Applied Soft Computing最新文献

{"title":"Multiple unmanned surface vehicles pathfinding in dynamic environment","authors":"Yuqing Lin,&nbsp;Liang Du,&nbsp;Kum Fai Yuen","doi":"10.1016/j.asoc.2025.112820","DOIUrl":"10.1016/j.asoc.2025.112820","url":null,"abstract":"<div><div>In the burgeoning field of autonomous maritime operations, efficiently coordinating and navigating multiple unmanned surface vehicles (USVs) in dynamic environments is a significant challenge. This study presents an enhanced Q-learning algorithm designed to improve pathfinding for multiple USVs in such settings. The algorithm innovates on the traditional Q-learning framework by adjusting the learning rate, Epsilon-greedy strategy, and penalty and reward functions, integrating a collision avoidance mechanism specifically tailored for complex maritime navigation. Extensive simulations across six diverse scenarios – ranging from single to multiple USVs operations in both static and dynamic obstacle environments – demonstrate the algorithm’s superior adaptability and efficiency compared to existing methods. Notably, in single USV scenarios, the improved Q-learning algorithm not only plots more direct paths but also reduces computational demands significantly over traditional path planning methods such as the A<span><math><msup><mrow></mrow><mrow><mo>∗</mo></mrow></msup></math></span> and APF algorithms. In multi-USV scenarios, it demonstrates robust performance, reducing calculation times by an average of 55.51% compared to SARSA, 49.14% compared to the original Q-learning, and 45.26% compared to the Speedy Q-learning approach. These advancements underscore the algorithm’s potential to enhance autonomous maritime navigation, laying a strong foundation for future improvements in the safety and efficiency of USV operations.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"172 ","pages":"Article 112820"},"PeriodicalIF":7.2,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419946","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":"Taxonomy of metrics for effectively estimating quantum software projects: A fuzzy-AHP based analysis","authors":"Mohammad Shameem ,&nbsp;Mohammad Nadeem ,&nbsp;Mahmood Niazi ,&nbsp;Sajjad Mahmood ,&nbsp;Ankur Kumar","doi":"10.1016/j.asoc.2025.112816","DOIUrl":"10.1016/j.asoc.2025.112816","url":null,"abstract":"<div><div>Quantum computing represents a revolutionary shift in computing, yet developing quantum software is significantly more complex than traditional software engineering. Existing research provides limited guidance on estimating costs, development efforts, and timelines within this emerging paradigm. This lack of guidance leaves a critical gap for software organizations aiming to manage quantum projects effectively. To address this gap, the proposed study investigates the key metrics influencing estimation in quantum software development. Through a comprehensive literature review, we identified 13 critical metrics categorized into four groups: technical complexity, resource availability, team expertise, and project environment. In the next phase, a survey-based empirical study was conducted to validate the identified metrics and their categories. Additionally, we applied the fuzzy-AHP method to determine the relative significance of each metric. Our results culminate in a prioritized taxonomical framework that provides a structured approach for managing quantum software development estimations. The findings suggest that adopting the proposed framework can significantly enhance overall project management within the quantum software engineering domain.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"172 ","pages":"Article 112816"},"PeriodicalIF":7.2,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403527","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":"Metaheuristic search algorithms in frequency constrained truss problems: Four improved evolutionary algorithms, optimal solutions and stability analysis","authors":"Hasan Tahsin Öztürk ,&nbsp;Hamdi Tolga Kahraman","doi":"10.1016/j.asoc.2025.112854","DOIUrl":"10.1016/j.asoc.2025.112854","url":null,"abstract":"<div><div>Truss problems with <u>f</u>requency <u>c</u>onstraints (TPFCs) are among the most complex real-world engineering optimization problems in the literature due to the non-linearity of the objective and constraint functions and the geometric structure of the search spaces. These problems have many local solutions due to the irregular geometric structure of the search spaces. Therefore, it is a challenge for meta-heuristic search (MHS) algorithms to converge stably to the global optimum solution for TPFCs. To overcome this challenge, this paper presents four new algorithms with improved performance for the optimization of TPFCs. The methodology of the research and the contributions to the literature are as follows: (i) a TPFC benchmark suite consisting of five different problem types was presented, (ii) for each problem in the benchmark suite, 152 different MHS algorithms were tested and the ones with the best convergence performance were identified, (iii) the update mechanisms of these algorithms that perform competitively on TPFCs were redesigned using the Natural Survivor Method (NSM). Thus, four different MHS algorithms with improved performance were proposed for the optimization of TPFCs, (iv) the optimal solutions for TPFCs were presented, (v) the stability of the proposed algorithms for TPFCs was analyzed and the times and success rates of finding feasible solutions were presented. According to the results of the statistical analysis, the optimal and feasible solutions for the 10/37/52/72/200 bar truss problems were found by the NSM-MadDE, NSM-LSHADE-CnEpSin, NSM-LSHADE-SPACMA and NSM-BO algorithms introduced in this paper.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"171 ","pages":"Article 112854"},"PeriodicalIF":7.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378387","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":"Pythagorean cubic fuzzy multiple attributes group decision method for sustainable supply chain management","authors":"Fei Wang","doi":"10.1016/j.asoc.2025.112802","DOIUrl":"10.1016/j.asoc.2025.112802","url":null,"abstract":"<div><div>A Pythagorean cubic fuzzy set (PCFS) is composed of Pythagorean fuzzy values and interval details. Unlike interval Pythagorean fuzzy sets, PCFS contains more data and can be valuable in complex multi-attribute group decision making (MAGDM). However, as a novel fuzzy set, certain essential principles of PCFS, such as the scoring function's implausibility and the absence of operations, require improvement. To address these concerns, we have refined the PCFS scoring function and introduced a new PCFS operation. Additionally, we have developed a PCFS reliability measure to account for uncertain expert opinions and attribute weights in MAGDM. Furthermore, overcoming the challenge of collecting PCFS evaluation data presents an obstacle. In the context of content distribution, the Heronian-mean (HM) operator tackles attribute association. While most existing Pythagorean-cubic fuzzy aggregation operators have an algebraic nature, we leverage the HM operator to establish a variety of Pythagorean cubic fuzzy aggregation operators. These operators showcase properties such as equivalence, monotonicity, boundedness, and commutative invariance. Finally, grounded in the Pythagorean cubic fuzzy HM aggregation operator, we introduce a MAGDM approach for sustainable supply chain management (SSCM). We conduct a practicality and superiority comparison with the existing Pythagorean cubic fuzzy aggregation operator. The primary contribution of this article is to enrich the research on aggregation operators of PCFS and expand their social applications in the realm of SSCM.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"172 ","pages":"Article 112802"},"PeriodicalIF":7.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387132","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":"Application of personalized individual semantics in MAGDM with preference information","authors":"Hongbin Liu ,&nbsp;Zhuoyu Xu","doi":"10.1016/j.asoc.2025.112822","DOIUrl":"10.1016/j.asoc.2025.112822","url":null,"abstract":"<div><div>Subjective and objective evaluations are often utilized simultaneously in Multi-Attribute Group Decision Making (MAGDM) problems. Decision makers’ different understanding of linguistic terms, i.e., personalized individual semantics, may influence decision making results. In this study, we propose a novel MAGDM model to deal with these problems based on two types of preference information: an objective multi-attribute linguistic decision matrix and subjective alternative rankings. In the first stage, we introduce a consistency-driven model to obtain the personalized interval numerical scales associated with each linguistic term and attribute weights. In the second stage, we introduce dynamic personalized individual semantics to maximize consensus by minimizing the discrepancy between individual opinions and collective opinion. The optimal alternative is then determined based on overall scores of the alternatives. We finally apply the proposed model in the selection of new energy vehicles, and conduct simulation experiments and comparative analysis. The results show that considering personalized individual semantics and weights of the decision makers brings much benefit for consensus reaching process in group decision making. A high group consensus level can be reached in this model, and the computational complexity of this model is acceptable.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"171 ","pages":"Article 112822"},"PeriodicalIF":7.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378388","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 neural network-based intelligent health monitoring system for oil and gas pipelines","authors":"Mohamed Almahakeri ,&nbsp;Ahmad Jobran Al-Mahasneh ,&nbsp;Mohammed Abu Mallouh ,&nbsp;Basel Jouda","doi":"10.1016/j.asoc.2025.112827","DOIUrl":"10.1016/j.asoc.2025.112827","url":null,"abstract":"<div><div>Oil and gas pipelines are critical infrastructures that require continuous monitoring to ensure public safety and prevent economic losses. This paper addresses the challenges associated with pipeline failures by proposing a Deep Neural Network (DNN)-based Structural Health Monitoring (SHM) system for real-time monitoring of oil and gas pipelines. The system utilizes installed transducers and ultrasound guided waves to collect data about the structural health without the need for pipeline shutdown. The DNN-based SHM system predicts three crucial crack parameters: crack location, width, and depth. The performance of the proposed system is compared with five commonly used Machine Learning (ML) approaches. The results demonstrate that the DNN-based SHM system outperforms the other ML-based systems, achieving 18 % less prediction error than the most accurate of the other ML approaches. Moreover, the average prediction accuracy with the proposed DNN approach for crack location, width, and depth were 97 %, 93 % and 96 %, respectively. The findings highlight the potential of DNNs for accurate and efficient pipeline health monitoring, contributing to improved decision-making and safe pipeline operations.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"171 ","pages":"Article 112827"},"PeriodicalIF":7.2,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378386","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":"Two-stage optimized unified adversarial patch for attacking visible-infrared cross-modal detectors in the physical world","authors":"Chengyin Hu ,&nbsp;Weiwen Shi ,&nbsp;Wen Yao ,&nbsp;Tingsong Jiang ,&nbsp;Ling Tian ,&nbsp;Wen Li","doi":"10.1016/j.asoc.2025.112818","DOIUrl":"10.1016/j.asoc.2025.112818","url":null,"abstract":"<div><div>Visible-infrared cross-modal object detectors, leveraging both visible and infrared imaging technologies, play a pivotal role in vision-based systems. However, they necessitate thorough security scrutiny due to the risks posed by physical adversarial attacks, which employ tailored physical inputs to deceive vision-based models, posing significant peril to the integrity of vision-based systems. While previous research has predominantly focused on the security of visible and infrared detectors individually, real-world scenarios often necessitate the use of visible-infrared cross-modal detectors with heightened reliability. However, there is a notable dearth of comprehensive security evaluations for these hybrid systems. Despite some efforts to explore attacks on cross-modal detectors, developing a robust and practical strategy remains a significant challenge. This study introduces TOUAP, a novel two-stage adversarial patch technique designed specifically for real-world, black-box visible-infrared detectors. TOUAP initiates with octagonal-shape optimization to create infrared adversarial samples, leveraging this to disrupt infrared detection. Subsequently, it generates visible patches resembling color QR codes while preserving the geometry of the infrared patch for precise cropping, thereby undermining the visible detector. The extensive experimental validation in both digital and physical domains emphatically underscores the superior effectiveness and robustness of TOUAP, outperforming conventional baseline methods convincingly.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"171 ","pages":"Article 112818"},"PeriodicalIF":7.2,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348764","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":"Small aerial object detection through GAN-integrated feature pyramid networks","authors":"Usman Ahmad ,&nbsp;Jing Liang ,&nbsp;Tianlei Ma ,&nbsp;Kunjie Yu ,&nbsp;Faisal Mehmood ,&nbsp;Farhad Banoori","doi":"10.1016/j.asoc.2025.112834","DOIUrl":"10.1016/j.asoc.2025.112834","url":null,"abstract":"<div><div>Small Aerial Object Detection (SAOD) is a pivotal research domain in computer vision, with significant applications in environmental regulation, intelligent surveillance, and autonomous vehicles. However, SAOD remains challenging due to low resolution, background noise, and variable object sizes. In this study, we propose a novel Feature Pyramid Generative Adversarial Network (FPGAN) to address these issues. FPGAN enhances feature extraction across multiple scales, improving precision, recall, and accuracy in detecting small aerial objects of diverse sizes. Furthermore, we integrate an Edge Sharpening Network (ESN) using the U-Net architecture to mitigate noise and distortions generated during adversarial learning, resulting in the FPGAN+ESN model. Extensive experiments on three SAOD datasets, namely DOTA, COWC, and OGST, demonstrate that our model outperforms state-of-the-art methods, showcasing remarkable improvements in detection accuracy. The proposed FPGAN+ESN approach enhances the resolution of small aerial objects and improves edge quality, leading to more robust and efficient SAOD. Our findings underscore the potential of the FPGAN+ESN model for tackling the complexities associated with SAOD tasks.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"171 ","pages":"Article 112834"},"PeriodicalIF":7.2,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394751","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":"Enhancing prediction accuracy for high-dimensional small-sample-size microarray data cancer by combining chebyshev interpolation with new dual-net GAN","authors":"Liang-Sian Lin ,&nbsp;Yao-San Lin ,&nbsp;Der-Chiang Li ,&nbsp;Susan C. Hu ,&nbsp;Chih-I Huang","doi":"10.1016/j.asoc.2025.112826","DOIUrl":"10.1016/j.asoc.2025.112826","url":null,"abstract":"<div><div>Cancer is a complex and multi-cellular interaction disease in the human immune system with compelling morbidity and mortality in patients. To improve cancer patient survival rate, medical professionals and clinicians commonly formulate prudent and effective cancer treatment plans by using the assistance of human microarray data. Studies reveal that gene expression profiling from microarray data has been frequently used to identify gene mutations in human cancers. But practically, this is an expensive and time-consuming technique when working with high-dimensional microarray cancer datasets. As a result, more recently, deep learning techniques in the field of artificial intelligence (AI) have become a widely effective and worthy technique to implement for cancer diagnosis and prognosis predictions in clinical applications. However, under high-dimensional but small-sample-size (HDSSS), statistically less sufficient patient sample cases, classic deep learning and machine learning models often fail to provide reliable prediction results in cancer diagnosis and prognosis. To tackle these tasks, this study focus effort on developing an AI approach termed Cheby-Dual-GAN virtual sample generation (VSG) technique, which combines Chebyshev interpolation with a novel dual-net GAN (Generative Adversarial Networks) generator, to generate high-dimensional artificial samples resembling the original data. We fed Chebyshev points and their membership function (MF) into the dual-net generator to generate highly representative virtual samples while avoiding mode collapse in GAN. Six microarray cancer datasets were used to demonstrate efficacy of the proposed method. Based on these microarray cancer datasets, we compared the Cheby-Dual-GAN method with two state-of-the-art VSG techniques. With different small training sample sizes, the proposed method can achieve significantly superior prediction accuracy in two predictive models compared to the two VSG techniques in terms of Accuracy (ACC), F-measure, mean absolute error (MAE), and mean absolute percentage error (MAPE) indicators. As a theoretical validation, the paired t-test is employed to determine whether the suggested method has statistically significant differences from the other methods. The results of paired t-test demonstrate that the proposed Cheby-Dual-GAN method enjoys significant improvement effects in terms of ACC, F-measure, MAE, MAPE indicators. According to our experimental results, the proposed method successfully outperforms other VSG methods for HDSSS microarray cancer datasets.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"171 ","pages":"Article 112826"},"PeriodicalIF":7.2,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372908","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":"Integrated generation adversarial and semi-supervised network for Corpus Callosum and Cavum Septum Pellucidum Complex segmentation in fetal brain ultrasound via progressive training","authors":"Qifeng Wang ,&nbsp;Dan Zhao ,&nbsp;Hao Ma ,&nbsp;Xiangjun Yang ,&nbsp;Bin Liu","doi":"10.1016/j.asoc.2025.112767","DOIUrl":"10.1016/j.asoc.2025.112767","url":null,"abstract":"<div><div>In prenatal diagnostics, measuring the Corpus Callosum (CC) is crucial for assessing fetal brain development, with the Cavum Septum Pellucidum (CSP) also playing a key role due to its close association with the CC. Our study addresses the challenge of distinguishing these interconnected structures by segmenting the Corpus Callosum and Cavum Septum Pellucidum Complex (CCSP) as a unified entity in mid-sagittal fetal brain ultrasound images. This approach ensures accurate biological measurements that reflect their combined significance in brain development. To improve segmentation accuracy and reduce errors inherent in manual methods, we propose the Fetal Brian Ultrasound Semi-supervised Generative Adversarial Segmentation Network (FB-SGASNet) tailored for few-shot datasets. FB-SGASNet enhances clinical applicability through: (i) an integrated framework combining the Target Segmentation Module (TSM) and Data Expansion Module (DEM) with a semi-supervised learning strategy; (ii) a progressive training strategy promoting parameter sharing between TSM and DEM; (iii) the introduction of Feature Fusion Attention Module (FFAM) and Dual-Stream Feature Attention Module (DSFAM) to improve key anatomical feature recognition; and (iv) the use of a specialized Fetal Brain CCSP dataset (FB-CCSP) with 200 annotated images for network training and validation. FB-SGASNet provides a practical solution for CCSP segmentation in few-shot datasets, enhancing the efficiency and accuracy of fetal brain ultrasound analysis, reducing reliance on specialized expertise, and enabling more timely prenatal evaluations.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"171 ","pages":"Article 112767"},"PeriodicalIF":7.2,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372801","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}