Metaheuristic Algorithms Research Articles (Page 1)

Abstract The application of Machine Learning (ML) models coupled with metaheuristic optimization algorithms represents a potentially powerful development in the field of predictive modeling, as it relates to sustainable energy materials. In this study, the electrochemical performance of biomass material from bamboo for energy storage applications is explored, focusing on the prediction of power density. The central objective is to enhance model accuracy with a novel hybrid model of Kernel Extreme Learning Machine (KELM) and Slime Mould Algorithm (SMA), Sunflower Optimization (SFO), and Social Ski Driver (SSD). The optimal predictive performance was achieved with KELM-SFO with a test Root Mean Square Error (RMSE) of 10,421.05, Mean Absolute Error (MAE) of 4,654.07, and R-squared (R 2 ) of 96.2 %. Early and fast plateauing of the SFO algorithm’s convergence curve indicated stable, early-stage optimization. In addition to filling a significant knowledge gap in ML-incorporated materials modeling, this work opens the door for future research on deep learning techniques, adaptive hybrid optimization algorithms, and real-time experimental validations to improve the electrochemical prediction efficiency in energy storage systems inspired by biomass.

Feature selection aims to identify a relevant subset of features from the original feature set to enhance the performance of machine learning models, which is crucial for improvig model accuracy. However, this task is highly challenging due to the enormous search space, often requiring the use of meta-heuristic algorithms to efficiently identify near-optimal feature subsets. This paper proposes an improved algorithm based on Northern Goshawk Optimization (NGO), called Elite-guided Hybrid Northern Goshawk Optimization (EH-NGO), for feature selection tasks. The algorithm incorporates an elite-guided strategy within the NGO framework, leveraging information from elite individuals to direct the population’s evolutionary trajectory. To further enhance population diversity and prevent premature convergence, a vertical crossover mutation strategy is adopted, which randomly selects two different dimensions of an individual for arithmetic crossover to generate new solutions, thereby improving the algorithm’s global exploration capability. Additionally, a boundary control strategy based on the global best solution is introduced to reduce ineffective searches and accelerate convergence. Experiments conducted on 30 benchmark functions from the CEC2017 and CEC2022 test set demonstrate the superiority of EH-NGO in global optimization, outperforming eight compared state-of-the-art algorithms. Furthermore, a novel feature selection method based on EH-NGO is proposed and validated on 22 datasets of varying scales. Experimental results show that the proposed method can effectively select feature subsets that contribute to improved classification performance.

Metaheuristic algorithms are powerful tools for solving complex optimization problems where traditional methods fail. The Social Spider Optimization (SSO) algorithm, inspired by the cooperative foraging behavior of spiders, is a notable swarm intelligence technique. However, it can be prone to premature convergence. This paper presents an enhanced variant, the Elite Opposition-Based Social Spider Optimization (EOSSO) algorithm, which integrates an elite opposition-based learning (OBL) strategy and an elite selection mechanism into the standard SSO framework. This integration aims to improve population diversity, enhance global exploration, and accelerate convergence. The performance of EOSSO is rigorously evaluated on a comprehensive set of 23 benchmark functions, including unimodal, multimodal, and fixed-dimension multimodal problems. Experimental results demonstrate that EOSSO significantly outperforms the standard SSO and other well-known metaheuristics in terms of solution accuracy, convergence speed, and stability. The algorithm exhibits a remarkable ability to escape local optima and refine solutions efficiently, proving its robustness and effectiveness as a high-performance optimizer for complex landscapes.

Predicting fatigue behavior in steel components is highly challenging due to the nonlinear and uncertain nature of material degradation under cyclic loading. In this study, four hybrid machine learning models were developed—Histogram Gradient Boosting optimized with Prairie Dog Optimization (HGPD), Histogram Gradient Boosting optimized with Wild Geese Algorithm (HGGW), Categorical Gradient Boosting optimized with Prairie Dog Optimization (CAPD), and Categorical Gradient Boosting optimized with Wild Geese Algorithm (CAGW)—by coupling two advanced ensemble learning frameworks, Histogram Gradient Boosting (HGB) and Categorical Gradient Boosting (CAT), with two emerging metaheuristic optimization algorithms, Prairie Dog Optimization (PDO) and Wild Geese Algorithm (WGA). This integrated approach aims to enhance the accuracy, generalization, and robustness of predictive modeling for steel fatigue life assessment. Shapley Additive Explanations (SHAP) were employed to quantify feature importance and enhance interpretability. Results revealed that reduction ratio (RedRatio) and total heat treatment time (THT) exhibited the highest variability, with RedRatio emerging as the dominant factor due to its wide range and significant influence on model outcomes. The SHAP-driven analysis provided clear insights into complex interactions among processing parameters and fatigue behavior, enabling effective feature selection without loss of accuracy. Overall, integrating gradient boosting with novel optimization algorithms substantially improved predictive accuracy and robustness, advancing decision-making in materials science.

Car classification, using different machine learning models with optimization frameworks, is done for evaluation in this work. We used different models, namely, Extra Trees, XGBoost, Gaussian Naive Bayes, K-Nearest Neighbors, Histogram-based Gradient Boosting (Hist Gradient Boosting), and Linear Discriminant Analysis, for classification. The car samples are classified as “very good,” “good,” “acceptable,” and “unacceptable.” Among these, Hist Gradient Boosting has the highest value for precision, accuracy, recall, and F1 score. We further tuned this model using Evolutionary Strategies, Evolutionary Programming, Covariance Matrix Adaptation Evolution Strategy, and the Flower Pollination Algorithm. Our outcomes indicate that the Covariance Matrix Adaptation Evolution Strategy and Flower Pollination Algorithm significantly enhanced the performance of the model and outperformed Evolutionary Programming. This work investigates the potential of integrating advanced machine learning models with sophisticated optimization strategies to deliver an effective car evaluation classification process that will be useful in this industry and, perhaps, in many other classification tasks.

Automation in crack and bug-hole detection through non-destructive evaluation methods remains a major challenge in Structural Health Monitoring. Typically, machine learning models are fed with original, grayscale, or binary formats of concrete images. Original images retain excessive color data, grayscale formats reduce color without preserving structural relevance, and binary images often lack sufficient detail for assessing damage type and severity. To overcome these limitations, multilevel colored image thresholding using meta-heuristic algorithms such as Particle Swarm Optimization, Genetic Algorithm, Jaya and Sine Cosine Algorithm—guided by Otsu’s objective function. This technique enhances the contrast, edges, and texture boundaries, which serve as crucial primitives for object recognition, compared to original images. Additionally, it increases pixel connectivity, thereby simplifying image analysis for deep learning and machine learning applications. In fact, this method reduces the number of colors by an average of 97.3%, significantly decreasing computational load, while maintaining a high Structural Similarity Index (SSIM) of 0.873. Experimental results demonstrate that the optimized images outperform the original, grayscale, and binary formats in both object detection performance and precise evaluation of damage severity and progression. This fusion of optimization and perceptual image representation presents a promising advancement for automated structural damage assessment.

Blockchain technology relies on cryptographic mechanisms for transaction security and data integrity. However, the growing computational complexity, high transaction costs, and scalability issues pose significant challenges to blockchain adoption. Traditional cryptographic methods—such as hashing, key generation, encryption, and decryption—introduce excessive computational overhead, leading to energy inefficiencies and increased latency. This research proposes an optimization-driven crypto analysis framework that integrates metaheuristic algorithms, combinatorial optimization, reinforcement learning, and game theory to enhance the efficiency and security of blockchain cryptographic processes. The framework focuses on optimized cryptographic computation, gas fee reduction in smart contracts, security enhancement against cryptanalysis, and improved scalability of consensus mechanisms. Experimental evaluations demonstrate up to 39.4\% reduction in cryptographic execution time, 29.4\% savings in smart contract gas fees, and 33.3\% improvement in decentralization of Proof-of-Stake validators. These results validate the effectiveness of the proposed framework in achieving secure, scalable, and cost-efficient blockchain operations.

Optimization of compressive strength and carbon footprint in fly ash geopolymer concrete using metaheuristic algorithms

Hybrid Renewable Energy Systems (HRESs) are a practical solution for providing reliable, low-carbon electricity to off-grid and remote communities. This review examines the role of energy storage within HRESs by systematically comparing electrochemical, mechanical, thermal, and hydrogen-based technologies in terms of technical performance, lifecycle cost, operational constraints, and environmental impact. We synthesize findings from implemented off-grid projects across multiple countries to evaluate real-world performance metrics, including renewable fraction, expected energy not supplied (EENS), lifecycle cost, and operation & maintenance burdens. Special attention is given to the emerging role of hydrogen as a long-term and cross-sector energy carrier, addressing its technical, regulatory, and financial barriers to widespread deployment. In addition, the paper reviews real-world implementations of off-grid HRES in various countries, summarizing practical outcomes and lessons for system design and policy. The discussion also includes recent advances in metaheuristic optimization algorithms, which have improved planning efficiency, system reliability, and cost-effectiveness. By combining technological, operational, and policy perspectives, this review identifies current challenges and future directions for developing sustainable, resilient, and economically viable HRES that can accelerate equitable electrification in remote areas. Finally, the review outlines key limitations and future directions, calling for more systematic quantitative studies, long-term field validation of emerging technologies, and the development of intelligent, Artificial Intelligence (AI)-driven energy management systems within broader socio-techno-economic frameworks. Overall, this work offers concise insights to guide researchers and policymakers in advancing the practical deployment of sustainable and resilient HRES.

Abstract Three-dimensional path planning for UAVs and real-world engineering design problems has been a hot research issue. Traditional methods show limitations in dealing with these complex nonlinear models. To overcome these challenges, this paper proposes a new metaheuristic method, Euler's optimizer (ELO), inspired by Euler's method for the numerical solution of differential equations.ELO simulates Euler's method to predict the position of the next point by the derivative and step size of the current point to perform the position updating, and sets the exploration weighting factor and the exploration factors that are used to tune the exploration and exploitation capabilities of the algorithm. We compared ELO with 11 representative state-of-the-art algorithms using the CEC2017 and CEC2022 benchmark suites, and performed the Wilcoxon rank sum test and Friedman's test. The results show that ELO outperforms the other comparative algorithms by 90\%, 96\%, 99\%, and 84\% on CEC2017 (30/50/100 dimensions) and CEC2022 (20 dimensions), respectively. Finally, ELO is applied to UAV 3D path planning and five real-world engineering design problems. The experimental results show that ELO achieves the best compared to all 11 comparison algorithms, demonstrating ELO's effectiveness and extensiveness in engineering optimization problems.

Abstract The backtracking search algorithm (BSA) is a popular metaheuristic algorithm known for its simplicity and effectiveness in solving complex optimization problems. However, like many other algorithms, BSA has some weaknesses, such as getting stuck in local optima and converging too quickly to suboptimal solutions. To address these issues, this study introduces a new variant of BSA called the combined power mutation-based backtracking search algorithm (CPMBSA). The performance of the proposed CPMBSA approach is enhanced through the integration of a modified power mutation operator, which strengthens the exploration capability, and a combined mutation operator. This can improve convergence while preserving population diversity. These modifications help CPMBSA achieve a better balance of exploration and exploitation during the search. The CPMBSA has been tested on 86 benchmark functions to verify its performance and robustness. In addition, the algorithm has been applied to six real-world chemical engineering optimization problems and two engineering design optimization problems to evaluate its practical use in complex design challenges. The analysis of results shows that CPMBSA performs better than the original BSA and other advanced metaheuristic algorithms in terms of solution accuracy, convergence speed, and stability. The present research demonstrates that the proposed CPMBSA is a powerful optimization tool for both theoretical benchmark test problems and real-world engineering applications.

Introduction: Smart cities demand efficient and sustainable solutions for urban waste management due to rising populations and environmental concerns. Traditional waste collection methods are inefficient, costly, and environmentally harmful. This research addresses these challenges by proposing a Smart Garbage Collection System (SGCS) that uses IoT-enabled Smart Garbage Bins (SGBs) and a heuristic-driven route optimization algorithm to dynamically schedule and guide Garbage Collection Vehicles (GCVs) for efficient municipal solid waste (MSW) collection. Methods: A modified A* algorithm, called Op-A*, was developed to generate dynamic optimal routes for GCVs. This algorithm integrates multiple heuristics such as distance, traffic conditions, road quality, bin fill levels, and vehicle capacity. The system architecture includes real-time data acquisition from ultrasonic sensors in SGBs, transmission to cloud servers, and route recalculation when environmental parameters change. Experimental scenarios were simulated on both a small graph (manual test) and a synthetic dataset of 500 nodes (city-scale model). Results: The Op-A* algorithm significantly outperformed traditional A* and other metaheuristic algorithms (e.g., Ant Colony Optimization, Genetic Algorithm, PSO) in terms of route optimality, measured by a novel Optimal Index (ratio of distance to waste collected). Results showed that incorporating real-time traffic and road quality heuristics reduced total route cost and improved waste collection efficiency. Optimal route decisions dynamically adapted to changes in node activity and vehicle constraints. Discussion: Experimental findings validate that the Op-A* algorithm's multi-heuristic strategy ensures timely waste collection, minimizes vehicle travel time and fuel consumption, and scales effectively to large, real-world datasets. However, limitations were identified such as high computational complexity for large datasets and sensitivity to heuristic weights (traffic and road quality). The reliance on robust IoT infrastructure and real-time data availability also presents challenges for practical deployment. Conclusion: The proposed Op-A* algorithm offers a robust and scalable solution for dynamic waste collection in smart cities by integrating multiple real-world heuristics into a single optimization framework. It outperforms existing algorithms by producing more efficient and responsive routing solutions. Future work will incorporate machine learning and reinforcement learning to further optimize dynamic pathfinding and predictive bin-level analysis, enhancing adaptability and efficiency.

The high rate of social media development has triggered a high rate of fake accounts, which are a great risk to the privacy of users and the integrity of the platform. These malicious accounts are hard to detect because user activity data is highly imbalanced, dimensional, and sequential. The emergence of fake profiles on social media endangers the privacy and trust of social media users. It is difficult to detect such accounts because of high-dimensional, highly sequential, and imbalanced user behavior data. Current techniques tend to miss out on the complicated activity patterns or even overfit, which is why a strong, scalable, and precise model of social media fraud detection is required. This study suggests a new deep learning architecture that entails a Temporal Convolutional Network (TCN) with Generative Adversarial Network (GAN)-based data augmentation to generate minority classes, and Autoencoder-based feature extraction to reduce dimensionality. The Seagull Optimization Algorithm (SOA), which is a metaheuristic algorithm, is used to optimize hyperparameters by balancing efficiency and speed of convergence in global search. The framework is tested on benchmark datasets (Cresci-2017 and TwiBot-22) and compared to the state-of-the-art models. It has been shown in experiments that the suggested TCN-GAN-SOA framework performs better, with ROC-AUC scores of 0.96 on Cresci-2017 and 0.95 on TwiBot-22, and a higher precision-recall value and better F1-scores. In addition, computational efficiency can be verified by the runtime analysis; case studies prove the framework’s strength when handling various situations of fraudulent behaviors. The given solution offers a scalable, reliable, and accurate methodology of detecting social media fraud based on the combination of sophisticated sequence modeling, realistic data augmentation, and hyperparameter optimization.

This study proposes an optimization-based methodology for predicting cutting forces in milling by eliminating the need for traditional offline calibration procedures. A mechanistic force model is employed, in which cutting force coefficients are identified using population-based metaheuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). Cutting force data collected during machining are utilized to optimize the model parameters directly. The performance of each algorithm is systematically evaluated through 30 independent trials to ensure statistical reliability. The DE algorithm demonstrated the best performance, converging in all 30 runs with an average of 197 iterations and 5.4 s, followed by PSO (363 iterations, 9.8 s), while GA exhibited lower reliability (18 successful runs, 2108 iterations, 62.9 s). The optimized coefficients were validated against experimental data, yielding mean prediction errors of 2.82 N (F x ) and 4.35 N (F y ). The proposed method offers a fast, accurate, and scalable solution for cutting force prediction, supporting adaptive process control, and contributing to the development of intelligent manufacturing systems.

Feature selection in high-dimensional datasets presents significant computational challenges, particularly in domains with large feature spaces and limited sample sizes. This paper introduces FL-SBA, a novel metaheuristic algorithm integrating fractional calculus enhancements with Laguerre operators into the Secretary Bird Optimization Algorithm framework for binary feature selection. The methodology incorporates fractional opposition-based learning utilizing Laguerre operators for enhanced population initialization with non-local memory characteristics, and a Laguerre-based binary transformation function replacing conventional sigmoid mechanisms through orthogonal polynomial approximation. Fractional calculus integration introduces memory effects that enable historical search information retention, while Laguerre polynomials provide superior approximation properties and computational stability. Comprehensive experimental validation across ten high-dimensional gene expression datasets compared FL-SBA against standard SBA and five contemporary methods including BinCOA, BAOA, BJSO, BGWO, and BMVO. Results demonstrate FL-SBA’s superior performance, achieving 96.06% average classification accuracy compared to 94.41% for standard SBA and 82.91% for BinCOA. The algorithm simultaneously maintained exceptional dimensionality reduction efficiency, selecting 29 features compared to 40 for competing methods, representing 27% improvement while achieving higher accuracy. Statistical analysis reveals consistently lower fitness values (0.04924 averages) and stable performance with minimal standard deviation. The integration addresses fundamental limitations in integer-based computations while enhancing convergence behavior. These findings suggest FL-SBA represents significant advancement in metaheuristic-based feature selection, offering theoretical innovation and practical performance improvements for high-dimensional optimization challenges.

Regenerative population strategy-I: A dynamic methodology to mitigate structural bias in metaheuristic algorithms

Smart City Electronics Security Using XGBoost With Metaheuristic Algorithms

NGBoost-based prediction of carbonation in Alkali-activated slag concrete enhanced by metaheuristic algorithms

Lighting efficiency optimization method based on artificial neural networks and metaheuristic algorithms

Shunting locomotives exhibit a wide and unpredictable range of power profiles. This unpredictability makes it impossible to rely on offline optimizations or predictive methods combined with online optimization. To maintain optimal performance across this broad range of operating conditions, the online control strategy must be robust. This article proposes a robust method to determine the optimal parameter combinations for an online energy management strategy of a hybrid fuel cell battery shunting locomotive, ensuring optimality across all scenario conditions. The first step involves extracting a statistically representative subspace for simulation, both in terms of parameter combinations and scenario conditions. A response surface model (numerical twin) is then constructed to extrapolate results across the entire space based on this simulated subspace. Using this model, the optimal solution is identified through metaheuristic algorithms (minimization search). Finally, the proposed solution is validated against a set of expert-defined scenarios. The result of the methodology ensures robust optimization across an infinite number of scenarios by minimizing the impact on both the fuel cell and the battery, without increasing mission costs.