Artificial Intelligence Review最新文献

{"title":"Multigroup cooperative evolutionary optimization algorithm combined with quantum entanglement for cross-field applications","authors":"Zhaoyang Lian,&nbsp;Bailu Si","doi":"10.1007/s10462-025-11279-7","DOIUrl":"10.1007/s10462-025-11279-7","url":null,"abstract":"<div><p>Swarm intelligence algorithms are a class of bionic probabilistic heuristic search methods that are inspired by the collective behaviors of biological agents. In this paper, a multigroup cooperative evolutionary optimization algorithm is proposed by referring to the interaction behaviors of species diversity and stability in the ecosystem. First, the group updating mechanism of the traditional seeking and tracking mode with a dynamic population update mechanism is adopted. The multi-population interactive update group and the quantum entanglement update group are introduced to guide the algorithm to gradually approach the global optimal solution. Second, the proposed bionic algorithm is extended for cross-field applications. The algorithm is applied to solve the function optimization problems, as well as problems in four distinct application fields, including robot routing optimization of grid maps, vehicle scheduling optimization of dairy enterprises, location optimization of logistics centers, and plasma trajectory planning optimization. The proposed multigroup cooperative evolutionary optimization algorithm achieves competitive results in these application fields, thus demonstrating its versatility and robustness.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 10","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11279-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145145705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cuckoo catfish optimizer: a new meta-heuristic optimization algorithm","authors":"Tian-Lei Wang,&nbsp;Shao-Wei Gu,&nbsp;Ren-Ju Liu,&nbsp;Le-Qing Chen,&nbsp;Zhu Wang,&nbsp;Zhi-Qiang Zeng","doi":"10.1007/s10462-025-11291-x","DOIUrl":"10.1007/s10462-025-11291-x","url":null,"abstract":"<div><p>A new meta-heuristic algorithm, Cuckoo Catfish Optimizer (CCO), is proposed for numerical optimization problems. It simulates the search, predation, and parasitic behavior observed in cichlids. Early iterations of the algorithm focus on executing a multidimensional enveloping search strategy and a compressed space strategy, combined with an auxiliary search strategy to efectively limit the escape space of cichlids. This phase ensures extensive exploration of the solution space. In the intermediate stage of iteration, the algorithm uses a transition strategy to promote a smooth transition from exploration to exploitation, endowing the algorithm with both a certain degree of exploration capability and exploitation capability. In later stages, the algorithm uses chaotic predation mechanisms to create disturbances around cichlids to improve the exploitation of optimal solutions. Throughout the entire optimization process, the guidance, parasitism, and death mechanisms of individuals are integrated, allowing individuals to adjust their positions in real-time and improve the overall convergence accuracy. This paper rigorously evaluates the performance of CCO through 23 classic test functions and three CEC test suites. The experimental results show that compared with 11 famous algorithms and 10 novel improved algorithms, CCO can obtain the optimal solution in 91.52% of the test functions, demonstrating its excellent ability in solving various numerical optimization problems. Additionally, through the successful application to 6 mechanical optimization problems, 3 photovoltaic cell parameter optimization problems, and 1 path opti- mization problem, the competitiveness of CCO in solving real-world problems is verified and highlighted. The CCO source code can be downloaded here: https://ww2.mathworks.cn/matlabcentral/fileexchange/176828-cuckoo-catfish-optimizer-a-new-meta-heuristic-optimization</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 10","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11291-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145145608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MAAPO: an innovative membrane algorithm based on artificial protozoa optimizer for multilevel threshold image segmentation","authors":"Xiaopeng Wang,&nbsp;Václav Snášel,&nbsp;Seyedali Mirjalili,&nbsp;Jeng-Shyang Pan","doi":"10.1007/s10462-025-11319-2","DOIUrl":"10.1007/s10462-025-11319-2","url":null,"abstract":"<div><p>This paper proposes a novel membrane algorithm based on artificial protozoa optimizer (MAAPO) for global optimization problems. The artificial protozoa optimizer (APO) is adopted as the base meta-heuristic algorithm due to its novelty and competitive performance. MAAPO integrates two key innovations: (1) a membrane computing (MC) framework that introduces a parallel distributed paradigm to improve population diversity and search dynamics, and (2) an enhanced autotrophic model within APO that uses a roulette-based fitness-distance balance (RFDB) mechanism for adaptive reference point selection. These strategies collectively enhance the algorithm’s exploration-exploitation balance and global search capabilities. To validate its performance, MAAPO is tested against 12 advanced algorithms on the CEC2017 test suite, and further applied to the multilevel thresholding image segmentation problem using Otsu and Kapur entropy as objective functions. The quality of segmented images is assessed using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and feature similarity index (FSIM) metrics. Experimental results demonstrate that MAAPO outperforms its counterparts, delivering superior segmentation quality. This research on MAAPO contributes an effective enhancement strategy to meta-heuristic algorithms and introduces a novel, highly applicable approach for complex image segmentation tasks. The source codes of MAAPO are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/181534-maapo.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 10","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11319-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145145057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Kirchhoff’s law algorithm (KLA): a novel physics-inspired non-parametric metaheuristic algorithm for optimization problems","authors":"Mojtaba Ghasemi,&nbsp;Nima Khodadadi,&nbsp;Pavel Trojovský,&nbsp;Li Li,&nbsp;Zulkefli Mansor,&nbsp;Laith Abualigah,&nbsp;Amal H. Alharbi,&nbsp;El-Sayed M. El-Kenawy","doi":"10.1007/s10462-025-11289-5","DOIUrl":"10.1007/s10462-025-11289-5","url":null,"abstract":"<div><p>This research introduces Kirchhoff’s Law Algorithm (KLA), a novel optimization method inspired by electrical circuit laws, particularly Kirchhoff’s Current Law (KCL). The KLA is evaluated using real-parameter test functions including CEC-2005, 2014, and 2017, comparing its performance with several established algorithms. Results from real-parameter and constrained benchmark functions affirm KLA’s accuracy and convergence rate superiority compared to other algorithms. Notably, when applied to the CEC-2005 benchmarks with dimensions ranging from 30 to 100, KLA demonstrates a remarkable ability to maintain population diversity throughout the search process within a feasible search space. Based on the average rank criteria, KLA consistently outperforms other algorithms despite its simplicity and lack of control parameters (aside from population size). This inherent simplicity makes KLA easy to use as-is, adaptable, and compatible with other optimization techniques. The source codes of the KLA algorithm are publicly available at https://nimakhodadadi.com/algorithms-%2B-codes.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 10","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11289-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145145056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-informed neural networks for PDE problems: a comprehensive review","authors":"Kuang Luo,&nbsp;Jingshang Zhao,&nbsp;Yingping Wang,&nbsp;Jiayao Li,&nbsp;Junjie Wen,&nbsp;Jiong Liang,&nbsp;Henry Soekmadji,&nbsp;Shaolin Liao","doi":"10.1007/s10462-025-11322-7","DOIUrl":"10.1007/s10462-025-11322-7","url":null,"abstract":"<div><p>As AI for Science continues to grow, Physics-informed neural networks (PINNs) have emerged as a transformative approach within the realm of scientific computing and deep learning, offering a robust and flexible framework for solving partial differential equations (PDEs) and other complex physical systems. By embedding physical laws directly into the architecture of neural networks, PINNs enable the integration of domain-specific knowledge, ensuring that the models adhere to known physics while fitting available data. In this paper, we provide a comprehensive overview of the state-of-the-art advancements and applications of PINNs across a broad spectrum of PDE problems. In particular, focus is given on the PINN architectures, data resampling methods for PINN, loss and activation functions, feature embedding methods and so on. What’s more, the potential future directions and the anticipated evolution of PINNs are also discussed. We aim to provide valuable insights into PINNs for PDE problems, with hope to encourage further exploration and research in this promising area.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 10","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11322-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145144549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plant leaf disease detection and classification using convolution neural networks model: a review","authors":"Tanko Daniel Salka,&nbsp;Marsyita Binti Hanafi,&nbsp;Sharifah M. Syed Ahmad Abdul Rahman,&nbsp;Dzarifah Binti Mohamed Zulperi,&nbsp;Zaid Omar","doi":"10.1007/s10462-025-11234-6","DOIUrl":"10.1007/s10462-025-11234-6","url":null,"abstract":"<div><p>Plants play a vital role in providing food on a global scale. Several environmental factors contribute to the occurrence of plant leaf diseases, leading to substantial reductions in crop yields. Nevertheless, the process of manually detecting plant leaf diseases is both time-consuming and prone to errors. Adopting deep learning technologies can address these challenges, and the efficacy of deep learning techniques in precision agriculture has been explored over the past decades. However, despite these applications, several gaps in plant leaf disease research still need to be addressed for efficient disease control. This paper, therefore, provides an in-depth review of the trends in using convolutional neural networks for leaf disease detection and classification. In addition, we also present the existing plant leaf disease datasets. It was found that convolutional neural network models, such as VGG, EfficientNet, GoogleNet, and ResNet, provide the highest accuracy in classifying plant leaf disease images. This review will provide valuable information for scholars who are seeking effective deep learning-based classifiers for plant leaf disease detection and classification.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 10","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11234-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145144550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive systematic literature review on artificial intelligence for error correction and modulation schemes in next-generation satellite communications","authors":"Ekta Sharma,&nbsp;Christopher P. Davey,&nbsp;Ravinesh C. Deo,&nbsp;Brad D. Carter,&nbsp;Sancho Salcedo-Sanz","doi":"10.1007/s10462-025-11317-4","DOIUrl":"10.1007/s10462-025-11317-4","url":null,"abstract":"<div><p>Communication systems continue to embrace the potential of Artificial Intelligence (AI) in error correction codes (ECC) with coded modulation schemes (CMS). Despite this, there remains a substantial performance gap in AI methods in terrestrial and satellite communication systems. Additionally, AI and power efficiency for Low Earth Orbit (LEO) satellites have shown a critical gap. To the best of the author’s knowledge, this is the first Systematic literature review attempting to bridge this vital gap to boost efficiency and add fault tolerance. From 389 articles published between 1993 and 2023, the construction and performance of 33 AI algorithms have been comprehensively reviewed for 16 ECC, seven higher-order CMS, and LEO satellites. Based on four key parameters: error correction, modulation, power, and energy efficiency, the PRISMA strategy with a 27-item checklist was adopted and 63 studies were selected to investigate the AI-based performance of terrestrial (40-studies) and LEO satellites (23-studies). Analysing nine performance metrics, Convolutional Neural Network was the most popular choice (20.6%) with an accuracy of 99% and SNR from 6-20dB, followed by Deep Neural Network (19.04%). The least used algorithm was Reinforcement learning (9.52%). Modified Reed Solomon codes showed the best measurement of power consumption and error rate. Adaptive LDPC codes provided a 45% increase in energy efficiency with an 11% computation decrease. Considering appropriate merits and challenges, the review identifies, discusses, and synthesises AI results to create a summary of current evidence for terrestrial and LEO satellites contributing to evidence-based practice for future researchers.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 10","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11317-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145144338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Informed machine learning to reconcile interpretability with fidelity in scientific applications","authors":"Andrea Murari,&nbsp;Riccardo Rossi,&nbsp;Luca Spolladore,&nbsp;Ivan Wyss,&nbsp;Michela Gelfusa","doi":"10.1007/s10462-025-11282-y","DOIUrl":"10.1007/s10462-025-11282-y","url":null,"abstract":"<div><p>Notwithstanding their impressive performances, unfortunately some of the most powerful machine learning (ML) models are obscure and almost impossible to interpret. Consequently, in the last years, there has been a rapid increase in research about eXplainable Artificial Intelligence, whose objective consists of improving their transparency. In scientific applications, explainability assumes a different flavour and cannot be reduced to pure user understanding but there is a premium also on <i>fidelity</i>, on developing models that reflect the actual mechanisms at play in the investigated phenomena. To this end, Genetic Programming supported Symbolic Regression (GPSR), conceived explicitly to manipulate symbols, can present various competitive advantages in finding a good trade-off between interpretability and realism. However, the search spaces are typically too large and the algorithms have to be steered to converge on the desired solutions. The present work describes techniques to constrain GPSR and to combine it with deep learning tools, so that the final models are expressed in terms of interpretable and realistic mathematical equations. The strategies to guide convergence include dimensional analysis, integration of prior information about symmetries and conservation laws, refinements of the fitness function and robust statistics. The performances are improved according to all the main metrics: accuracy, robustness against noise and outliers, capability of handling data sparsity and interpretability. Great attention has been paid to introducing practical solutions, covering most essential aspects of the data analysis process, from the treatment of the uncertainties to the quantification of the equations’ complexity. All the main applications of supervised ML, from regression to classification, are considered (and the extension to unsupervised and reinforcement learning are not expected to pose major difficulties). Theoretical considerations, systematic numerical tests, simulations with multiphysics codes and the results of actual experiments prove the potential of the proposed improvements.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 10","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11282-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145167079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in environmental and Earth system sciences: explainability and trustworthiness","authors":"Josepha Schiller,&nbsp;Stefan Stiller,&nbsp;Masahiro Ryo","doi":"10.1007/s10462-025-11165-2","DOIUrl":"10.1007/s10462-025-11165-2","url":null,"abstract":"<div><p>Explainable artificial intelligence (XAI) methods have recently emerged to gain insights into complex machine learning models. XAI can be promising for environmental and Earth system science because high-stakes decision-making for management and planning requires justification based on evidence and systems understanding. However, an overview of XAI applications and trust in AI in environmental and Earth system science is still missing. To close this gap, we reviewed 575 articles. XAI applications are popular in various domains, including ecology, engineering, geology, remote sensing, water resources, meteorology, atmospheric sciences, geochemistry, and geophysics. XAI applications focused primarily on understanding and predicting anthropogenic changes in geospatial patterns and impacts on human society and natural resources, especially biological species distributions, vegetation, air quality, transportation, and climate-water related topics, including risk and management. Among XAI methods, the SHAP and Shapley methods were the most popular (135 articles), followed by feature importance (27), partial dependence plots (22), LIME (21), and saliency maps (15). Although XAI methods are often argued to increase trust in model predictions, only seven studies (1.2%) addressed trustworthiness as a core research objective. This gap is critical because understanding the relationship between explainability and trust is lacking. While XAI applications continue to grow, they do not necessarily enhance trust. Hence, more studies on how to strengthen trust in AI applications are critically needed. Finally, this review underlines the recommendation of developing a “human-centered” XAI framework that incorporates the distinct views and needs of multiple stakeholder groups to enable trustworthy decision-making.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 10","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11165-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145167078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discriminative projective dictionary pair based broad metric learning system: algorithm and its applications in pattern classification","authors":"Junwei Duan,&nbsp;Yutong Zou","doi":"10.1007/s10462-025-11324-5","DOIUrl":"10.1007/s10462-025-11324-5","url":null,"abstract":"<div><p>Pattern classification plays a pivotal role in a wide range of domains, including computer vision and healthcare. The Broad Learning System (BLS) has attracted considerable attention for its competitive classification performance and computational efficiency. However, its reliance on randomly initialized parameters and lack of iterative updates often lead to performance instability. Directly applying backpropagation to refine these parameters may further result in overfitting. To address these limitations, this research propose a novel framework called the Discriminative Projective Dictionary Pair-based Broad Metric Learning System (D-BMLS). The foundation of this system is the Broad Metric Learning System (BMLS), which integrates a metric subsystem that employs iterative learning to reduce sensitivity to random initialization while leveraging the structural advantages of metric learning to suppress overfitting. Although this improves robustness, it can also introduce computational overhead and still struggle with nonlinear data modeling due to the dual-mapping structure of BLS. To overcome these challenges, D-BMLS incorporates Discriminative Projective Dictionary Pair Learning, which encodes input data into a low-dimensional, linearly separable space. This reduces the number of learnable parameters and enhances the model’s capacity to capture nonlinear relationships through linear transformations. Extensive experiments on five different tasks including image classification, signal recognition, and high-dimensional feature analysis demonstrate the superior performance of D-BMLS. Ablation studies on three benchmark datasets verify the contributions of each component, and results on a synthetic dataset highlight the metric subsystem’s effectiveness in mitigating overfitting.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 10","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11324-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145144172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}