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Deep learning at the service of metaheuristics for solving numerical optimization problems

Neural computing and Applications 2025年 1-36页

作者： Oyelade, Olaide N. Ezugwu, Absalom E. Saha, Apu K. Thieu, Nguyen V. Gandomi, Amir H. School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Bangladesh Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 United States Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Faculty of Computer Science Phenikaa University Yen Nghia Hanoi Ha Dong12116 Viet Nam Department of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Integrating deep learning methods into metaheuristic algorithms has gained attention for addressing design-related issues and enhancing performance. The primary objective is to improve solution quality and convergence speed within solution search spaces. This study investigates the use of deep learning methods as a generative model to learn historical content, including global best and worst solutions, solution sequences, function evaluation patterns, solution space characteristics, population modification trajectories, and movement between local and global search processes. An LSTM-based architecture is trained on dynamic optimization data collected during the metaheuristic optimization process. The trained model generates an initial solution space and is integrated into the optimization algorithms to intelligently monitor the search process during exploration and exploitation phases. The proposed deep learning-based methods are evaluated on 55 benchmark functions of varying complexities, including CEC 2017 and compared with 13 biology-based, evolution-based, and swarm-based metaheuristic algorithms. Experimental results demonstrate that all the deep learning-based optimization algorithms achieve high-quality solutions, faster convergence rates, and significant performance improvements. These findings highlight the critical role of deep learning in addressing design issues, enhancing solution quality, trajectory, and performance speed in metaheuristic algorithms. © The Author(s) 2025.

关键词： Contrastive Learning

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API-GEN: Accelerator Tool for Generation of REST APIs

API-GEN: Accelerator Tool for Generation of REST APIs

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Senthil Kumar T Jayapradha J Mardeni Bin Roslee Hajna Shaik Sanskriti Garg Chilakala Sudhamani Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India Faculty of Engineering Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Application Programming Interfaces (APIs) are crucial for enabling seamless communication between software systems, allowing them to exchange data and perform tasks efficiently. They underpin essential operations across industries like e-Commerce, social media, and financial services. However, the traditional approach to API development is often labor-intensive, time-consuming, and prone to errors, diverting developers’ focus from innovation to repetitive coding tasks. This paper explores the concept and functionality of APIs, with a focus on automated API generation using JSON and class-based models. By automating this process, developers can streamline access to data from databases, significantly reducing manual effort while improving efficiency and accuracy. The study compares existing methods of API development with automated approaches, highlighting how automation can save time, minimize costs, and enhance the overall software development lifecycle. With its ability to simplify data integration and accelerate development, automated API generation represents a transformative solution for addressing the challenges of modern software systems, making the process faster, error-free, and more efficient.

关键词： Technological innovation Automation Databases Data integration Manuals Software systems Encoding Application programming interfaces Software development management Python

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Survey on AI-Enabled Resource Management for 6G Heterogeneous Networks: Recent Research, Challenges, and Future Trends

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Computers, Materials and Continua 2025年第3期83卷 3585-3622页

作者： Hayder Faeq Alhashimi Mhd Nour Hindia Kaharudin Dimyati Effariza Binti Hanafi Feras Zen Alden Faizan Qamar Quang Ngoc Nguyen Centre of Advanced Communication Research and Innovation (ACRI) Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur 50603 Malaysia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi Mara Shah Alam 40450 Malaysia Centre for Cyber Security Faculty of Information Science and Technology (FTSM) Universiti Kebangsaan Malaysia (UKM) Bangi 43600 Malaysia Faculty of Science and Engineering Waseda University Tokyo 169-8555 Japan Faculty of Telecommunications Posts and Telecommunications Institute of Technology Hanoi 11518 Vietnam

The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time networks. Artificial Intelligence (AI) advancements have contributed to the development of several innovative technologies by providing sophisticated specific AI mathematical models such as machine learning models, deep learning models, and hybrid models. Furthermore, intelligent resource management allows for self-configuration and autonomous decision-making capabilities of AI methods, which in turn improves the performance of 6G networks. Hence, 6G networks rely substantially on AI methods to manage resources. This paper comprehensively surveys the recent work of AI methods-based resource management for 6G networks. Firstly, the AI methods are categorized into Deep Learning (DL), Federated Learning (FL), Reinforcement Learning (RL), and Evolutionary Learning (EL). Then, we analyze the AI approaches according to optimization issues such as user association, channel allocation, power allocation, and mode selection. Thereafter, we provide appropriate solutions to the most significant problems with the existing approaches of AI-based resource management. Finally, various open issues and potential trends related to AI-based resource management applications are presented. In summary, this survey enables researchers to understand these advancements thoroughly and quickly identify remaining challenges that need further investigation.

关键词： Artificial intelligence (AI) resource management deep learning (DL) federated learning (FL) reinforcement learning (RL) evolutionary learning (EL)

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Deep Learning Meets Oversampling: A Learning Framework to Handle Imbalanced Classification

arXiv

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arXiv 2025年

作者： Kishanthan, Sukumar Hevapathige, Asela Faculty of Engineering University of Ruhuna Galle80000 Sri Lanka College of Engineering Computing and Cybernetics The Australian National University CanberraACT2601 Australia

Despite extensive research spanning several decades, class imbalance is still considered to be a profound difficulty for both machine learning and deep learning models. While data oversampling is the foremost technique to address this issue, traditional sampling techniques are often decoupled with the training phase of the predictive model, resulting suboptimal representations. To address this, we propose a novel learning framework that is capable in generating synthetic data instances in a data-driven manner. The proposed framework formulates the oversampling process as a composition of discrete decision criteria, thereby enhancing the representation power of the model’s learning process. Extensive experiments on the imbalanced classification task demonstrates the superiority of our framework over the state-of-the-art algorithms. © 2025, CC BY.

关键词： Contrastive Learning

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Towards Trustworthy Aircraft Safety: Explainable AI for Accurate Incident and Accident Predictions 20th

Towards Trustworthy Aircraft Safety: Explainable AI for Accu...

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20th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2024

作者： Amin, Maryam Noor, Umara Fatima, Manahil Rashid, Zahid Altmann, Jörn Department of Software Engineering Faculty of Computing and Information Technology International Islamic University Islamabad44000 Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul Korea Republic of College of Engineering Seoul National University Seoul Korea Republic of Integrated Major in Smart City Global Convergence Seoul National University Seoul Korea Republic of

ISBN: (纸本)9783031812255

Despite technological advancements, ensuring aircraft safety remains a challenge, however, Machine learning (ML)-based approaches for predicting future incidents play a crucial role in addressing flight safety. As ML models increase in complexity, their decision-making process becomes less transparent, posing significant challenges to trustworthiness. While simpler models demonstrate lower accuracy, more intricate models such as deep neural networks achieve higher accuracy but sacrifice interpretability. In this study, we enhance trustworthiness in aircraft safety prediction by leveraging a dataset of past accidents and incidents to prevent similar accidents from occurring in the future. To achieve this, we apply Random Forest and Extreme Gradient Boosting models to classify different categories of aircraft incidents. Additionally, we apply two powerful explainable artificial intelligence (XAI) techniques: Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) to provide insights into both local and global predictions made by the models. Notably, our results reveal high accuracy in these predictions while maintaining trustworthiness. This research contributes to the advancement of XAI and offers valuable insights for safety-critical applications and decision support systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Aircraft accidents

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Reactive Navigation of the Ackermann Steering Robot in Unknown Environments

Reactive Navigation of the Ackermann Steering Robot in Unkno...

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International Conference on Autonomous Robots and Agents, ICARA

作者： ANA ŠELEK IVAN HRABAR SAŠA ŠTEKOVIĆ Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Autegra D.O.O. Zagreb Croatia

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Mobile robots are now widely used in numerous real-world applications that have complex navigation requirements, especially in environments used by humans. This requires highly accurate navigation that can be performed in realtime. In this paper, a method for generating a smooth motion of nonholonomic mobile robots is proposed. It enables robots to move optimally toward the desired goal and allows fast path replanning when encountering unknown or dynamic obstacles. The method generates smooth, collision-free trajectories based on clothoids, ensuring high computational efficiency and suitability for realtime path planning. By applying a smoothing algorithm, the proposed method improves the robot's efficiency in terms of travel time and trajectory length from start to goal, as demonstrated by a comparison with model predictive control.

关键词： Smoothing methods Navigation Dynamics Real-time systems Trajectory Computational efficiency Mobile robots Collision avoidance Testing Predictive control

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Provenance Graph Kernel

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IEEE Transactions on Knowledge and Data engineering 2025年第6期37卷 3653-3668页

作者： Kohan Marzagao, David Huynh, Trung Dong Helal, Ayah Baccas, Sean Moreau, Luc King's College London Department of Informatics LondonWC2R 2LS United Kingdom Manchester Metropolitan University Department of Computing and Mathematics ManchesterM15 6BX United Kingdom University of Sussex Faculty of Science Engineering and Medicine BrightonBN1 9RH United Kingdom

Provenance is a standardised record that describes how entities, activities, and agents have influenced a piece of data;it is commonly represented as graphs with relevant labels on both their nodes and edges. With the growing adoption of provenance in a wide range of application domains, users are increasingly confronted with an abundance of graph data, which may prove challenging to process. Graph kernels, on the other hand, have been successfully used to efficiently analyse graphs. In this paper, we introduce a novel graph kernel called provenance kernel, which is inspired by and tailored for provenance data. We employ provenance kernels to classify provenance graphs from three application domains. Our evaluation shows that they perform well in terms of classification accuracy and yield competitive results when compared against existing graph kernel methods and the provenance network analytics method while more efficient in computing time. Moreover, the provenance types used by provenance kernels are a symbolic representation of a tree pattern which can, in turn, be described using the domain-agnostic vocabulary of provenance. Therefore, provenance types thus allow for the creation of explanations of predictive models built on them. © 1989-2012 IEEE.

关键词： Kernel Metadata Presence Network Agents Hospitals Accuracy W 3 C Vectors Training Machine Learning Hands Kernel Methods Data Provenance Graph Classification Provenance Analytics Interpretable Machine Learning Graph Kernel Provenance Graph Computation Time Classification Accuracy Kernel Methods Support Vector Machine Classification Task Shortest Path Nodes In The Graph General Types Kings College London Graph Neural Networks Category Labels Graph Topology Node Attributes Network Metrics Node Labels Input Graph Theoretical Complexity Graph Attention Network Edge Labels Best Performing Method Family Of Graphs Artificial Intelligence Training Start Node Time Cost Similar Level Of Performance Classification Decision Group Method Positive Semidefinite

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Machine learning for Internet of Things (IoT) device identification: a comparative study

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PeerJ Computer Science 2025年 11卷 1-34页

作者： Tahaei, Hamid Liu, Anqi Forooghikian, Hamid Gheisari, Mehdi Zaki, Faiz Anuar, Nor Badrul Fang, Zhaoxi Huang, Longjun Institute of Artificial Intelligence Shaoxing University Shaoxing China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia Department of Management Islamic Azad University of Kish Kish Iran Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Selangor Kuala Lumpur Malaysia Department of Computer Science and Engineering Shaoxing University Shaoxing China

The rapid deployment of millions of connected devices brings significant security challenges to the Internet of Things (IoT). IoT devices are typically resource-constrained and designed for specific tasks, from which new security challenges are introduced. As such, IoT device identification has garnered substantial attention and is regarded as an initial layer of cybersecurity. One of the major steps in distinguishing IoT devices involves leveraging machine learning (ML) techniques on device network flows known as device fingerprinting. Numerous studies have proposed various solutions that incorporate ML and feature selection (FS) algorithms with different degrees of accuracy. Yet, the domain needs a comparative analysis of the accuracy of different classifiers and FS algorithms to comprehend their true capabilities in various datasets. This article provides a comprehensive performance evaluation of several reputable classifiers being used in the literature. The study evaluates the efficacy of filter-and wrapper-based FS methods across various ML classifiers. Additionally, we implemented a Binary Green Wolf Optimizer (BGWO) and compared its performance with that of traditional ML classifiers to assess the potential of this binary meta-heuristic algorithm. To ensure the robustness of our findings, we evaluated the effectiveness of each classifier and FS method using two widely utilized datasets. Our experiments demonstrated that BGWO effectively reduced the feature set by 85.11% and 73.33% for datasets 1 and 2, respectively, while achieving classification accuracies of 98.51% and 99.8%, respectively. The findings of this study highlight the strong capabilities of BGWO in reducing both the feature dimensionality and accuracy gained through classification. Furthermore, it demonstrates the effectiveness of wrapper methods in the reduction of feature sets. 2025 Tahaei et al.

关键词： Cyber attacks

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Gasoline and Crude Oil Price Prediction using Multi-headed Variational Neighbour Search-tuned Recurrent Neural Networks

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Computational Economics 2025年 1-36页

作者： Kljajic, Maja Mizdrakovic, Vule Jovanovic, Luka Bacanin, Nebojsa Simic, Vladimir Pamucar, Dragan Zivkovic, Miodrag Business Economics Singidunum University Danijelova 32 Belgrade 11000 Serbia Department of Mathematics Saveetha School of Engineering SIMATS Thandalam Tamilnadu Chennai 602105 India Informatics and Computing Singidunum University Danijelova 32 Belgrade 11000 Serbia Sinergija University Raje Banjičića Bijeljina 76300 Bosnia and Herzegovina University of Belgrade Faculty of Transport and Traffic Engineering Vojvode Stepe 305 Belgrade 11010 Serbia Yuan Ze University College of Engineering Department of Industrial Engineering and Management Taoyuan City 320315 Taiwan Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan İstinye University Faculty of Engineering and Natural Sciences Industrial Engineering Department İstanbul Turkey

The rapidly growing worldwide demand for energy and finite reservoirs of fossil fuels have intensified interest in energy projection research. Artificial intelligence, particularly in time series forecasting, holds significant promise for enhancing predictions of both cost and demand, offering numerous prospective applications over various domains. Numerous variables, ranging from socio-economic conditions to distribution, supply, and international policies, exert influence on global price fluctuations. Thus, crafting precise forecasts necessitates consideration of these multifaceted factors. Through an examination of existing literature, a discernible gap emerges in the quest for advancement in this domain. Consequently, this study proposes to delve into the perspective of employing multi-headed long short-term memory (LSTM) models for gasoline and crude oil price prediction, an area largely untouched by multi-headed approaches. Moreover, recognizing the computational demands of such models, this research emphasizes the development of lightweight methodologies, characterized by a modest neuron within each layer and trained over a limited epoch count. Given the pivotal role of hyper-parameter selection in algorithm performance, an adapted version of the variable neighbour search algorithm is introduced to aid in tuning the model’s architecture and training parameters. A comprehensive side-by-side comparison is undertaken utilizing gasoline and oil data sourced from diverse public repositories, employing a variety of contemporary optimizers. The resultant outcomes are subjected to strict statistical scrutiny to ascertain the robustness and significance of the findings. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Crude oil prices Gasoline prices Modified VNS Multi-headed long short-term memory models Time series prediction

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Machine Learning Model for Wind Power Forecasting Using Enhanced Multilayer Perceptron

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Computers, Materials & Continua 2025年第5期83卷 2287-2303页

作者： Ahmed A.Ewees Mohammed A.A.Al-Qaness Ali Alshahrani Mohamed Abd Elaziz Computer Science and Artificial Intelligence Department College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia Department of Computer Damietta UniversityDamietta34517Egypt College of Physics and Electronic Information Engineering Zhejiang Normal UniversityJinhua321004China Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt

Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy *** enhances the efficiency and reliability of renewable energy *** approaches inform energy management strategies,reduce reliance on fossil fuels,and support the broader transition to sustainable energy *** primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data *** research advances an optimized Multilayer Perceptron(MLP)model using recently proposedmetaheuristic optimization algorithms,namely the FireHawk Optimizer(FHO)and the Non-Monopolize Search(NO).A modified version of FHO,termed FHONO,is developed by integrating NO as a local search mechanism to enhance the exploration capability and address the shortcomings of the original *** developed FHONO is then employed to optimize the MLP for enhanced wind power *** effectiveness of the proposed FHONO-MLP model is validated using renowned datasets from wind turbines in *** results of the comparative analysis between FHONO-MLP,conventionalMLP,and other optimized versions of MLP show that FHONO-MLP outperforms the others,achieving an average RootMean Square Error(RMSE)of 0.105,Mean Absolute Error(MAE)of 0.082,and Coefficient of Determination(R^(2))of 0.967 across all *** findings underscore the significant enhancement in predictive accuracy provided by FHONO and demonstrate its effectiveness in improving wind power forecasting.

关键词： Wind power forecasting multilayer perceptron fire hawk optimizer non-monopolize search

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