Applied Soft Computing最新文献_第4页

Application of multi-objective evolutionary algorithm based on transfer learning in sliding bearing 基于迁移学习的多目标进化算法在滑动轴承中的应用

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-04-11 DOI: 10.1016/j.asoc.2025.113111

Xuepeng Ren, Maocai Wang, Guangming Dai, Lei Peng

{"title":"Application of multi-objective evolutionary algorithm based on transfer learning in sliding bearing","authors":"Xuepeng Ren, Maocai Wang, Guangming Dai, Lei Peng","doi":"10.1016/j.asoc.2025.113111","DOIUrl":"10.1016/j.asoc.2025.113111","url":null,"abstract":"<div><div>In recent years, decomposition-based multi-objective evolutionary algorithms have gained increasing attention for solving complex optimization problems. However, existing weight vector adaptation methods often struggle to balance diversity and convergence. To address this issue, we propose a multi-objective evolutionary algorithm based on transfer learning (MOEA/D-TL), which integrates joint distribution adaptation (JDA) to coordinate the populations generated by genetic and differential operators. The key innovations of MOEA/D-TL include: (1) a dual-operator framework that leverages JDA to integrate the strengths of both operators; (2) auxiliary population labeling using Pareto dominance, leveraging JDA’s characteristics; and (3) sparsity-driven adaptive weight vector adjustment to refine population distribution. Extensive experiments on 44 benchmark problems demonstrate that MOEA/D-TL outperforms nine state-of-the-art algorithms, achieving a 42%–60% improvement across three performance metrics. When applied to the optimization of sliding bearings with conflicting objectives (load capacity, heat generation, and friction coefficient), MOEA/D-TL yields solutions with broader distribution and improved uniformity compared to seven other algorithms. These results validate the algorithm’s capability to balance diversity and convergence effectively.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"176 ","pages":"Article 113111"},"PeriodicalIF":7.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829442","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

Defect recognition network for optical fiber cables based on feature information compensation 基于特征信息补偿的光纤电缆缺陷识别网络

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-04-11 DOI: 10.1016/j.asoc.2025.113139

Shao-Kai Zheng, Sheng-Su Ni, Peng Yan, Hao Wang, Dao-Lei Wang

{"title":"Defect recognition network for optical fiber cables based on feature information compensation","authors":"Shao-Kai Zheng, Sheng-Su Ni, Peng Yan, Hao Wang, Dao-Lei Wang","doi":"10.1016/j.asoc.2025.113139","DOIUrl":"10.1016/j.asoc.2025.113139","url":null,"abstract":"<div><div>The occurrence of electrical corrosion defects in ADSS optical fiber cables presents a significant challenge to the reliable operation of communication lines. Despite the importance of this issue, there has been limited research on accurately detecting electrical corrosion defects in recent years. Moreover, existing defect detection algorithms for industrial issues, such as electrical corrosion in ADSS optical fiber cables, are prone to feature information loss. To address this, we propose an improved Feature Compensation You Only Look Once (FC-YOLO) algorithm for effective detection of electrical corrosion defects in optical cables. First, we proposed the Feature Information Compensated Fusion Network (FICFN), which compensates for fusion features, mitigates the loss of defect information during cross-layer fusion, and enhances feature fusion. Second, an auxiliary training head is integrated into the head network, improving the information expression capability of the FICFN. Finally, an Efficient Local Attention (ELA) mechanism is incorporated into the neck network to boost the localization capabilities of the FICFN. To evaluate the efficacy of the proposed FC-YOLO, we conducted comparison experiments using different mainstream algorithms on both the ADSS electrical corrosion defects dataset and the NEU-DET dataset. Results from the ADSS dataset show that, compared to the YOLOv10s algorithm, the proposed algorithm achieves a 4.7 % increase in mean average precision (mAP@50), reaching 90.2 %, and a 4.1 % improvement in mAP@50–95. These enhancements meet the specifications required for power inspection. On the NEU-DET dataset, the algorithm improved mAP@50 and mAP@50–95 by 8.0 % and 6.1 %, respectively, demonstrating its adaptability for industrial defect detection tasks.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"176 ","pages":"Article 113139"},"PeriodicalIF":7.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829446","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 multi-objective evolutionary algorithm based on a grid with adaptive divisions for multi-objective optimization with irregular Pareto fronts 基于自适应划分网格的不规则Pareto前沿多目标优化算法

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-04-11 DOI: 10.1016/j.asoc.2025.113106

Zhe Liu , Fei Han , Qinghua Ling , Henry Han , Jing Jiang , Qing Liu

{"title":"A multi-objective evolutionary algorithm based on a grid with adaptive divisions for multi-objective optimization with irregular Pareto fronts","authors":"Zhe Liu , Fei Han , Qinghua Ling , Henry Han , Jing Jiang , Qing Liu","doi":"10.1016/j.asoc.2025.113106","DOIUrl":"10.1016/j.asoc.2025.113106","url":null,"abstract":"<div><div>The performance degradation of most existing multi-objective optimization evolutionary algorithms (MOEAs) when tackling multi-objective problems (MOPs) with irregular Pareto fronts is a critical challenge in the field of multi-objective optimization. To address this issue, a novel grid-based MOEA is proposed in this paper. This algorithm dynamically adjusts the number of grid divisions during the optimization process, thereby enabling effective partitioning of the objective space and guiding solution distribution across MOPs with varying Pareto front shapes. Additionally, to enhance diversity preservation, a grid stabilization strategy is proposed to maintain a stable environment for diversity, while a boundary solution protection strategy ensures diversity by promoting exploration of the boundaries. Furthermore, a population reselection method is designed to bolster exploration capabilities within the objective space. Experimental results from benchmark test suites, which include a variety of Pareto front types, demonstrate that our proposed algorithm outperforms seven state-of-the-art MOEAs in addressing both irregular and regular Pareto front MOPs.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"176 ","pages":"Article 113106"},"PeriodicalIF":7.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848446","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

Data-driven oil production strategy selection under uncertainties 不确定条件下数据驱动的采油策略选择

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-04-10 DOI: 10.1016/j.asoc.2025.113108

Gabriel Cirac , Guilherme Daniel Avansi , Jeanfranco Farfan , Denis José Schiozer , Anderson Rocha

{"title":"Data-driven oil production strategy selection under uncertainties","authors":"Gabriel Cirac , Guilherme Daniel Avansi , Jeanfranco Farfan , Denis José Schiozer , Anderson Rocha","doi":"10.1016/j.asoc.2025.113108","DOIUrl":"10.1016/j.asoc.2025.113108","url":null,"abstract":"<div><div>This study presents a user-friendly tool to assist in selecting oil production strategies when facing high levels of uncertainty in a real case. Specifically, we address the challenge of determining optimal well-bore positioning and control parameters under uncertain geological conditions, aiming to maximize production efficiency while managing computational complexity. The model deals with decision-making factors and geological data, represented by high-dimensional maps traditionally handled through intensive numerical methods. The production strategy goes through robust optimization based on decision variables in set <span><math><mi>P</mi></math></span>, such as well-bore positioning, and uncertainties associated with 3D reservoir properties in set <span><math><mi>R</mi></math></span>, such as porosity and permeability. The method combines two sets of <span><math><mi>P</mi></math></span> variables, emphasizing positioning and control guidelines. The technique employs representative scenarios to find a generally applicable strategy considering <span><math><mrow><mi>P</mi><mo>×</mo><mi>R</mi></mrow></math></span> mixtures. The variables <span><math><mi>R</mi></math></span> describe a real and heterogeneous reservoir in the pre-salt area in Brazil. The method focuses on critical information through dimensionality reduction while guaranteeing faster, more accurate, robust decisions and balancing efficiency with effectiveness. We rely upon machine-learning, such as Gradient Boosting Regression with few-shot training strategies. The SHapley Additive exPlanations and feature importance allow the model interpretation, enabling us to understand how the well-bore positioning impacts the response. The method is integrated into the optimization loop to work alongside the simulator, and both methods work in tandem as a fast metaheuristic system supported by a slow numerical one. The method improves the computational footprint by 76%.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113108"},"PeriodicalIF":7.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823546","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 deep learning-based particle contribution evaluation mechanism for meta-heuristic optimization algorithms 基于深度学习的元启发式优化算法粒子贡献评价机制

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-04-10 DOI: 10.1016/j.asoc.2025.113119

Fang Su, Ying Liu, Liquan Chen

{"title":"A deep learning-based particle contribution evaluation mechanism for meta-heuristic optimization algorithms","authors":"Fang Su, Ying Liu, Liquan Chen","doi":"10.1016/j.asoc.2025.113119","DOIUrl":"10.1016/j.asoc.2025.113119","url":null,"abstract":"<div><div>Meta-heuristic algorithms have been a popular research field nowadays. However, they are prone to falling into local optima, especially when applied to the Problem with Weak Influence of Global Optimal Solutions (PWIGOS), where the global optimal solution has a very small influence area in the search space. In this paper, based on the analysis of the influence of PWIGOS on meta-heuristic optimization algorithms, a novel Particle Contribution Evaluation Mechanism (PCEM) is proposed. Different from the current mechanisms in this field, PCEM is innovative in that it uses deep learning models to infer whether a particle is a high contribution particle within the influence region of the global optimum according to the feature information. This provides meta-heuristics with this additional critical information from outside the optimization process to guide the correct evolution of particle population. Additionally, a dynamic threshold setting method and a particle evolution adjustment method are designed, and three different types of classic and representative meta-heuristic algorithms, differential evolution (DE), Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) are selected as application examples of PCEM. Experiments are conducted on 27 benchmark functions, CEC2017 benchmark suite and four real-word problems. According to the statistical results, PCEM not only excels in particle contribution assessment but also significantly enhances algorithm performance, especially when addressing challenging PWIGOS.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"176 ","pages":"Article 113119"},"PeriodicalIF":7.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829456","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 dynamic scheme for collaborative redundant manipulators aided with neural networks in a competitive manner 基于竞争方式的神经网络协同冗余机械臂动态方案

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-04-10 DOI: 10.1016/j.asoc.2025.113115

Ying Kong, Xi Chen, Jiayue Yin

引用次数: 0

Joint hierarchical multi-granularity adaptive embedding discriminative learning for unsupervised domain adaptation 联合分层多粒度自适应嵌入判别学习的无监督域自适应

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-04-10 DOI: 10.1016/j.asoc.2025.113026

Pufei Li , Pin Wang , Yongming Li , Yinghua Shen , Witold Pedrycz

{"title":"Joint hierarchical multi-granularity adaptive embedding discriminative learning for unsupervised domain adaptation","authors":"Pufei Li , Pin Wang , Yongming Li , Yinghua Shen , Witold Pedrycz","doi":"10.1016/j.asoc.2025.113026","DOIUrl":"10.1016/j.asoc.2025.113026","url":null,"abstract":"<div><div>Unsupervised domain adaptation (UDA) is an effective technique that aims to transfer knowledge from well-labeled source data to target data that lacks labels and has a different distribution. Most existing methods only considered domain center-wise alignment to reduce global differences across domains, resulting in a coarse alignment. In recent years, researchers further considered aligning class centers to ensure the consistency of local distributions. However, these methods utilized a solely mean vector to represent the entire class distribution, which is still coarse and cannot fully capture the distribution characteristics of intra-class data. Inspired by the “knowledge pyramid” theory, a novel UDA method termed adaptive hierarchical multi-granularity embedded learning (HMGEL) is proposed to solve this problem, which aims to minimize the distribution gap of samples across domains from the perspective of hierarchical multi-granularity. This method can reflect the distribution of samples from coarse to fine, which is helpful for better UDA. Firstly, granular envelopes are created to explore intra-class structures and complex distributional properties at a more fine-grained level. Based on the granular envelopes, domain centers and class centers are combined for cross-domain distribution alignment, allowing for the capture of sample information at hierarchical multi-granularity from coarse to fine. Then, a robust sample-to-granular envelope cross-domain local structure learning strategy is designed to improve the discrimination capability of target domain features under hierarchical multi-granularity. Extensive experiments on five benchmark datasets show that the proposed HMGEL method is effective at a significant level.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113026"},"PeriodicalIF":7.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821256","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 framework to undersample and refine the synthetic minority set 欠采样和细化合成少数派集的框架

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-04-09 DOI: 10.1016/j.asoc.2025.113095

Payel Sadhukhan

{"title":"A framework to undersample and refine the synthetic minority set","authors":"Payel Sadhukhan","doi":"10.1016/j.asoc.2025.113095","DOIUrl":"10.1016/j.asoc.2025.113095","url":null,"abstract":"<div><div>Oversampling the minority class is a popular strategy for coping with the imbalance of datasets. It improves the cognition of the minority points to an admissible extent. Nonetheless, the synthetic minority instances accentuate the overlap between the majority class and the augmented minority class. It is detrimental to the rightful cognition of both classes. To this end, this paper introduces a novel strategy to undersample the synthetic minority set. A multi-armed bandit (MAB) guided protocol is followed to [i] identify the synthetic minority instances that contribute to the increased overlap between the two classes and [ii] subsequently remove (undersample) them iteratively to obtain a refined synthetic minority set. Simulation on synthetic datasets shows that the proposed strategy is successful in increasing the Gromov–Wasserstein distance between the original majority class distribution and the synthetic minority points’ distribution (as compared to the regular oversampled data obtained through state-of-the-art techniques). Empirical evaluation in sixteen real-world datasets, four state-of-the-art minority oversamplers, and two refinement techniques manifest the competence of the proposed strategy over baseline results and against the two competing methods. The proposed strategy has improved the performance of the majority class without bringing down the minority class’s performance and can be incorporated in sensitive real-world domains.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113095"},"PeriodicalIF":7.2,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816145","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

Advancement of artificial intelligence applications in hydrocarbon well drilling technology: A review 人工智能在油气钻井技术中的应用进展

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-04-09 DOI: 10.1016/j.asoc.2025.113129

Shadfar Davoodi , Mohammed Al-Shargabi , David A. Wood , Mohammad Mehrad

{"title":"Advancement of artificial intelligence applications in hydrocarbon well drilling technology: A review","authors":"Shadfar Davoodi , Mohammed Al-Shargabi , David A. Wood , Mohammad Mehrad","doi":"10.1016/j.asoc.2025.113129","DOIUrl":"10.1016/j.asoc.2025.113129","url":null,"abstract":"<div><div>In recent years, the petroleum upstream has increasingly relied on artificial intelligence (AI), with applications spanning machine/deep learning (ML/DL), hybrid models, and committee machine learning. Particularly in drilling engineering (DE), AI has become crucial for addressing complex subsurface challenges. Nevertheless, its implementation continues to be a significant obstacle owing to the technological, operational, and engineering challenges involved in real-time applications of DE approaches. This review examines AI technologies in DE, focusing on their practicality, performance, and associated challenges. It evaluates models for predicting drilling fluid properties, hole cleaning, rate of penetration, wellbore trajectory, fluid hydraulics, bit wear, borehole stability, subsurface problems, and fault diagnosis. It explores integrating AI models with downhole sensors and surface data for real-time/automated drilling control, alongside real-world AI application cases. It highlights the benefits of combining ML/DL with optimization algorithms in hybrid models and analyzes trends in AI research in DE through bibliometric and scientometric studies. Guidelines are provided for selecting and improving AI algorithms for various drilling applications and assessing their economic impacts. The review concludes by identifying future research directions to advance AI applications in the drilling industry.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"176 ","pages":"Article 113129"},"PeriodicalIF":7.2,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829455","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

Optimizing multi-drone patrol path planning under uncertain flight duration: A robust model and adaptive large neighborhood search with simulated annealing 不确定飞行时间下多无人机巡逻路径规划优化：鲁棒模型和模拟退火自适应大邻域搜索

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-04-09 DOI: 10.1016/j.asoc.2025.113107

Xiaoduo Li , He Luo , Guoqiang Wang , Zhihong Song , Qiwen Gou , Fanhe Meng

{"title":"Optimizing multi-drone patrol path planning under uncertain flight duration: A robust model and adaptive large neighborhood search with simulated annealing","authors":"Xiaoduo Li , He Luo , Guoqiang Wang , Zhihong Song , Qiwen Gou , Fanhe Meng","doi":"10.1016/j.asoc.2025.113107","DOIUrl":"10.1016/j.asoc.2025.113107","url":null,"abstract":"<div><div>When conducting drone path planning, the flight duration of drones is a critical factor influencing the planning solution. Given the characteristics of drone batteries, accurately predicting the actual flight duration is challenging. It is crucial to reduce the impact of uncertain flight duration on path feasibility. To solve this problem, this paper proposes a robust optimization method that constructs a budget uncertainty set to describe the uncertain flight duration. To facilitate the solution process of the model, the strong duality theorem is employed to transform the robust model into a mixed integer linear programming model. To efficiently handle large-scale path planning problems, a hybrid heuristic algorithm with robust feasibility check (ALSA-RFC) is proposed. This algorithm combines the advantages of adaptive large neighborhood search and simulated annealing. Furthermore, to ensure the robustness of the solution, a method for generating robust initial solutions quickly and a robust feasibility checking method for solutions are constructed. Numerical experimental results demonstrate that ALSA-RFC can quickly find high-quality robust solutions. Additionally, through Monte Carlo simulations, the impact of robust parameters on the robustness of the solution scheme is analyzed, evaluating the performance of the algorithm in different scenarios. Comparisons with chance-constrained programming methods revealed that ALSA-RFC can significantly reduce the sensitivity of path planning results to fluctuations in flight duration without substantially increasing flight costs. Finally, a case study is conducted to further validate the practicality of ALSA-RFC in real-world applications.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"176 ","pages":"Article 113107"},"PeriodicalIF":7.2,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829449","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