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Application of metaheuristic algorithms with Supervised Machine Learning for Accurate Power Consumption Prediction

COGNITIVE COMPUTATION 2025年第1期17卷 1-35页

作者： Wang, Mengxia Zhu, Chaoyang Zhang, Yunxiang Deng, Jinxin Cai, Yiwei Wei, Wei Guo, Mengxing Commun Univ China Inst Social Innovat & Publ Culture Beijing 100000 Peoples R China Int Engn Psychol Inst US Denver CO 80201 USA Univ Illinois Champaign IL 61820 USA Hainan Vocat Univ Sci & Technol Haikou 570100 Peoples R China Shenzhen High Level Talents Dev Promot Assoc Shenzhen 518000 Peoples R China CDA Int Accelerator Shenzhen 518000 Peoples R China Beijing Inst Technol Shenzhen Res Inst Shenzhen 518000 Peoples R China Univ Wollongong Wollongong City 2223 Australia Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Shandong Open Univ Jinan 250000 Peoples R China

Accurate power consumption prediction is a crucial part of energy management. Some of the machine learning models that are the focus of this study for the prediction of power use include Support Vector Regression, Adaptive Boosting, and Decision Tree Regression. These models have been improved with the use of some novel optimizers-namely, the Trochoid Search Optimization, Red-Tailed Hawk, and Giant Armadillo Optimization methods-for hyper-parameter tuning to enhance prediction accuracy. When tested against real data, DTGA outperformed with R2 values of 0.9918, 0.9924, and 0.9934 for three zones. This work extends the study on the forecast of power consumption by integrating machine learning and optimization techniques that provide effective energy management strategies.

关键词： Power consumption prediction Optimization techniques Support vector regression metaheuristic algorithm Machine learning

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An efficient prediction of punching shear strength in reinforced concrete slabs through boosting methods and metaheuristic algorithms

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STRUCTURES 2025年 74卷

作者： Khajavi, Erfan Khanghah, Amir Reza Taghavi Khiavi, Ali Javadzade Islamic Azad Univ Dept Civil Engn Ardebil Iran Univ Mohaghegh Ardabili Dept Civil Engn Ardebil Iran

Safety and optimum designs in civil engineering applications depend on correctly estimating critical parameters such as punching shear strength (Pu) in concrete slabs. This work, therefore, studies improvement in prediction accuracy and generalization by applying five advanced boosting machine learning methods to overcome traditional empirical methods' drawbacks. Five state-of-the-art boosting models, namely, XGBoost Regression (XGBr), Histogram Gradient Boosting Regression (HGBr), Light Gradient Boosting Regression (LGBr), CatBoost, and AdaBoost, were conducted in modeling Pu prediction. K-fold cross-validation has been conducted to select only the best two models for further elaboration with state-of-the-art optimization algorithms, including Quadratic Interpolation Optimization (QIO), Red-tailed Hawk algorithm (RTH), Giant Armadillo Optimization (GAO), and Greylag Goose Optimization (GGO). Some of the ensemble techniques were also used to enhance robustness and accuracy. Several sensitivity analysis methods were conducted to determine the effect of input parameters on model performance. Indeed, very important insight was provided about major contributing factors. The proposed techniques have certain significant advantages over others. It can process nonlinear relationships, provide a minimum prediction error, and recognize those parameters critical to punching shear. This paper emphasizes how integrating machine learning with optimization and sensitivity analysis can enable the development of stateof-the-art predictive modeling in structural engineering that is reliable and more efficient than traditional methods.

关键词： Punching shear strength Concrete slab Boosting models metaheuristic algorithm Ensemble method

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Prediction of venous clinical severity score in yoga practitioners and non-practitioners using discriminant analysis and metaheuristic algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 278卷

作者： Wang, Fengcai Wang, Fei Jimei Univ Chengyi Coll Dept Phys Educ & Arts Xiamen 361021 Fujian Peoples R China Minist Sports Xiamen Inst Technol Xiamen 361021 Fujian Peoples R China

Venous Clinical Severity Score is a key measure of the severity of venous disease. A new prediction method VCSS for yogic practitioners and non-practitioners using both theoretical knowledge and advanced computational techniques has been presented in this study. Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Robust Sparse Linear Discriminant Analysis were used as classifiers, enhanced by feature selection methods to identify the most critical predictors that improve the mathematical and theoretical interpretability the dataset. In an attempt to enhance model performance, some new hybridization methods were applied: the integration of the Tyrannosaurus Optimization algorithm, Brown Bear Optimization, and Walrus Optimization algorithm. The modeling process, designed with k-fold cross-validation, was carried out carefully to avoid biases and provide robust and reliable predictions. The novelty of this dataset, involving people with and without yoga practice, brings about a newer perspective in understanding the impacts of lifestyles on venous health. These results prove that the hybrid Linear Discriminant Analysis-Brown Bear Optimization model outperforms the others in terms of accuracy, justifying optimization algorithms' role in modern predictive analytics. The contribution of the present study does not stop at underlining the health relevance of yoga but also showcases how feature selection and optimization techniques go hand-in-hand in fine-tuning discriminant models, contributing significantly to health research and predictive modeling.

关键词： Yoga practice Venous Clinical Severity Score Linear Discriminant Analysis Quadratic Discriminant Analysis metaheuristic algorithm

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Structural bias in metaheuristic algorithms: Insights, open problems, and future prospects

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 92卷

作者： Rajwar, Kanchan Deep, Kusum Indian Inst Technol Roorkee Dept Math Roorkee 247667 Uttarakhand India Indian Stat Inst Stat Qual Control & Operat Res Unit Hyderabad 500007 Telangana India

This paper addresses a critical issue of structural bias in metaheuristic algorithms, a key factor that often hinders their effectiveness in solving complex optimization problems. Such biases, typically resulting from the design of algorithmic operators and solution construction processes, can lead to a decrease in performance over time. Despite its importance, structural bias is little understood and rarely explored. Moreover, the theoretical framework for structural bias in this context is notably underdeveloped. To the best of our knowledge, no comprehensive review of structural bias in metaheuristic algorithms is available to date. Consequently, this study is subjected to a thorough literature review, providing the mathematical definition of structural bias, the theoretical background, and an extensive analysis of its various forms within metaheuristic algorithms. This paper discusses structural bias in several metaheuristic algorithms, including the Genetic algorithm, Particle Swarm Optimization, Differential Evolution, and Ant Colony Optimization. Methodologies for identifying structural bias, currently scattered across several studies, are categorized into four classes and discussed through the implementation of Particle Swarm Optimization, highlighting their advantages and limitations. Additionally, five critical open problems are identified, and essential research directions for future exploration are outlined. As the first comprehensive review of structural bias - an issue gaining increasing attention - this work is expected to serve as a vital resource for algorithm designers and the research community.

关键词： metaheuristic algorithm Evolutionary algorithm Structural bias Optimization technique

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Developing machine learning models with metaheuristic algorithms for droplet size prediction in a microfluidic microchannel

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 87卷

作者： Eslami, Faezeh Kamali, Reza Shiraz Univ Sch Mech Engn Shiraz *** Iran

Droplet-based microfluidics holds promise for advanced biomedical applications due to its enhanced accuracy, throughput, rapid reaction time, and minimal reagent consumption. However, achieving droplets with the desired radius often entails costly design iterations. In this paper, we propose a novel approach that combines machine learning algorithms with metaheuristic algorithms to leverage their respective strengths in speed and accuracy. Specifically, we demonstrate the effectiveness of the Rain Optimization algorithm (ROA) in enhancing the accuracy of five machine learning models. Additionally, we introduce a hybrid model, optimized by ROA, for predicting droplet generation in a cross-junction microfluidic channel, achieving a coefficient of determination (R 2 ) of 0.937, reflecting a 3.6 % increase in accuracy. We employ symbolic regression to validate the hybrid model, yielding an R 2 value of 0.987, indicating strong agreement with real data. Furthermore, we utilize the SHAP method in Explainable AI to interpret the hybrid model ' s performance. Notably, the proposed hybrid model requires only four seconds to solve the prediction problem, significantly reducing computational time compared to the numerical model and experimental method. This approach facilitates the study of each input parameter ' s impact on droplet size prediction, offering otherwise challenging and costly insights to obtain experimentally.

关键词： Droplet-based microfluidic metaheuristic algorithm Rain optimization algorithm Particle swarm optimization Symbolic regression Explainable AI

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Role of metaheuristic algorithms in healthcare: a comprehensive investigation across clinical diagnosis, medical imaging, operations management, and public health

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2024年第3期11卷 223-247页

作者： Lameesa, Aiman Hoque, Mahfara Alam, Md Sakib Bin Ahmed, Shams Forruque Gandomi, Amir H. Asian Inst Technol Data Sci & Artificial Intelligence Chang Wat 12120 Pathum Thani Thailand Asian Univ Women Sci & Math Program Chittagong 4000 Bangladesh North South Univ Dept Math & Phys Dhaka 1229 Bangladesh Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW 2007 Australia Obuda Univ Univ Res & Innovat Ctr EKIK H-1034 Budapest Hungary

metaheuristic algorithms have emerged in recent years as effective computational tools for addressing complex optimization problems in many areas, including healthcare. These algorithms can efficiently search through large solution spaces and locate optimal or near-optimal responses to complex issues. Although metaheuristic algorithms are crucial, previous review studies have not thoroughly investigated their applications in key healthcare areas such as clinical diagnosis and monitoring, medical imaging and processing, healthcare operations and management, as well as public health and emergency response. Numerous studies also failed to highlight the common challenges faced by metaheuristics in these areas. This review thus offers a comprehensive understanding of metaheuristic algorithms in these domains, along with their challenges and future development. It focuses on specific challenges associated with data quality and quantity, privacy and security, the complexity of high-dimensional spaces, and interpretability. We also investigate the capacity of metaheuristics to tackle and mitigate these challenges efficiently. metaheuristic algorithms have significantly contributed to clinical decision-making by optimizing treatment plans and resource allocation and improving patient outcomes, as demonstrated in the literature. Nevertheless, the improper utilization of metaheuristic algorithms may give rise to various complications within medicine and healthcare despite their numerous benefits. Primary concerns comprise the complexity of the algorithms employed, the challenge in understanding the outcomes, and ethical considerations concerning data confidentiality and the well-being of patients. Advanced metaheuristic algorithms can optimize the scheduling of maintenance for medical equipment, minimizing operational downtime and ensuring continuous access to critical resources. Graphical Abstract

关键词： metaheuristic algorithm healthcare industry clinical diagnosis public health operations management

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Tuning of cascade PID controller gains of quadcopter under bounded disturbances using metaheuristic based research algorithm

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AERONAUTICAL JOURNAL 2025年

作者： Copur, E. H. Balta, E. Bilgic, H. H. Necmettin Erbakan Univ Dept Aerosp Engn TR-42040 Konya Turkiye Necmettin Erbakan Univ Dept Aeronaut Engn TR-42040 Konya Turkiye

The proportional-integral-derivative (PID) controller remains widely used in industrial applications today due to its simplicity and ease of implementation. However, tuning the controller's gains is crucial for achieving desired performance. This study compares the performance of PID controllers within a cascade control architecture designed for both position and attitude control of a quadcopter. Particle swarm optimisation (PSO), grey wolf optimisation (GWO), artificial bee colony (ABC), and differential evaluation (DE) methods are employed to optimally tune the PID parameters. A set of PID gains is determined offline by minimising various modified multi-objective functions based on different fitness measures: IAE, ISE, ITAE and ITSE. These measures are adapted as fitness functions for position and attitude control. A simulation study is conducted to determine which fitness function yields the optimal PID gains, as evidenced by the lowest values of the objective functions. In these simulations, two different desired trajectories are designed, and the controllers are applied to ensure the quadcopter tracks these trajectories accurately. Additionally, to test the robustness of the flight control architecture and the finely tuned PID controllers against various environmental effects and parametric uncertainties, several case scenarios are also explored.

关键词： Cascade PID controller search algorithm metaheuristic algorithm quadcopter optimization

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Exact and metaheuristic algorithms for Variable Reduction

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2024年第6期28卷 1704-1718页

作者： Song, Aijuan Wu, Guohua Zhou, Ling Wang, Ling Pedrycz, Witold Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2V4 Canada Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland

A variable reduction strategy (VRS) drives evolutionary algorithms (EAs) to evolve more effectively by simplifying optimization problems. To represent a decision space with the smallest set of variables via VRS, a variable reduction optimization problem (VROP) has been defined, which could be handled by a heuristic rule-based automatic variable reduction algorithm (HR-AVRA). However, HR-AVRA has limitations in identifying an optimal solution and is susceptible of getting trapped in local optimums. To handle these challenges, we propose to solve VROP with exact and metaheuristic algorithms. We construct a linear programming model of VROP for the first time. Then, Gurobi, an exact algorithm optimizer, is employed to seek and identify an optimal solution for it. Subsequently, we design two representative single-point-based and population-based metaheuristic algorithms, simulated annealing-based (SA-AVRA) and ant colony optimization-based (ACO-AVRA) methods, to solve VROP more effectively. These variable reduction algorithms can group variables of a continuous optimization problem into reduced and core variables, with the latter representing both reduced variables and the entire decision space. Extensive experiments verify that Gurobi can provide optimal solutions for small-scale VROPs, while SA/ACO-AVRA enables more reduced variables than other variable reduction methods. We also integrate various variable reduction algorithms with several competitive EAs to tackle equality-constrained optimization problems and nonlinear equations systems. Experiments show that EAs integrated with a variable reduction algorithm outperform both standard EAs and those with several other complexity reduction methods. Furthermore, integrating a variable reduction algorithm that introduces more reduced variables generally can lead to better performance of EAs.

关键词： Mathematical models Search problems metaheuristics Standards Simulated annealing Linear programming Diversity reception Continuous optimization problem evolutionary algorithm (EA) exact algorithm metaheuristic algorithm variable reduction optimization problem (VROP)

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A Systematic Review of metaheuristic algorithms in Human Activity Recognition: Applications, Trends, and Challenges

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第2期16卷 731-742页

作者： Kisoi, John Deutero Yusup, Norfadzlan Junaini, Syahrul Nizam Univ Malaysia Sarawak Fac Comp Sci & Informat Technol Kota Samarahan Malaysia

metaheuristic algorithms have emerged as promising techniques for optimizing human activity recognition (HAR) systems. This systematic review examines the application of these algorithms in HAR by analyzing relevant literature published between 2019 and 2024. A comprehensive search across multiple databases yielded 27 studies that met the inclusion criteria. The analysis revealed that Genetic algorithms (GA) exhibit classification accuracy rates ranging from 88.25% to 96.00% in activity recognition and up to 90.63% in localization tasks. Notably, Oppositional and Chaos Particle Swarm Optimization (OCPSO) combined with MI-1DCNN significantly improves detection accuracy, demonstrating a 2.82% improvement over standard PSO with Support Vector Machine (SVM) as classifier approaches. Our analysis highlights a growing trend toward hybrid metaheuristic approaches that enhance feature selection and classifier optimization. However, challenges related to computational cost and scalability persist, underscoring key areas for future research. These findings emphasize the potential of metaheuristic algorithms to significantly advance HAR. Future studies should explore the development of more computationally efficient hybrid models and the integration of metaheuristic optimization with deep learning architectures to enhance system robustness and adaptability.

关键词： metaheuristic algorithm human activity recognition systematic review application trend challenge literature

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The aerodynamic force estimation of a swept-wing UAV using ANFIS based on metaheuristic algorithms

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AERONAUTICAL JOURNAL 2024年第1322期128卷 739-755页

作者： Uzun, M. Bilgic, H. H. Copur, E. H. Coban, S. Iskenderun Tech Univ Dept Airframe & Powerplant Maintenance Iskenderun Turkiye Necmettin Erbakan Univ Dept Aeronaut Engn Konya Turkiye Necmettin Erbakan Univ Dept Astronaut Engn Konya Turkiye

In this paper, a new approach to modeling and controlling the problems associated with a morphing unmanned aerial vehicle (UAV) is proposed. Within the scope of the study, a dataset was created by obtaining a wide range of aerodynamic parameters for the UAV with Ansys Fluent under variable conditions using the computational fluid dynamics approach. For this, a large dataset was created that considered 5 different angles of attack, 14 different swept angles, and 5 different velocities. While creating the dataset, the analyses were verified by considering studies that have been experimentally validated in the literature. Then, an artificial intelligence-based model was created using the dataset obtained. metaheuristic algorithms such as the artificial bee colony algorithm, ant colony algorithm and genetic algorithms are used to increase the modeling success of the adaptive neuro-fuzzy inference system (ANFIS) approach. A novel modeling approach is proposed that constitutes a new decision support system for real-time flight. According to the results obtained, all the ANFIS models based on metaheuristic algorithms were more successful than the traditional approach, the multilinear regression model. The swept angle that meets the minimum lift needed by the UAV for different flight conditions was estimated with the help of the designed decision support system. Thus, the drag force is minimised while obtaining the required lift force. The performance of the UAV was compared with the nonmorphing configuration, and the results are presented in tables and graphs.

关键词： aerodynamic force ANFIS metaheuristic algorithm morphing prediction swept wing UAV

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