Search Results (187)

Optimizing cable forces in cable-stayed bridges is challenging due to structural nonlinearity and the limitations of traditional methods, which often focus on isolated performance indicators. This study proposes an integrated framework combining Gaussian process regression (GPR) with an enhanced whale optimization algorithm improved by the Salp Swarm Algorithm (EWOSSA). GPR is first used to model the nonlinear relationship between cable forces and structural responses. The EWOSSA then efficiently optimizes the GPR-based model to identify optimal cable forces. A case study on a cable-stayed bridge with a 2 × 145 m main spans demonstrates the effectiveness of the proposed approach. Compared with conventional methods such as the internal-force equilibrium and zero-displacement methods, the EWOSSA-GPR framework achieves superior performance across multiple structural metrics. It ensures a more uniform cable force distribution, reduces girder displacements, and improves bending moment profiles, offering a comprehensive solution for optimal structural performance in cable-stayed bridges. Full article

Precise forecasting of lithium-ion battery health status is crucial for safe, efficient, and sustainable operation throughout the battery life cycle, especially in applications like electric vehicles (EVs) and renewable energy storage systems. In this study, an improved particle swarm optimization–support vector regression (IPSO-SVR) model is proposed for dynamic hyper-parameter tuning, integrating multiple intelligent optimization algorithms (including PSO, genetic algorithm, whale optimization, and simulated annealing) to enhance the accuracy and generalization of battery state-of-health (SOH) estimation. The model dynamically adjusts SVR hyperparameters to better capture the nonlinear aging characteristics of batteries. We validate the approach using a publicly available NASA lithium-ion battery degradation dataset (cells B0005, B0006, B0007). Key health features are extracted from voltage–capacity curves (via incremental capacity analysis), and correlation analysis confirms their strong relationship with battery capacity. Experimental results show that the proposed IPSO-SVR model outperforms a conventional PSO-SVR benchmark across all three datasets, achieving higher prediction accuracy: a mean MAE of 0.611%, a mean RMSE of 0.794%, a mean MSE of 0.007%, and robustness a mean R² of 0.933. These improvements in SOH prediction not only ensure more reliable battery management but also support sustainable energy practices by enabling longer battery life spans and more efficient resource utilization. Full article

This paper presents a novel approach to supplier clustering by utilizing the Arithmetic Optimizer Algorithm (AOA), addressing the complex challenge of supplier segmentation in modern supply chain management. The AOA framework is applied to solve the multi-criteria clustering problem inherent to supplier classification. Using a real-world dataset of 500 suppliers with 12 performance criteria, including cost, quality, delivery reliability, and sustainability metrics, our method demonstrates effective clustering performance compared to conventional techniques. The AOA achieves a silhouette coefficient of 56.5% and a Davies–Bouldin index of 56.6%, outperforming several other state-of-the-art metaheuristic algorithms, including the Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), and Harris Hawks Optimization (HHO). The algorithm’s robustness is validated through extensive sensitivity analysis and statistical tests. The results indicate that the proposed approach successfully identifies distinct supplier segments with approximately 85% accuracy, enabling more effective supplier relationship management strategies. Full article

Fuel cells (FCs) are widely used in various applications such as transportation, vehicles, and energy storage, as well as in commercial and residential buildings. The FC is connected to the grid via an inverter, which converts DC power to AC power for integration with the AC grid. Thus, it is essential to adjust the gain of the inverter’s controllers to improve FC performance and the quality of the power generated by the FCs. In this work, a fractional-order PID (FOPID) controller is used to control an inverter where the FOPID’s gain settings are determined optimally to improve the performance of the current controller of the solid-oxide fuel cell (SOFC). The optimal parameters of the FOPID are obtained using a newly developed and efficient algorithm called beluga whale optimization (BWO). To highlight the efficiency of the proposed optimization approach, the obtained results are compared with particle swarm optimization (PSO) and the conventional active power controller (APC). The findings of this paper demonstrate that the SOFC achieves significantly superior performance when the FOPID controller is optimally tuned using BWO across all performance metrics related to the FC inverter. PSO also yields good results, ensuring smooth system operation and good performance. Based on the results, the output current from the SOFC using the BWO and PSO algorithms aligns well with the reference current, whereas the APC exhibits poor performance in tracking reference current changes in two cases. Specifically, the APC introduces a delay of approximately one second (0.5 to 0.6 s), resulting in poor control performance. This delay causes the system to deviate from the reference current control (RCC) by 10%, leading to poor performance. However, the proposed optimization algorithms effectively resolve this issue, offering a robust solution for enhanced current control. Full article

Improving urban wastewater treatment efficiency and quality is urgent for most cities. The accurate wastewater flowrate forecast of a wastewater treatment plant (WWTP) is crucial for cutting energy use and reducing pollution. In this study, two hybrid models are proposed: ARIMA–Markov and ARIMA–LSTM–Transformer. Using 5 min-interval inlet flowrate data from a WWTP in 2024, the two models were verified and compared. Forecasts for 1 day, 7 days, and 2 months ahead were made, and model accuracies were compared. Ten repetitions with the same dataset assess stability, and ARIMA–LSTM–Transformer, with better performance, were selected. Then, the Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO) algorithm, and Sparrow Search Algorithm (SSA) were used for optimization, with the WOA excelling in accuracy and stability. Experimental results show that compared to the single model Transformer, WOA–ARIMA–LSTM–Transformer did better in forecasting wastewater flowrate. The combined model enables efficient and accurate wastewater flowrate forecasting, highlighting the combined model’s application potential. Full article

As market competition intensifies, the survival and development of suppliers increasingly rely on research and development (R&D) investment and innovation. Due to the uncertainty of factors affecting supplier R&D investment, the risks faced by supplier R&D investment are also uncertain. Therefore, identifying and assessing risks in advance and controlling risks can provide effective support for suppliers to carry out risk management of R&D investment. This paper selects key factors through literature review and factor analysis, and establishes a risk index evaluation system for R&D investment of medical material suppliers. Seventeen indicators that affect and constrain project investment factors were identified as input variables of the back propagation (BP) neural network, the comprehensive score of the R&D investment risk assessment was used as the output variable of medical supplies suppliers, and a risk assessment model for the R&D investment of medical material suppliers was established. By leveraging the ability of particle swarm optimization (PSO), whale optimization algorithm (WOA), and genetic algorithm (GA) to search for global optimal solutions, the BP neural network is improved to avoid becoming trapped in local optimal solutions and enhance the model’s generalization ability. The improvement in accuracy and convergence speed of these three methods is compared and analyzed. The results show that the BP neural network improved by the genetic algorithm has better accuracy and faster convergence speed in predicting and assessing risks. This indicates that the BP neural network model improved by genetic algorithm is effective and feasible for predicting the risk assessment of the R&D investment of medical supplies suppliers. Full article

To comprehensively address the challenges of aperture design, element spacing optimization, and sidelobe suppression in sparse radar array antennas, this paper proposes a hybrid particle swarm optimization (PSO) algorithm that integrates quantum-behavior mechanisms with genetic mutation. The algorithm enhances global search capability through the introduction of a quantum potential well model, while incorporating adaptive mutation operations to prevent premature convergence, thereby improving optimization accuracy during later iterations. The simulation results demonstrate that for sparse linear arrays, planar rectangular arrays, and multi-ring concentric circular arrays, the proposed algorithm achieves a sidelobe level (SLL) reduction exceeding 0.24 dB compared to conventional approaches, including the grey wolf optimizer (GWO), the whale optimization algorithm (WOA), and classical PSO. Furthermore, it exhibits superior global iterative search performance and demonstrates broader applicability across various array configurations. Full article

This research focuses on route optimization for autonomous ground vehicles, with key applications in precision agriculture, logistics and surveillance. Its goal is to create planning techniques that increase productivity and flexibility in changing settings. To achieve this, a PRISMA-based systematic literature review was carried out, encompassing works published during the last five years in databases like IEEE Xplore, ScienceDirect and Scopus. The search focused on topics related to route optimization, unmanned ground vehicles and heuristic algorithms. From the analysis of 56 selected articles, trends, technologies and challenges in real-time route planning were identified. Fifty-seven percent of the recent studies focus on UGV optimization, with prominent applications in agriculture, aiming to maximize efficiency and reduce costs. Heuristic algorithms, such as Humpback Whale Optimization, Firefly Search and Particle Swarm Optimization, are commonly employed to solve complex search problems. The findings underscore the need for more flexible planning techniques that integrate spatiotemporal and curvature constraints, allowing systems to respond effectively to unforeseen changes. By increasing their effectiveness and adaptability in practical situations, our research helps to provide more reliable autonomous navigation solutions for crucial applications. Full article

In this paper, we introduce the Levy Flight-enhanced Whale Optimization Algorithm with Tabu Search elements (LWOATS), an innovative hybrid optimization approach that enhances the standard Whale Optimization Algorithm (WOA) with advanced local search techniques and elite solution management to improve performance on global optimization problems. Techniques from the Tabu Search algorithm are adopted to balance the exploration and exploitation phases, while an elite reintroduction strategy is implemented to retain and refine the best solutions. The efficient optimization of LWOATS is further aided by the utilization of Levy flights and local search based on the Nelder–Mead simplex method. An Orthogonal Experimental Design (OED) analysis was employed to fine-tune the algorithm’s parameters. LWOATS was tested against three different algorithm sets: fundamental algorithms, advanced Differential Evolution (DE) variants, and improved WOA variants. Wilcoxon tests demonstrate the promising performance of LWOATS, showing improvements in convergence speed, accuracy, and robustness compared to traditional WOA and other metaheuristic algorithms. After extensive testing against a challenging set of benchmark functions and engineering optimization problems, we conclude that our proposed method is well suited for tackling high-dimensional optimization tasks and constrained optimization problems, providing substantial computational efficiency gains and improved overall solution quality. Full article

Photovoltaic (PV) energy generation has seen rapid growth in recent years due to its sustainability and environmental benefits. However, accurately identifying PV panel parameters is crucial for enhancing system performance, especially under varying environmental conditions. This study presents an enhanced approach for estimating PV panel parameters using a Modified Quasi-Opposition-Based Killer Whale Optimization (MQOB-KWO) technique. The research aims to improve parameter extraction accuracy by optimizing the one-diode model (ODM), a widely used representation of PV cells, using a modified metaheuristic optimization technique. The proposed algorithm leverages a Quasi-Opposition-Based Learning (QOBL) mechanism to enhance search efficiency and convergence speed. The methodology involves implementing the MQOB-KWO in MATLAB R2021a and evaluating its effectiveness through experimental I-V data from two unlike photovoltaic panels. The findings are contrasted to established optimization techniques from the literature, such as the original Killer Whale Optimization (KWO), Improved Opposition-Based Particle Swarm Optimization (IOB-PSO), Improved Cuckoo Search Algorithm (ImCSA), and Chaotic Improved Artificial Bee Colony (CIABC). The findings demonstrate that the proposed MQOB-KWO achieves superior accuracy with the lowest Root Mean Square Error (RMSE) compared to other methods, and the lowest error rates (Root Mean Square Error—RMSE, and Integral Absolute Error—IAE) compared to the original KWO, resulting in a better value of the coefficient of determination (${\mathrm{R}}^2$), hence effectively capturing PV module characteristics. Additionally, the algorithm shows fast convergence, making it suitable for real-time PV system modeling. The study confirms that the proposed optimization technique is a reliable and efficient tool for improving PV parameter estimation, contributing to better system efficiency and operational performance. Full article

Accurate segmentation of brain tumors in magnetic resonance imaging (MRI) remains a challenging task due to heterogeneous tumor structures, varying intensities across modalities, and limited annotated data. Deep learning has significantly advanced segmentation accuracy; however, it often suffers from sensitivity to hyperparameter settings and limited generalization. To overcome these challenges, bio-inspired metaheuristic algorithms have been increasingly employed to optimize various stages of the deep learning pipeline—including hyperparameter tuning, preprocessing, architectural design, and attention modulation. This review systematically examines developments from 2015 to 2025, focusing on the integration of nature-inspired optimization methods such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and novel hybrids including CJHBA and BioSwarmNet into deep learning-based brain tumor segmentation frameworks. A structured multi-query search strategy was executed using Publish or Perish across Google Scholar and Scopus databases. Following PRISMA guidelines, 3895 records were screened through automated filtering and manual eligibility checks, yielding a curated set of 106 primary studies. Through bibliometric mapping, methodological synthesis, and performance analysis, we highlight trends in algorithm usage, application domains (e.g., preprocessing, architecture search), and segmentation outcomes measured by metrics such as Dice Similarity Coefficient (DSC), Jaccard Index (JI), Hausdorff Distance (HD), and ASSD. Our findings demonstrate that bio-inspired optimization significantly enhances segmentation accuracy and robustness, particularly in multimodal settings involving FLAIR and T1CE modalities. The review concludes by identifying emerging research directions in hybrid optimization, real-time clinical applicability, and explainable AI, providing a roadmap for future exploration in this interdisciplinary domain. Full article

This study presents an advanced hybrid energy management system (EMS) designed for isolated microgrids, aiming to optimize the integration of renewable energy sources with backup systems to enhance energy efficiency and ensure a stable power supply. The proposed EMS incorporates solar photovoltaic (PV) and wind turbine (WT) generation systems, coupled with a battery energy storage system (BESS) for energy storage and management and a microturbine (MT) as a backup solution during low generation or peak demand periods. Maximum power point tracking (MPPT) is implemented for the PV and WT systems, with additional control mechanisms such as pitch angle, tip speed ratio (TSR) for wind power, and a proportional-integral (PI) controller for battery and microturbine management. To optimize EMS operations, a novel hybrid optimization algorithm, the JSO-GJO (Jellyfish Search and Golden Jackal hybrid Optimization), is applied and benchmarked against Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO), Artificial Bee Colony (ABC), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA). Comparative analysis indicates that the JSO-GJO algorithm achieves the highest energy efficiency of 99.20%, minimizes power losses to 0.116 kW, maximizes annual energy production at 421,847.82 kWh, and reduces total annual costs to USD 50,617,477.51. These findings demonstrate the superiority of the JSO-GJO algorithm, establishing it as a highly effective solution for optimizing hybrid isolated EMS in renewable energy applications. Full article

As a new form of economic activity driven by data resources and digital technologies, the digital economy underscores the strategic significance of data as a core production factor. This growing importance necessitates accurate and robust valuation methods. Data valuation poses core modeling challenges due to its nonlinear nature and the instability of neural networks, including gradient vanishing, parameter sensitivity, and slow convergence. To overcome these challenges, this study proposes a genetic algorithm-optimized BP (GA-BP) model, enhancing the efficiency and accuracy of data valuation. The BP neural network employs a symmetrical architecture, with neurons organized in layers and information transmitted symmetrically during both forward and backward propagation. Similarly, the genetic algorithm maintains a symmetric evolutionary process, featuring symmetric operations in both crossover and mutation. The empirical data used in this study are sourced from the Shanghai Data Exchange, comprising 519 data samples. Based on this dataset, the model incorporates 9 primary indicators and 21 secondary indicators to comprehensively assess data value, optimizing network weights and thresholds through the genetic algorithm. Experimental results show that the GA-BP model outperforms the traditional BP network in terms of mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²), achieving a 47.6% improvement in prediction accuracy. Furthermore, GA-BP exhibits faster convergence and greater stability. When compared to other models such as long short-term memory (LSTM), convolutional neural networks (CNNs), and optimization-based BP variants like particle swarm optimization BP (PSO-BP) and whale optimization algorithm BP (WOA-BP), GA-BP demonstrates superior generalization and robustness. This approach provides valuable insights into the commercialization of data assets. Full article

Intradialytic hypotension (IDH) is a critical complication in patients with chronic kidney disease undergoing dialysis, affecting both patient safety and treatment efficacy. This study examines the application of advanced machine learning techniques, combined with metaheuristic optimization methods, to improve predictive models for intradialytic hypotension (IDH) in hemodialysis patients. Given the critical nature of IDH, which can lead to significant complications during dialysis, the development of effective predictive tools is vital for improving patient safety and outcomes. Dialysis session data from 758 patients collected between January 2016 and October 2019 were analyzed. Particle Swarm Optimization, Grey Wolf Optimizer, Pendulum Search Algorithm, and Whale Optimization Algorithm were employed to reduce the feature space, removing approximately 45% of clinical and analytical variables while maintaining high recall for the minority class of patients experiencing hypotension. Among the evaluated models, the XGBoost classifier showed superior performance, achieving a macro F-score of 0.745 with a recall of 0.756 and a precision of 0.718. These results highlight the effectiveness of the combined approach for early identification of patients at risk for IDH, minimizing false negatives, and improving clinical decision-making in nephrology. Full article

It is noted that existing mine emergency-rescue algorithms have overlooked the requirement for multi-route sharing at critical nodes and have offered limited network visualisation. Consequently, a multi-team rescue-path-planning algorithm based on FA-MDPSO (Firefly Algorithm-Multiple Constraints Discrete Particle Swarm Optimisation) was proposed, and a graph-structure optimisation method combining a Force-Directed Layout with Breadth-First Search was introduced for node arrangement and visualisation. Methodologically, the superiority of the improved DPSO (Discrete Particle Swarm Optimisation) in route-planning precision was first validated on the DIMACS dataset. Subsequently, the hyperparameters of MDPSO (Multiple Constraints Discrete Particle Swarm Optimisation) were optimised by means of four intelligent algorithms—ACO (Ant Colony Optimization), FA (Firefly Algorithm), GWO (Grey Wolf Optimizer) and WOA (Whale Optimization Algorithm). Finally, simulations of one to three rescue-team deployments were conducted within a mine-fire scenario, and node-importance analysis was performed. Results indicated that FA-MDPSO achieved comprehensive superiority in route precision, search efficiency and convergence speed, with FA-based hyperparameter optimisation proving most effective in comparative experiments. The graph-structure optimisation was found to substantially reduce crossings and enhance hierarchical clarity. Moreover, the three-team deployment yielded the shortest equivalent path (56,357.02), and node-visitation frequency was observed to be highly concentrated on a small number of key nodes. This not only significantly improves the collaborative rescue efficiency but also provides intuitive and practical technical support for intelligent mine rescue operations. It lays an important foundation for optimising mine emergency rescue plans, ensuring the safety of underground personnel, and promoting the intelligent development of mines. Full article