Intelligent Systems with Applications最新文献_第6页

Sentiment analysis: From rule-based lexicons to large language models 情感分析：从基于规则的词汇到大型语言模型

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-12-01 Epub Date: 2025-11-07 DOI: 10.1016/j.iswa.2025.200599

Maikel Leon

{"title":"Sentiment analysis: From rule-based lexicons to large language models","authors":"Maikel Leon","doi":"10.1016/j.iswa.2025.200599","DOIUrl":"10.1016/j.iswa.2025.200599","url":null,"abstract":"<div><div>This study provides a comprehensive review of two decades of research in opinion mining and sentiment analysis, addressing the fragmentation of prior work across methodologies, application domains, and data sources. The evolution of the field is traced from pre-1990 rule-based systems to lexicon heuristics, statistical learning, machine learning, deep learning, and the current wave of transformer-driven, multimodal, and generative models. Applications are examined across marketing, finance, politics, and social media, with emphasis on how methodological innovations have improved accuracy and enabled broader adoption. Best practices – including transformer fine-tuning, prompt engineering, zero-shot and few-shot learning, multimodal fusion, and domain adaptation – are analyzed to distill evidence-based guidelines for researchers and practitioners. The synthesis shows how sentiment analysis has shaped critical areas, including brand management, investor decision-making, political discourse, and online user engagement. Findings highlight the effectiveness of transformer-based approaches, particularly when combined with domain adaptation and prompt engineering, in delivering state-of-the-art performance. Beyond methodological and applied insights, the study identifies promising directions for future research, including real-time customer journey analytics, explainability in generative AI, robustness across multiple languages, ethical implications, and sustainability considerations. By consolidating dispersed knowledge into a unified account, this review provides both historical grounding and a structured roadmap that advances theoretical understanding and informs managerial practice.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200599"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AGU: Adaptive gradient unlearning for efficient machine unlearning AGU：用于高效机器学习的自适应梯度学习

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-12-01 Epub Date: 2025-10-07 DOI: 10.1016/j.iswa.2025.200592

Naglaa E. Ghannam , Esraa A. Mahareek

{"title":"AGU: Adaptive gradient unlearning for efficient machine unlearning","authors":"Naglaa E. Ghannam , Esraa A. Mahareek","doi":"10.1016/j.iswa.2025.200592","DOIUrl":"10.1016/j.iswa.2025.200592","url":null,"abstract":"<div><div>The right to be forgotten is an essential requirement for machine learning systems under privacy regulations such as the GDPR and CCPA. We introduce Adaptive Gradient Unlearning (AGU), a novel influence-based algorithm designed to efficiently remove the contribution of specified training data while preserving overall model utility. Unlike retraining-based methods, AGU calculates parameter-level gradient sensitivity scores over the forget set to identify which weights are most influenced by the data targeted for deletion. These scores are then normalized and used to adaptively scale gradient updates, selectively erasing data influence without disrupting unrelated knowledge. Convergence is managed via dual stopping criteria based on changes in model parameters and empirical privacy leakage, which is measured by prediction divergence before and after unlearning. AGU achieves strong empirical results on six benchmark datasets including MNIST, CIFAR-10, CIFAR-100, IMDB, UCI Adult, and Tiny-ImageNet-200. In comparison with state-of-the-art methods such as SISA, SCRUB, AmnesiacML, SALUN, Boundary Unlearning, and retraining (ORTR) as a benchmark, AGU yields the best accuracy retention, unlearning times, memory overhead, and privacy leak. For example: AGU achieves an average of 98.3 % on MNIST while unlearning four times faster and using a third of the memory cost in comparison with ORTR. These results make a case for AGU as a practical, scalable data deletion approach with privacy guarantees in an era of deep learning and further extendable into federated and decentralized systems.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200592"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fusing explainable deep learning ensembles and LLM recommendations for real-time plant leaf disease diagnosis 融合可解释的深度学习集合和LLM建议用于实时植物叶片疾病诊断

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-12-01 Epub Date: 2025-10-24 DOI: 10.1016/j.iswa.2025.200596

Dip Kumar Saha , Mohammad Rasel Ahmed , Tushar Deb Nath , Rounakul Islam Boby , Md. Jakir Hossen , M.F. Mridha

{"title":"Fusing explainable deep learning ensembles and LLM recommendations for real-time plant leaf disease diagnosis","authors":"Dip Kumar Saha , Mohammad Rasel Ahmed , Tushar Deb Nath , Rounakul Islam Boby , Md. Jakir Hossen , M.F. Mridha","doi":"10.1016/j.iswa.2025.200596","DOIUrl":"10.1016/j.iswa.2025.200596","url":null,"abstract":"<div><div>Timely and accurate identification of plant leaf diseases plays a vital role in ensuring sustainable agriculture and universal food security. Accurate identification of plant leaf diseases ensures healthier plant cultivation, which is pivotal for sustainable agriculture operations. In this study, we present a plant leaf disease recognition mechanism that utilizes a stacking ensemble structure combined with a Large Language Model (LLM) and Explainable AI (XAI) mechanism to improve identification accuracy and comprehensibility. To capture high textural structure, we utilized the Gray Level Co-occurrence Matrix (GLCM), whereas the MobileNetV3 architecture was utilized to maintain low computational cost in feature extraction. GoogleNet was integrated to improve multi-scale feature extraction by employing inception blocks, which effectively obtain fine-grained details and universal spatial patterns. Our ensemble framework integrates improved versions of MobileNetV3, GoogleNet, and ConvNeXtSmall with CatBoost employed as a nonlinear meta-learner allowing the framework to effectively capture complex connections among the base models within the ensemble framework. Moreover, we utilized additional CNN models, including AlexNet and EfficientNetV2B0, to compare the result of our proposed stacking ensemble model and to evaluate its generalization ability over various architectural designs. In addition, we developed a real-time system integrating an LLM with the proposed ensemble model, ensuring automatic plant leaf disease recognition and delivering corresponding curing recommendations. Our findings contribute to plant-based agriculture by enabling early diagnosis of leaf diseases and providing real-time recommendations through DL and LLM technology.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200596"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The semantic correlation mining method of multimodal data in constructing techno-economic knowledge graph of power grid 构建电网技术经济知识图谱中多模态数据的语义关联挖掘方法

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-12-01 Epub Date: 2025-09-21 DOI: 10.1016/j.iswa.2025.200588

Ling Qiu, Mengqi Pan, Nuoya Lv

{"title":"The semantic correlation mining method of multimodal data in constructing techno-economic knowledge graph of power grid","authors":"Ling Qiu, Mengqi Pan, Nuoya Lv","doi":"10.1016/j.iswa.2025.200588","DOIUrl":"10.1016/j.iswa.2025.200588","url":null,"abstract":"<div><div>Due to the diverse formats and complex structures of multimodal data, effectively managing its complexity and correlations remains challenging. Moreover, when dealing with large-scale data, traditional methods often encounter issues such as low computational efficiency and inaccurate results. This paper proposes a semantic association mining method for multimodal data. This method utilizes ETL technology to convert text and table data from different files into nodes and relational edges in the knowledge graph. By optimizing the word vector matrix through the skip character model, it can better capture the semantic information of text data and accurately reflect semantic similarity. Through integrating nodes such as equipment, design technologies and installation addresses, a technical and economic knowledge graph of the power grid is constructed. For the calculation of multimodal object associations, the data first undergoes label preprocessing, feature processing, and semantic relationship structuring before the association is computed using the cosine similarity formula. By using the association rule algorithm to mine the correlation relationships among time-series variables, potential correlations such as the operating status of equipment and the overall performance of the power grid can be discovered, thereby improving the understanding and prediction ability of the power grid’s operating status. The experimental results demonstrate that the proposed method achieves the highest accuracy and recall rate at 98.20 %, with an F-measure of 93.89 %, a bit error rate below 0.9, and a time consumption of approximately 7.34 s.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200588"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI safety practices and public perception: Historical analysis, survey insights, and a weighted scoring framework 人工智能安全实践和公众认知：历史分析、调查见解和加权评分框架

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-12-01 Epub Date: 2025-10-01 DOI: 10.1016/j.iswa.2025.200583

Maikel Leon

{"title":"AI safety practices and public perception: Historical analysis, survey insights, and a weighted scoring framework","authors":"Maikel Leon","doi":"10.1016/j.iswa.2025.200583","DOIUrl":"10.1016/j.iswa.2025.200583","url":null,"abstract":"<div><div>Artificial Intelligence (AI) safety has evolved in tandem with advances in technology and shifts in societal attitudes. This article presents a historical and empirical analysis of AI safety concerns from the mid-twentieth century to the present, integrating archival records, media narratives, survey data, landmark research, and regulatory developments. Early anxieties (rooted in Cold War geopolitics and science fiction) focused on physical robots and autonomous weapons. In contrast, contemporary debates focus on algorithmic bias, misinformation, job displacement, and existential risks posed by advanced systems, such as Large Language Models (LLMs). This article examines the impact of key scholarly contributions, significant events, and regulatory milestones on public perception and governance approaches. Building on this context, this study proposes an improved LLM safety scoring system that prioritizes existential risk mitigation, transparency, and governance accountability. Applying the proposed framework to leading AI developers reveals significant variation in safety commitments. The results underscore how weighting choices affect rankings. Comparative analysis with existing indices highlights the importance of nuanced, multidimensional evaluation methods. The paper concludes by identifying pressing governance challenges, including the need for global cooperation, robust interpretability, and ongoing monitoring of harm in high-stakes domains. These findings demonstrate that AI safety is not static but somewhat shaped by historical context, technical capabilities, and societal values—requiring the continuous adaptation of both policy and evaluation frameworks to align AI systems with human interests.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200583"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced Online Grooming detection employing Context Determination and Message-Level Analysis 采用上下文确定和消息级分析的增强在线修饰检测

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-12-01 Epub Date: 2025-11-10 DOI: 10.1016/j.iswa.2025.200607

Jake Street, Isibor Kennedy Ihianle, Funminiyi Olajide, Ahmad Lotfi

{"title":"Enhanced Online Grooming detection employing Context Determination and Message-Level Analysis","authors":"Jake Street, Isibor Kennedy Ihianle, Funminiyi Olajide, Ahmad Lotfi","doi":"10.1016/j.iswa.2025.200607","DOIUrl":"10.1016/j.iswa.2025.200607","url":null,"abstract":"<div><div>Online Grooming (OG) is a prevalent threat facing predominately children online, with groomers using deceptive methods to prey on the vulnerability of children on social media/messaging platforms. These attacks can have severe psychological and physical impacts, including a tendency towards revictimization. Current technical measures are inadequate, especially with the advent of end-to-end encryption which hampers message monitoring. Existing solutions focus on the signature analysis of child abuse media, which does not effectively address real-time OG detection. This paper proposes that OG attacks are complex, requiring the identification of specific communication patterns between adults and children alongside identifying other insights (e.g. Sexual language) to make an accurate determination. It introduces a novel approach leveraging advanced models such as BERT and RoBERTa for Message-Level Analysis and a Context Determination approach for classifying actor interactions, between adults attempting to groom children and honeypot children actors. This approach included the introduction of Actor Significance Thresholds and Message Significance Thresholds to make these determinations. The proposed method aims to enhance accuracy and robustness in detecting OG by considering the dynamic and multi-faceted nature of these attacks. Cross-dataset experiments evaluate the robustness and versatility of our approach. This paper’s contributions include improved detection methodologies and the potential for application in various scenarios, addressing gaps in current literature and practices.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200607"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Federated learning using quality-based aggregation method for brain tumour segmentation on multimodality medical images 基于质量的聚合方法的联邦学习在多模态医学图像上的脑肿瘤分割

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-12-01 Epub Date: 2025-11-08 DOI: 10.1016/j.iswa.2025.200601

Rim El Badaoui , Ester Bonmati , Vasileios Argyriou , Barbara Villarini

{"title":"Federated learning using quality-based aggregation method for brain tumour segmentation on multimodality medical images","authors":"Rim El Badaoui , Ester Bonmati , Vasileios Argyriou , Barbara Villarini","doi":"10.1016/j.iswa.2025.200601","DOIUrl":"10.1016/j.iswa.2025.200601","url":null,"abstract":"<div><div>Deep learning for medical imaging has shown great potential in improving patient outcomes due to its high accuracy in disease diagnosis. However, a major challenge preventing the widespread adoption of such models in clinical settings is data accessibility, which conflicts with the General Data Protection Regulation (GDPR) in a traditional centralised training environment. Hence, to address this issue, Federated Learning (FL) was introduced as a decentralised alternative that enables collaborative model training among data owners without sharing any private data. Despite its significance in healthcare, limited research has explored FL for medical imaging, particularly in multimodal brain tumour segmentation, due to challenges such as data heterogeneity.</div><div>In this study, we present Federated E-CATBraTS, an advanced federated deep learning model derived from the existing E-CATBraTS framework. This model is designed to segment brain tumours from multimodal magnetic resonance imaging (MRI) while preserving data privacy. Our framework introduces a novel aggregation method, DaQAvg, which optimally combines model weights based on data size and quality, demonstrating resilience against corrupted medical images.</div><div>We evaluated the performance of Federated E-CATBraTS using two publicly available datasets: UPenn-GBM and UCSF-PDGM, including a degraded version of the latter to assess the efficacy of our aggregation method. The results indicate a 6% overall improvement over traditional centralised approaches. Furthermore, we conducted a comprehensive comparison against state-of-the-art FL aggregation algorithms, including FedAVG, FedProx and FedNova. While FedNova demonstrated the highest overall DSC, DaQAvg demonstrated superior robustness to noisy conditions, showcasing its specific advantage in maintaining performance with variable data quality, a critical aspect in medical imaging.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200601"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semantic SLAM: A comprehensive survey of methods and applications 语义SLAM：方法和应用的综合调查

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-12-01 Epub Date: 2025-11-10 DOI: 10.1016/j.iswa.2025.200591

Houssein Kanso , Abhilasha Singh , Etaf El Zarif , Nooruldeen Almohammed , Jinane Mounsef , Noel Maalouf , Bilal Arain

{"title":"Semantic SLAM: A comprehensive survey of methods and applications","authors":"Houssein Kanso , Abhilasha Singh , Etaf El Zarif , Nooruldeen Almohammed , Jinane Mounsef , Noel Maalouf , Bilal Arain","doi":"10.1016/j.iswa.2025.200591","DOIUrl":"10.1016/j.iswa.2025.200591","url":null,"abstract":"<div><div>This paper surveys the different approaches in semantic Simultaneous Localization and Mapping (SLAM), exploring how the incorporation of semantic information has enhanced performance in both indoor and outdoor settings, while highlighting key advancements in the field. It also identifies existing gaps and proposes potential directions for future improvements to address these issues. We provide a detailed review of the fundamentals of semantic SLAM, illustrating how incorporating semantic data enhances scene understanding and mapping accuracy. The paper presents semantic SLAM methods and core techniques that contribute to improved robustness and precision in mapping. A comprehensive overview of commonly used datasets for evaluating semantic SLAM systems is provided, along with a discussion of performance metrics used to assess their efficiency and accuracy. To demonstrate the reliability of semantic SLAM methodologies, we reproduce selected results from existing studies offering insights into the reproducibility of these approaches. The paper also addresses key challenges such as real-time processing, dynamic scene adaptation, and scalability while highlighting future research directions. Unlike prior surveys, this paper uniquely combines (i) a systematic taxonomy of semantic SLAM approaches across different sensing modalities and environments, (ii) a comparative review of datasets and evaluation metrics, and (iii) a reproducibility study of selected methods. To our knowledge, this is the first survey that integrates methods, datasets, evaluation practices, and application insights into a single comprehensive review, thereby offering a unified reference for researchers and practitioners. In conclusion, this review underscores the vital role of semantic SLAM in driving advancements in autonomous systems and intelligent navigation by analyzing recent developments, validating findings, and highlighting future research directions.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200591"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Federated learning for cyber attack detection to enhance security in protection schemes of cyber-physical energy systems 基于联邦学习的网络攻击检测，提高网络物理能源系统防护方案的安全性

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-12-01 Epub Date: 2025-09-25 DOI: 10.1016/j.iswa.2025.200590

Lei Du, Qingzhi Zhu

{"title":"Federated learning for cyber attack detection to enhance security in protection schemes of cyber-physical energy systems","authors":"Lei Du, Qingzhi Zhu","doi":"10.1016/j.iswa.2025.200590","DOIUrl":"10.1016/j.iswa.2025.200590","url":null,"abstract":"<div><div>Cyber-attacks increasingly target the protection systems that safeguard cyber-physical energy systems (CPES), making it more difficult to deliver security and reliability requirements. The protection schemes in power grids, which depend on real-time forecasts from digital relays and Apple devices, require detection of physical faults and, simultaneously, malicious cyber attacks. This paper developed a decentralized federated learning-based framework to assist with the detection of cyber attacks in the protection schemes of cyber-physical energy systems (CPES), with the goals of privacy preservation and scalability. Attention was paid to the whole range of threats, including false data injection (FDI), man-in-the-middle, replay, and denial of service (DoS) across distributed substations without centralization of raw datasets. A lightweight neural network model was trained locally before being aggregated using federated averaging to develop a collaborative approach to learning across multiple substations. Based on the 3-machine, 9 bus case, simulations were run with synthetic attack datasets. The proposed method achieved an average detection accuracy of 96.7% while also preserving the confidentiality and non-disclosure of data. The study also highlighted some of the challenges related to implementation, conceptual drift, and the computational limits of hosting the solution, thereby providing a better understanding of planning and deploying the solution in smart grid applications.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200590"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

New Harris Hawks algorithms for the Close-Enough Traveling Salesman Problem 足够近旅行商问题的新Harris Hawks算法

IF 4.3

Intelligent Systems with Applications Pub Date : 2025-12-01 Epub Date: 2025-09-22 DOI: 10.1016/j.iswa.2025.200586

Tansel Dokeroglu, Deniz Canturk

{"title":"New Harris Hawks algorithms for the Close-Enough Traveling Salesman Problem","authors":"Tansel Dokeroglu, Deniz Canturk","doi":"10.1016/j.iswa.2025.200586","DOIUrl":"10.1016/j.iswa.2025.200586","url":null,"abstract":"<div><div>This study introduces a novel application of the Harris Hawks Optimization (HHO) algorithm to the Close-Enough Traveling Salesman Problem (CETSP), a challenging combinatorial optimization problem where circular neighborhoods rather than exact coordinates represent target points. To tackle the CETSP’s spatial complexity and high-dimensional solution space, we develop new HHO algorithms, including a parallel multi-population variant designed using the OpenMP framework. This parallel algorithm allows multiple subpopulations to evolve simultaneously, increasing diversity and computational efficiency, particularly on large-scale and real-time instances. Furthermore, new problem-specific exploration and exploitation operators are introduced, tailored to the CETSP’s geometric structure, enabling better guidance of the search process toward high-quality solutions. A comprehensive empirical evaluation is conducted on 47 benchmark instances, encompassing synthetic problem instances and a real-world robotic welding scenario in automotive manufacturing. The results show that the proposed methods outperform existing state-of-the-art techniques such as Genetic Algorithm (GA), Memetic Algorithm (MA-CETSP) and Variable Neighborhood Search (VNS)-based approaches, achieving 18 new best-known solutions. The experimental findings underline the strong convergence behavior, robustness across diverse problem sizes, and practical applicability of the algorithm. Additionally, the algorithm’s modular and extensible structure leads the way for future adaptations to multi-objective and dynamic versions of CETSP, broadening its relevance for both academic research and industrial deployment.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200586"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0