier detection is an essential yet challenging task in intelligent data analysis, and some density-based unsupervised methods have been introduced to identify outliers in low-density regions. However, these methods still suffer from inaccurate density estimation and limited capability in detecting diverse types of outliers. In this study, we propose a nonparametric outlier detection method with the fusion of density and distance (POD-FDD). The proposed method employs adaptive kernel density estimation based on natural neighborhoods, which reduces the sensitivity to parameters in density estimation. Moreover, the optimistic and pessimistic densities are introduced to enhance the reliability of density estimation in the local neighborhood. In addition, approximate reachability distance information is integrated to improve the capability of identifying cluster outliers. Ultimately, a robust parametric-free outlier detection method is developed to detect different types of outliers. Extensive comparative experiments and statistical significance analysis on synthetic and public datasets demonstrate its superior performance, achieving an average improvement of 1.97 % in the AUC metric.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"185 ","pages":"Article 113898"},"PeriodicalIF":6.6,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158921","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}