Search Results (1,053)

Influence maximization (IM) in social networks aims to identify the optimal set of seed nodes that maximizes influence spread under a given diffusion model. The standard Grey Wolf Optimizer (GWO) suffers from two fundamental limitations when applied to this problem: an inflexible exploration–exploitation transition controlled by a linearly decreasing parameter; and a rigid three-level leadership hierarchy that suppresses individual diversity and promotes premature convergence. In this paper, we propose a Multi-Role Cooperative Grey Wolf Optimizer (Multiple-roles GWO) that addresses both limitations through two complementary mechanisms. First, a Q-learning-based adaptive phase transition mechanism monitors population diversity, fitness improvement rate, and iteration progress in real time, enabling the algorithm to dynamically shift between exploration and exploitation. Second, inspired by the principle of division of labor, the exploitation phase is restructured into a four-role cooperative framework comprising leaders, explorers, followers, and losers, each executing a distinct search strategy to improve local search coverage and maintain population diversity. Experiments on six real-world social networks under the Independent Cascade model show that Multiple-roles GWO achieves competitive or superior influence spread compared with state-of-the-art heuristic baselines, with comparable computational efficiency. Full article

Accurate stock price forecasting remains a challenging problem because financial markets are highly dynamic, nonlinear, and influenced by changing economic conditions. Deep learning models, particularly Long Short-Term Memory (LSTM) networks, have shown strong capability in capturing temporal dependencies in financial time series. However, their performance is often sensitive to hyperparameter selection, which can affect convergence, stability, and generalization. This research presents a comparative forecasting framework using three bio-inspired hyperparameter optimization algorithms: Sand Cat Swarm Optimization (SCSO), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO). Each of the optimization algorithms works on a common hyperparameter search space comprising the learning rate, dropout rate, batch size and number of units in the LSTM layers, while the input sequence length is fixed at 60 trading days. The models are compared with a fixed-hyperparameter baseline LSTM to validate whether hyperparameter optimization improves price forecasting. The models are evaluated using daily data from five DAX-index equities over two sample periods: 2018–2023 and 1996–2024, respectively, with 2023 and 2019–2024 being their respective held-out test sets. The LSTM predicts the next-trading-day log return, which is transformed into a forecast of the next adjusted closing price. Historical prices and technical indicators representing trend, momentum, and volatility are incorporated into the modeling process. Chronological data splitting, training-only scaler fitting, validation-based hyperparameter selection, and a held-out test set used exclusively for final evaluation are employed to limit look-ahead bias, with every model evaluated across three random seeds. Model performance is assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), coefficient of determination (R²) and directional accuracy, along with support from Diebold–Mariano tests and Ljung–Box and ARCH residual diagnostics. LSTM–SCSO achieves the strongest aggregate error performance during 2018–2023, reducing mean RMSE by approximately 2.5% relative to the baseline. During 1996–2024, PSO–LSTM ranks first on RMSE, MAE, and MAPE, reducing mean RMSE by approximately 3.9%, while GWO–LSTM produces closely comparable results. Optimized models record the lowest equity-level mean RMSE for four of five equities in the short-term experiment and three of five equities in the long-term experiment. However, Diebold–Mariano significance varies across model–equity comparisons, and aggregate directional accuracy remains below 50% for all models in both periods. Long-term residual diagnostics also identify substantial autocorrelation and ARCH effects. The findings demonstrate that bio-inspired metaheuristic optimization can improve one-step-ahead LSTM forecasts, but its benefits depend on the optimizer, equity, and historical evaluation period. Full article

Improving energy efficiency and renewable-energy hosting capacity in distribution networks is essential for sustainable power-system development. Conventional networks face power losses, voltage deviations, thermal stress, and limited flexibility to accommodate increasing renewable penetration. Accordingly, optimal placement and sizing of photovoltaic-based distributed generation (PV-DG) has become an effective approach for enhancing network performance. This study applies the Grey Wolf Optimiser (GWO) to PV-DG allocation considering three objectives: minimisation of active power loss and total voltage deviation, and maximisation of the voltage stability index. To mitigate the premature convergence of standard GWO, Opposition-Based Learning (OBL) is integrated to enhance exploration through opposite candidate solutions. The proposed OBL-GWO framework is evaluated on the IEEE 33-bus system under three PV-DG penetration scenarios using five-year irradiance and load data from Riyadh, including seasonal uncertainty and long-term load growth. Compared with standard GWO, it achieves average additional gains of 11.24, 10.0, 6.79, and 2.74 percentage points in power-loss reduction, TVD reduction, VSI improvement, and minimum-bus-voltage improvement, respectively. Sensitivity analysis confirms limited dependence on moderate changes in objective weights. A persistent cloudy-week case demonstrates robust performance against GWO, PSO, and GA under adverse irradiance. Scalability tests on IEEE 69- and 118-bus systems confirm that OBL-GWO retains its solution-quality and convergence advantages on larger networks. Full article

Although population-based metaheuristic algorithms have been widely applied to the One-Dimensional Cutting Stock Problem (1D-CSP), their performance is often limited by premature convergence and insufficient local search capability. This study presents a comparative investigation of the effect of local search on four population-based metaheuristic paradigms for the one-dimensional cutting stock problem (1D-CSP). Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Grey Wolf Optimizer (GWO) are evaluated both in their standard forms and after incorporating a common Simulated Annealing (SA)-based local refinement procedure. The objective is not to introduce a new hybridization strategy but to systematically examine whether and to what extent the same local search mechanism affects algorithms with different search characteristics. The methods are evaluated on two complementary benchmark datasets comprising 39 industrial instances from the Japanese chemical fiber industry and 1800 CUTGEN1 instances. Performance was evaluated using clipping loss, computational cost, Friedman ranks, win counts, and paired Wilcoxon signed-rank tests. According to the results, statistically significant differences were observed for GA, PSO, and GWO across both benchmark sets, while the difference between ACO and M-ACO was not statistically significant. The study provides a comparative assessment of how the common local search component affects different metaheuristic paradigms for full 1D-CSP. Full article

Aiming at the multi-objective optimization problem of dynamic economic emission dispatch (DEED) considering plug-in electric vehicles (PEVs), this paper proposes an improved multi-objective grey wolf optimizer (IMGWO) to simultaneously minimize power generation costs and pollutant emissions. Traditional economic dispatch requires a trade-off between economic benefits and environmental sustainability. However, these two objectives are inherently conflicting, rendering single-objective optimization strategies ineffective for practical DEED scenarios. In this study, PEVs are integrated into the DEED framework to implement systematic charging and discharging scheduling, which realizes peak shaving and valley filling and further improves the operational quality of the power grid. The incorporation of PEVs also renders the DEED problem more nonlinear, non-convex, and non-smooth. To address these complex characteristics, the proposed IMGWO adopts modified logistic chaotic mapping for population initialization, combined with a nonlinear convergence factor and dynamic weight strategy. These improvements effectively enhance the global search ability and convergence accuracy while preventing premature convergence. Furthermore, the Pareto optimal solution set is utilized to resolve conflicts among multiple optimization objectives. Comparative experiments on standard benchmark test cases verify that the proposed IMGWO outperforms four existing advanced algorithms in comprehensive evaluation indicators, demonstrating its excellent effectiveness and robustness. Finally, the proposed algorithm is applied to three classic PEV-integrated DEED cases. The results confirm that the IMGWO can achieve superior scheduling performance and verify the positive effect of PEV participation in improving grid dispatching quality and unit operation efficiency. Full article

Air void content (Va) is a key volumetric parameter governing the performance of Stone Mastic Asphalt (SMA), yet its prediction becomes challenging when steel slag, fibers, and additives are incorporated. This study develops an interpretable machine learning framework for predicting Va using 74 mixtures collected from 16 published studies and nine mixture-related variables. XGBoost was optimized using Particle Swarm Optimization and Grey Wolf Optimizer (GWO), with the best configuration obtained by GWO at a population size of 40 and a minimum development-stage 5-fold cross-validation (CV) RMSE of 0.371%. On the principal 70/30 evaluation partition, the optimized model achieved R² = 0.944, RMSE = 0.397%, MAE = 0.262%, and MAPE = 0.054, outperforming the evaluated benchmark models in R², RMSE, MAE, and MAPE. Robustness analyses showed that prediction accuracy was sensitive to data partitioning and literature-source composition, indicating that the reported performance should be interpreted within the represented data domain. SHAP, permutation importance, and feature-ablation analyses consistently identified binder penetration as the most influential predictor, followed mainly by asphalt content and softening point. Overall, the proposed framework provides an interpretable tool for preliminary Va estimation and mixture screening, while independent laboratory and field validation remains necessary before practical deployment. Full article

This paper presents an approach for optimal operation of self-healing networked microgrids (NMGs) under both normal operation and emergency conditions using the pufferfish optimization algorithm (POA). The proposed methodology is based on an energy management system (EMS) with two levels and independent functions. The lower-level is designed for normal operation, where the local controller of each microgrid (MG) performs the optimal dispatch of power from the dispatchable sources. During an emergency case in any MG, the higher-level EMS is activated, and the global controller is brought into operation. Physically, the NMGs are connected by tie-lines, while cyber links are established to exchange information and control signals for coordinated operation. Each MG operates to supply its local demand during normal operation conditions, resulting in no electrical power exchange between MGs. İn case of generation deficiency or a fault leading to generation outage, electrical power can be exchanged through the existing interconnections, enabling the affected microgrid to receive support from neighboring MGs. The main objective of POA is to minimize the total operating cost, in which the economic impact of network power losses is incorporated into the single objective function. Simulation studies were conducted using MATLAB and DIgSILENT software over one day. The performance of POA was compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO) under the same computational settings. Statistical and convergence analyses show that POA achieves the lowest mean operating cost across all studied cases, with low run-to-run variability and favorable convergence behavior. The results demonstrate the effectiveness of the proposed approach in improving the economic operation of NMGs under both normal and emergency conditions. Full article

Accurate State-of-Health (SOH) prediction for lithium-ion batteries is essential for the safe and stable operation of Battery Management Systems (BMSs). However, achieving high-precision estimation remains challenging due to the complex interplay of non-stationary degradation characteristics, observation noise, and bidirectional temporal dependencies. This study proposes a robust hybrid temporal prediction framework integrating Variational Mode Decomposition (VMD), Bidirectional Long Short-Term Memory (BiLSTM) networks, an Attention mechanism, and the Grey Wolf Optimizer (GWO). First, VMD is employed to decompose the raw SOH time-series signals, isolating observation noise and mitigating temporal non-stationarity. A BiLSTM network is then utilized to extract bidirectional temporal correlations throughout the aging process, while an attention mechanism adaptively allocates weights to focus on critical degradation features. Furthermore, GWO automatically searches for optimal network hyperparameters, eliminating the subjective bias of manual tuning and enhancing cross-condition generalization. The proposed framework is rigorously validated using full-lifecycle datasets of four lithium-ion batteries (CS2_35 to CS2_38) from the CALCE laboratory. Standard temporal networks (LSTM, GRU, and Transformer) serve as comparative baselines, alongside an intra-battery ablation study to quantify the performance gain of each module. Experimental results demonstrate that the proposed VMD–GWO–BiLSTM–Attention framework achieves enhanced prediction accuracy compared to standard baseline temporal models (LSTM, GRU, and Transformer) across all evaluated degradation profiles. For the CS2_35 battery with a smooth aging trend, the root mean square error (RMSE) and mean absolute percentage error (MAPE) are minimized to 0.008 and 1.442%, respectively. Even under the severe capacity fluctuations of the CS2_38 battery, the model successfully controls the RMSE at 0.012 and the MAPE at 2.541%, exhibiting a significant error reduction compared to all baselines. These findings confirm that the synergistic integration of these four modules effectively addresses complex, nonlinear battery aging scenarios, offering a viable methodological baseline for deep-learning-based SOH prediction. Full article

Accurate parameter estimation of photovoltaic (PV) models is essential for performance evaluation, efficiency enhancement, and reliable energy forecasting in solar energy systems. However, the nonlinear, multimodal, and implicit nature of the current–voltage (I–V) relationship makes this task challenging for conventional optimization methods, which often suffer from premature convergence and sensitivity to initial conditions. In this study, a modified variant of the Artificial Circulatory System Algorithm, termed Dynamic Elite Cooperative ACSA (DEC-ACSA), is proposed for estimating the unknown parameters of the Single-Diode Model (SDM). The proposed approach extends the original ACSA by incorporating dynamic population grouping, elite-guided cooperative interaction, and directional elite refinement, thereby aiming to improve convergence stability and the utilization of high-quality population information. The objective is to minimize the residual root mean square error (RMSE) of the implicit SDM equation using measured I–V data from four established benchmarks: the RTC France solar cell and the PWP201, STM6-40/36, and STP6-120/36 PV modules. The performance of DEC-ACSA is evaluated against the original ACSA, Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Henry Gas Solubility Optimization (HGSO) over 30 independent runs under an equal budget of 50,100 function evaluations. The DEC-ACSA configuration selected on RTC France was retained unchanged for the three additional module benchmarks. DEC-ACSA achieved mean residual RMSE values of 1.2514 × 10⁻³, 2.656 × 10⁻³, 2.647 × 10⁻³, and 1.8108 × 10⁻² for RTC France, PWP201, STM6-40/36, and STP6-120/36, respectively, while consistently reducing run-to-run variability relative to ACSA. Holm-corrected tests showed no significant difference from PSO on RTC France and PWP201, whereas significant differences from all comparison algorithms were observed on STM6-40/36 and STP6-120/36. I–V reconstruction further confirmed close agreement with the measured data across all four PV systems. Full article

Modern manufacturing enterprises are increasingly transitioning to multi-variety, small-batch production. This shift introduces significant scheduling challenges, particularly due to the job hierarchy constraints inherent in assembling multi-level intermediate parts. Furthermore, non-machining preparation times—such as tool switching, material handling, and equipment standby—significantly impact production efficiency. To address these challenges, this paper investigates the flexible job shop batch scheduling problem by integrating order and operation consolidation under strict job hierarchy constraints. To mathematically formulate the scheduling problem with non-serial operation precedence networks and dynamic batching, we develop a mixed-integer programming model. The primary objective is to simultaneously minimize the maximum completion time (makespan) and total tardiness. To solve this efficiently, an Improved Grey Wolf Optimization (IGWO) algorithm is proposed. The algorithm features a novel two-tier coding scheme tailored for consolidation logic and employs a hybrid population initialization strategy to enhance initial solution quality. Moreover, it improves the standard hunting mechanism, utilizes Variable Neighborhood Search (VNS) for local exploitation, and independently applies a Simulated Annealing (SA) dynamic acceptance mechanism to balance global exploration and local exploitation. Extensive experiments using small-, medium-, and large-scale industrial data from a power station valve manufacturer validate the effectiveness of the proposed model and algorithm in optimizing complex batch scheduling schemes. Full article

Reliable integration of wind energy into modern power grids requires control strategies capable of maintaining stable operation under stochastic wind conditions and grid-side disturbances. This paper presents an adaptive metaheuristic optimization and numerical modeling framework for robust multi-scenario tuning of proportional–integral controller parameters in a doubly fed induction generator (DFIG)-based wind-energy conversion system. The optimized control loops include the rotor-side converter, grid-side converter, rotor-speed loop, and DC-link voltage loop. Unlike conventional tuning approaches that rely on nominal operating points or limited deterministic cases, the proposed formulation evaluates each candidate controller over multiple operating scenarios, including start-up dynamics, step wind-speed variation, random wind fluctuation, and grid-voltage dip conditions. An Adaptive Whale Optimization Algorithm (AWOA) is developed by incorporating diversity-aware adaptation and stagnation-handling mechanisms into the standard WOA structure to improve the exploration–exploitation balance during the search process. The tuning objective combines aggregate transient-performance minimization with robustness-oriented scenario evaluation, thereby promoting controller gains that remain effective across uncertain operating conditions. Comparative numerical simulations against Grey Wolf Optimizer, Generalized Grey Wolf Optimizer, Moth-Flame Optimizer, and standard WOA show that the proposed AWOA achieves lower aggregate Integral Time Squared Error values across the considered cases. Convergence assessment, ablation analysis, and hold-out robustness testing further confirm the contribution of the adaptive mechanisms. Time-domain responses also demonstrate improved DC-link voltage regulation and reactive-power recovery under severe grid disturbances. These results indicate that the proposed framework can enhance the reliability and resilience of grid-connected DFIG wind-energy systems, supporting more robust and sustainable renewable-energy integration. Full article

The rapid transition toward electric vehicles (EVs) in Saudi Arabia requires reliable and sustainable charging infrastructure capable of supporting long-distance highway transportation. However, the deployment of emergency charging systems is challenged by sparse charging infrastructure, stochastic emergency charging demand, battery degradation under harsh climatic conditions, and the need for cost-effective integration of renewable energy resources. This study presents a three-stage unified techno-economic planning framework for renewable-assisted emergency EV charging networks that integrates strategically located charging hubs with a coordinated fleet of solar-assisted Mobile Emergency Charging Vehicles (MECVs). The proposed framework jointly optimizes charging hub locations, photovoltaic (PV) generation capacity, battery energy storage system (BESS) sizing, and MECV allocation while explicitly accounting for stochastic emergency charging demand, renewable-energy utilization, and temperature-dependent battery degradation. Emergency charging demand is modeled using Monte Carlo simulation based on EV penetration scenarios, and battery aging is incorporated into the optimization through a temperature-dependent degradation model. The planning problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) model and comparatively solved using three independent metaheuristic algorithms, namely the Musical Chairs Algorithm (MCA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO). The proposed framework is evaluated using two representative highway corridors in Saudi Arabia. The results indicate that renewable-assisted charging can reduce annual grid-related CO₂ emissions by approximately 25,360 and 57,641 t CO₂/year for the Riyadh–Dammam and Riyadh–Makkah corridors, respectively. Battery degradation contributes approximately 12.7–14.2% of the total annualized system cost, highlighting the importance of incorporating lifecycle degradation into infrastructure planning. Temperature sensitivity analysis further indicates the significant influence of harsh climatic conditions on battery lifetime, renewable-energy utilization, and overall system economics. The proposed framework provides a practical planning methodology for developing reliable, sustainable, and economically viable emergency EV charging infrastructure in regions with similar geographical and climatic characteristics. Full article

Robust controller tuning is essential for nonlinear systems operating under plant uncertainty and external disturbances. This study proposes a scenario-based framework for tuning PID and FOPID controllers for a nonlinear cart-inverted-pendulum system, using separate training and test scenarios to assess generalization. Five training scenarios incorporating parametric variations, disturbances, and noise are used during optimization, while four unseen scenarios are reserved for evaluation. The Slime Mold Algorithm (SMA), Artificial Hummingbird Algorithm (AHA), and Grey Wolf Optimizer (GWO) are compared under identical computational budgets and initial populations. The objective combines weighted integral of time-weighted absolute error (ITAE) measures with a standard-deviation penalty to promote consistent performance across scenarios. Robust tuning reduces the mean unseen test cost of PID controllers by approximately 20% compared with nominal tuning. Its effect is more pronounced for FOPID controllers: nominal tuning causes instability in several unseen cases, whereas robust tuning eliminates failures in the test set. Increasing the robustness penalty reduces the generalization gap by about 17% for PID and 46% for FOPID. Across fifteen unseen plant configurations, the robust-tuned FOPID-AHA controller remains stable, while the nominal counterpart fails under the two most severe combined-stress conditions. These results show that scenario-based tuning improves controller reliability beyond a single nominal operating point. Full article

To overcome the nonlinear and non-stationary characteristics of rolling bearing vibration signals and the challenge of extracting incipient weak fault features, this paper proposes a joint fault diagnosis method based on Variational Mode Decomposition (VMD), Maximum Correlated Kurtosis Deconvolution (MCKD) and Support Vector Machine (SVM). Different from most existing studies that separately optimize individual stages of the fault diagnosis workflow, the proposed method adopts a multi-strategy enhanced grey wolf algorithm (S-LE-EGWO) to collaboratively tune parameters for multiple key modules within a unified framework. Firstly, taking the minimum envelope entropy as the fitness function, the S-LE-EGWO algorithm is utilized to optimize the mode number $K$ and penalty factor $\alpha$ of VMD to realize adaptive decomposition of vibration signals. Secondly, kurtosis combined with the correlation coefficient is adopted to select effective. Intrinsic Mode Function (IMF), and the signal is reconstructed based on the screened components. Then, the S-LE-EGWO algorithm is employed to optimize the parameters of MCKD to realize effective extraction of periodic fault impulses. Finally, multi-dimensional fault features are extracted, dimension-reduced by Kernel Principal Component Analysis (KPCA), and fed into the optimized SVM classifier to complete fault identification. Feature-oriented mechanism analysis is carried out using simulation signals, and the proposed method is validated on the CWRU rolling-bearing dataset, with comparative investigations against four mainstream optimization-based diagnostic algorithms. The test results show that the proposed method can effectively mine weak fault features of bearings. Compared with other algorithms, the presented method achieves superior identification performance and possesses favorable recognition capability for incipient weak faults, which can realize the classification of bearing faults. Full article