Search Results (269)

Microgrids are evolving from simple hybrid systems into complex, multi-energy platforms with high-dimensional optimization challenges due to technological diversification, sector coupling, and increased data granularity. This review systematically examines the intersection of microgrid optimization and metaheuristic algorithms, focusing on the period from 2015 to 2025. We first trace the technological evolution of microgrids and identify the drivers of increased optimization complexity. We then provide a structured overview of metaheuristic algorithms—including evolutionary, swarm intelligence, physics-based, and human-inspired approaches—and discuss their suitability for high-dimensional search spaces. Through a comparative analysis of case studies, we demonstrate that metaheuristics such as genetic algorithms, particle swarm optimization, and the gray wolf optimizer can reduce the computation time to under 10% of that required by an exhaustive search while effectively handling multimodal, constrained objectives. The review further highlights the growing role of hybrid algorithms and the need to incorporate uncertainty into optimization models. We conclude that future microgrid design will increasingly rely on adaptive and hybrid metaheuristics, supported by standardized benchmark problems, to navigate the growing dimensionality and ensure resilient, cost-effective, and sustainable systems. This work provides a roadmap for researchers and practitioners in selecting and developing optimization frameworks for the next generation of microgrids. Full article

To solve the limitations of single optimization algorithms, such as premature convergence, insufficient global exploration, and high susceptibility to local optima, a Hybrid Optimization Algorithm (HOA) based on multi-subpopulation collaboration and multi-strategy fusion is proposed. The HOA uses Logistic chaotic mapping for population initialization to enhance uniformity and diversity. The population is then divided into four subpopulations; each is optimized independently using different strategies, including the genetic algorithm (GA), Gray Wolf Optimizer (GWO), self-attention mechanism, and k-nearest neighbor graph (kNN). This design leverages the strengths of individual algorithms while mitigating their respective limitations. An elite information exchange mechanism facilitates knowledge transfer by randomly reassigning elite individuals across subpopulations at fixed iteration intervals. Additionally, global optimization strategies including differential evolution (DE), Simulated Annealing (SA), Local Search (LS), and time of arrival (TOA) position adjustment are integrated to balance exploration and exploitation, thereby enhancing convergence accuracy and the ability to escape local optima. Evaluated on the CEC2017 benchmark suite and real-world engineering problems, the HOA demonstrates superior performance in convergence speed, accuracy, and robustness compared to single-algorithm approaches—notably, HOA ranks 1st in 30-dimensional CEC2017 functions. By effectively integrating multiple optimization strategies, the HOA provides an effective and reliable solution to complex optimization challenges. Full article

This study introduces an innovative Honey Badger Optimization (HBO) designed to address the Optimal Power Flow (OPF) challenge in electrical power systems. HBO is a unique population-based searching method inspired by the resourceful foraging behavior of honey badgers when hunting for food. In this algorithm, the dynamic search process of honey badgers, characterized by digging and honey-seeking tactics, is divided into two distinct stages, exploration and exploitation. The OPF problem is formulated with objectives including fuel cost minimization and voltage deviation reduction, alongside operational constraints such as generator limits, transformer settings, and line power flows. HBO is applied to the IEEE 30-bus test system, outperforming existing methods such as Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO) in both fuel cost reduction and voltage profile enhancement. Results indicate significant improvements in system performance, achieving 38.5% and 22.78% better voltage deviations compared to GWO and PSO, respectively. This demonstrates HBO’s efficacy as a robust optimization tool for modern power systems. In addition to the single-objective studies, a multi-objective OPF formulation was investigated to produce the complete Pareto front between fuel cost and voltage deviation objectives. The proposed HBO successfully generated a well-distributed set of trade-off solutions, revealing a clear conflict between economic efficiency and voltage quality. The Pareto analysis demonstrated HBO’s strong capability to balance these competing objectives, identify knee-point operating conditions, and provide flexible decision-making options for system operators. Full article

Whether an unmanned aerial vehicle (UAV) can complete its mission successfully is determined by trajectory planning. Reasonable and efficient UAV trajectory planning in 3D environments is a complex global optimization problem, in which numerous constraints need to be considered carefully, including mountainous terrain, obstacles, no-fly zones, safety altitude, smoothness, flight distance, and so on. Generally speaking, symmetry characteristics from the starting point to the endpoint can be concluded from the potential spatial multiple trajectories. Aiming at the deficiencies of the Sparrow Search Algorithm (SSA) in 3D symmetric trajectory planning such as population diversity and local optimization, the sine–cosine function and the Lévy flight strategy are combined, and the Improved Sparrow Search Algorithm (ISSA) is proposed, which can find a better solution in a shorter time by dynamically adjusting the search step size and increasing the occasional large step jumps so as to increase the symmetry balance of the global search and the local development. In order to verify the effectiveness of the improved algorithm, ISSA is simulated and compared with the Sparrow Search Algorithm (SSA), Particle Swarm Algorithm (PSO), Gray Wolf Algorithm (GWO) and Whale Optimization Algorithm (WOA) in the same environment. The results show that the ISSA algorithm outperforms the comparison algorithms in key indexes such as convergence speed, path cost, obstacle avoidance safety, and path smoothness, and can meet the requirement of obtaining a higher-quality flight path in a shorter number of iterations. Full article

Ozone density at cold-point mesopause (O3-CPM) can provide information on long-term atmospheric trends. Compared to ground-level ozone, O3-CPM is not only adversely affected by chemical substances emitted from human activities but is also regulated by solar radiation. Therefore, an accurate prediction of O3-CPM is necessary. However, it is difficult for traditional forecasting methods to predict the main trends and seasonal characteristics of ozone time series while capturing the random components and noise of O3-CPM. In order to improve the prediction accuracy of O3-CPM, this paper proposes a hybrid SSA-SARIMA-GSVR model based on the Singular Spectrum Analysis (SSA) method, which combines the Seasonal Autoregressive Integrated Moving Average Model (SARIMA) and the Gray Wolf Algorithm Optimized Support Vector Regression Algorithm (GSVR). First, the O3-CPM sequence is decomposed using SSA, and the concept of reconstruction threshold (RT) is introduced to categorize the decomposed singular values into two classes. The categorized RT reconstructed sequences containing periodic features and major trends are fed into the SARIMA model for prediction, and the N-RT reconstructed sequences (original sequence N minus RT reconstructed sequence) containing stochastic components and nonlinear features are fed into the GSVR model for prediction. The final prediction results are obtained by superimposing the outputs of these two models. The results confirm that, compared to various commonly used time series forecasting models such as Long Short-Term Memory (LSTM), Informer, SVR, SARIMA, GSVR, SSA-GSVR, and SSA-SARIMA models, the proposed SSA-SARIMA-GSVR hybrid prediction model has the lowest error evaluation metrics, enabling accurate and efficient prediction of the O3-CPM time series. Specifically, the proposed model achieved an RMSE of 0.26, MAE of 0.212, and R² of 0.987 on the test set, outperforming the best baseline model (SARIMA) by 45.8%, 42.1%, and 3.1%, respectively. Full article

To investigate the characteristic pressure distribution patterns when gripping ski poles during skiing, this study addresses the challenges of measuring grip force on the complex curved surfaces of ski poles. A dataset of experimental samples was established, and grip force data were extracted using deep neural network (DNN) training. To reduce errors caused by dynamic force distribution and domain shifts due to varying hand postures, a hybrid method combining deep neural networks with the bio-inspired Gray Wolf Optimization (GWO) algorithm was proposed. This approach enables the fusion of hand-related feature data, facilitating the development of a high-precision grip force prediction model for skiing. A multi-point flexible array sensor was selected to detect force at key contact points. Through system calibration, grip force data were collected and used to construct a comprehensive database. A backpropagation (BP) neural network was then developed to process the sensor data at these characteristic points using deep learning techniques. The data fusion model was trained and further optimized through the GWO-BPNN (Gray Wolf Optimizer–backpropagation neural network) algorithm, which focuses on correcting and classifying force data based on dominant force-bearing units. Experimental results show that the optimized model achieves a relative error of less than 2% compared to calibration experiments, significantly improving the accuracy of flexible sensor applications. This model has been successfully applied to the development of intelligent skiing gloves, offering a scientific foundation for performance guidance and evaluation in skiing sports. Full article

To address the limitations of the original Sand Cat Swarm Optimization (SCSO) algorithm—such as static strategy selection, insufficient population diversity, and coarse boundary handling—this paper proposes a multi-strategy enhanced version, namely the Modified Sand Cat Swarm Optimization (MSCSO). The algorithm improves performance through three core strategies: (1) an adaptive strategy selection mechanism that dynamically adapts to different optimization phases; (2) an adaptive crossover–mutation strategy inspired by differential evolution, in which mutation vectors are generated with the guidance of the global best solution and updated via binomial crossover, thereby enhancing both population diversity and local search capability; and (3) a boundary control mechanism guided by the global best solution, which repairs out-of-bound solutions by relocating them between the global best and the boundary, thus preserving useful search information and avoiding oscillation near the limits. To validate the performance of MSCSO, extensive experiments were conducted on the CEC2020 and CEC2022 benchmark suites under 10- and 20-dimensional scenarios, where MSCSO was compared with seven algorithms, including Particle Swarm Optimization (PSO) and Gray Wolf Optimizer (GWO). The results demonstrate that MSCSO consistently outperforms its competitors on unimodal, multimodal, and hybrid functions. Notably, MSCSO achieved the best Friedman ranking across all dimensions. Ablation studies further confirm that the three proposed strategies exhibit strong synergy, collectively accelerating convergence and enhancing stability. In addition, MSCSO was applied to multilevel threshold image segmentation, where Otsu’s criterion was adopted as the objective function and experiments were conducted on five benchmark images with 4–10 thresholds. The results show that MSCSO achieves superior segmentation quality, significantly outperforming the comparison algorithms. Overall, this study demonstrates that MSCSO effectively balances exploration and exploitation without increasing computational complexity, providing not only a powerful tool for global optimization but also a reliable technique for engineering tasks such as multilevel threshold image segmentation. These findings highlight its strong theoretical significance and promising application potential. Full article

With the vigorous development of contemporary clean energy, the participation rate of photovoltaic (PV) power generation in the whole power system is increasing day by day, and accurate PV power prediction technology is crucial for the optimal scheduling of the power system. However, the frequent occurrence of extreme climate in recent years has caused greater disturbance to PV power generation, which greatly increases the degree of difficulty in accurately predicting PV power generation and thus affects the security, economy, reliability and stability of grid system operation. In order to predict PV power under extreme climatic conditions, we firstly elaborate the PV power prediction methods and their respective advantages and disadvantages for sand, dust, rainstorm and snowfall in existing studies, and on this basis, we propose the Gray Wolf Optimization for Short-Term Forecasting Models of the Long and Short-Term Memory Model based on K-Means clustering, which ensures the accuracy of PV power prediction under extreme climatic conditions. power prediction accuracy under extreme climate conditions. Firstly, the K-means clustering algorithm is utilized to perform weather typing, which is divided into four weather categories, namely, dusty weather, heavy rain, heavy snow and normal weather. Then, for the weather typing results, the prediction effects of the Gray Wolf Optimization Long Short-Term Memory Network (GWO-LSTM) Model, Random Forest (RF) Model, Multilayer Feedforward Neural Network (BP) Model, and Long and Short-Term Memory Network (LSTM) Model are compared, respectively. The prediction results indicate that GWO-LSTM achieves the highest forecasting accuracy, with a mean root mean square error (RMSE) of 0.6235 across all four weather scenarios. Its prediction accuracy reaches approximately 95%, providing effective data support for the safe and stable operation of new power systems featuring high proportions of grid-connected photovoltaic generation. Full article

For the ship pipe routing design (SPRD) problem, previous studies have mainly employed bio-inspired algorithms such as multi-objective ant colony optimization (MOACO), non-dominated sorting genetic algorithm II (NSGA-II), and multi-objective particle swarm optimization (MOPSO). This paper proposes a novel approach based on the multi-objective A* (MOA*) algorithm to solve the SPRD. First, the optimization objectives and constraints of the SPRD problem are defined, and then an MOA*-based routing framework is developed. The time and space complexities of the approach are analyzed, and key components such as the cost functions, the solution dominance relationship, dynamic probability-based pruning, and neighbor node exploration strategy are designed to enhance solution diversity and search efficiency. Additionally, a space cascade expansion method is proposed to improve the computational efficiency of the MOA* in large-scale grid spaces. Comparative studies with MOACO, NSGA-II, GA-A*, and gray wolf optimization (GWO) on simulated cases of varying complexities and practical piping scenarios demonstrate the effectiveness of the MOA*. Furthermore, the applicability of the MOA* is validated against practical piping requirements, including the rapid generation of sub-optimal solutions, non-orthogonal routing, and partitioned pipe layouts. Experimental results, supported by a C++/OpenGL-based prototype software, show that the MOA* requires no extensive parameter tuning, exhibits stable computational efficiency and optimization capability, and demonstrates competitive performance in Pareto-optimal diversity compared with other algorithms. Full article

In the context of large-scale renewable integration and increasing demand for power-system flexibility, energy-storage systems are indispensable components of modern grids, and their safe, reliable operation is a decisive factor in investment decisions. To mitigate lifecycle degradation and cost increases caused by frequent charge–discharge cycles, this study puts forward a two-layer energy storage capacity configuration optimization approach with explicit integration of cycle life restrictions. The upper-level model uses time-of-use pricing to economically dispatch storage, balancing power shortfalls while maximizing daily operational revenue. Based on the upper-level dispatch schedule, the lower-level model computes storage degradation and optimizes storage capacity as the decision variable to minimize degradation costs. Joint optimization of the two levels thus enhances overall storage operating efficiency. To overcome limitations of the conventional Whale Optimization Algorithm (WOA)—notably slow convergence, limited accuracy, and susceptibility to local optima—an Improved WOA (IWOA) is developed. IWOA integrates circular chaotic mapping for population initialization, a golden-sine search mechanism to improve the exploration–exploitation trade-off, and a Cauchy-mutation strategy to increase population diversity. Comparative tests against WOA, Gray Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO) show IWOA’s superior convergence speed and solution quality. A case study using measured load data from an industrial park in Zhuzhou City validates that the proposed approach significantly improves economic returns and alleviates capacity degradation. Full article

This study evaluates the estimation of Weibull distribution parameters (shape, k; scale, c) for wind speed modeling in wind energy potential assessments. Traditional empirical methods—Justus Moment Method (JEM), Power Density Method (PDM), Energy Pattern Factor Method (EPFM), Lysen Moment Method (LAM), and Standard Deviation Empirical Method (SEM)—are compared with advanced artificial intelligence optimization algorithms (AIOAs), including Genetic Algorithm (GA), Gravitational Search Algorithm (GSA), Sine Cosine Algorithm (SCA), Teaching-Learning-Based Optimization (TLBA), Grey Wolf Optimizer (GWA), Red Fox Algorithm (RFA), and Red Panda Optimization Algorithm (RPA). Using hourly wind speed data from Foça, Urla, Karaburun, and Çeşme in Turkey, the analysis demonstrates that AIOAs, particularly GA, GSA, SCA, TLBA, and GWA, outperform empirical methods, achieving low RMSE (0.0071) and high R² (0.9755). SEM and LAM perform competitively among empirical methods, while PDM and EPFM show higher errors, highlighting their limitations in complex wind speed distributions. The study also conducts a techno-economic analysis, assessing capacity factors, unit energy costs, and payback periods. Foça and Urla are identified as optimal investment sites due to high energy yields and economic efficiency, whereas Çeşme is unviable due to low production and long payback periods. This research provides a robust framework for Weibull parameter estimation, demonstrating AIOAs’ superior accuracy and offering a decision-support tool for sustainable wind energy investments. Full article

With rapid urbanization and industrialization in China, existing gas supply networks urgently require renewal and optimization. This paper proposes a Gray Wolf Optimizer (GWO)-based method for reducing calculation errors and a Zebra Optimization Algorithm (ZOA)-based approach for gas supply pressure distribution. For error correction, the pipe friction coefficient is adjusted to minimize the deviation between calculated and actual flows. The GWO reduces average relative error to 0.01% with satisfactory iteration speed and efficiency. For pressure distribution, supply-end pressures are optimized to reduce energy consumption and enhance system performance. The ZOA shows strong convergence and global search capabilities. These methods provide valuable theoretical and practical insights for optimizing gas supply networks, supporting green transformation and sustainable development. Full article

Precise forecasting of carbon content in the converter’s reblowing phase is pivotal to boosting steel production efficiency and ensuring effective control over molten steel quality. However, existing mechanistic models based on material balance and decarbonization kinetics suffer from insufficient accuracy due to simplifying assumptions. In contrast, data-driven models rely on data quality, lack generalization capability, and lack physical interpretability. Additionally, integral models based on flue gas analysis suffer from data latency issues. To overcome these limitations, this study proposed a real-time carbon content prediction model for the converter’s reblowing phase, leveraging a physics-informed long short-term memory (PI-LSTM) network. First, flue gas data was processed using a carbon integration model to generate a carbon content change curve during the reblowing stage as a reference for actual values; second, a dual-branch network structure was designed, where the LSTM branch simultaneously predicts carbon content and key unmeasurable parameters in the decarbonization kinetics, while the mechanism branch combined these parameters with the decarbonization formula to calculate carbon content under mechanism constraints; finally, a joint loss function (combining data-driven loss and mechanism constraint loss) was used to train the model, and the gray wolf optimization (GWO) algorithm was employed to optimize hyperparameters. Experimental results show that compared to the mechanism model (MM) and LSTM model, the PI-LSTM model achieves an average absolute error (MAE) of 0.0077, a root mean square error (RMSE) of 0.0112, and endpoint carbon content hit rates within ±0.005%, ±0.01%, ±0.015% error ranges, achieving 53.71%, 82.23%, and 95.45%, respectively, significantly improving prediction accuracy and physical plausibility. This model lays a robust groundwork for dynamic closed-loop real-time control of carbon levels in the converter’s reblowing stage. Full article

In response to the inability to quickly assess wind noise performance during the early stages of automotive styling design, this paper proposes a method for predicting interior wind noise by integrating automotive point cloud models with the Gray Wolf Optimization Residual Network model (GWO-ResNet). Based on wind tunnel test data under typical operating conditions, the point cloud model of the test vehicle is compressed using an auto-encoder and used as input features to construct a nonlinear mapping model between the whole vehicle point cloud and the wind noise level at the driver’s left ear. Through adaptive optimization of key hyperparameters of the ResNet model using the gray wolf optimization algorithm, the accuracy and generalization of the prediction model are improved. The prediction results on the test set indicate that the proposed GWO-ResNet model achieves prediction results that are consistent with the actual measured values for the test samples, thereby validating the effectiveness of the proposed method. A comparative analysis with traditional ResNet models, GWO-LSTM models, and LSTM models revealed that the GWO-ResNet model achieved Mean Absolute Percentage Error (MAPE) and mean squared error (MSE) of 9.72% and 20.96, and 9.88% and 19.69, respectively, on the sedan and SUV test sets, significantly outperforming the other comparison models. The prediction results on the independent validation set also demonstrate good generalization ability and stability (MAPE of 10.14% and 10.15%, MSE of 23.97 and 29.15), further proving the reliability of this model in practical applications. The research results provide an efficient and feasible technical approach for the rapid evaluation of wind noise performance in vehicles and provide a reference for wind noise control in the early design stage of vehicles. At the same time, due to the limitations of the current test data, it is impossible to predict the wind noise during the actual driving of the vehicle. Subsequently, the wind noise during actual driving can be predicted by the test data of multiple working conditions. Full article

The integration of multiple renewable and storage units in electric vehicle (EV) hybrid energy systems presents significant challenges in stability, dynamic response, and disturbance rejection, limitations often encountered with conventional sliding mode control (SMC) and super-twisting SMC (STSMC) schemes. This paper proposes a condition-based integral terminal super-twisting sliding mode control (CBITSTSMC) strategy, with gains optimally tuned using an improved gray wolf optimization (I-GWO) algorithm, for coordinated control of a multi-source DC–DC converter system comprising photovoltaic (PV) arrays, fuel cells (FCs), lithium-ion batteries, and supercapacitors. The CBITSTSMC ensures finite-time convergence, reduces chattering, and dynamically adapts to operating conditions, thereby achieving superior performance. Compared to SMC and STSMC, the proposed controller delivers substantial reductions in steady-state error, overshoot, and undershoot, while improving rise time and settling time by up to 50%. Transient stability and disturbance rejection are significantly enhanced across all subsystems. Controller-in-the-loop (CIL) validation on a Delfino C2000 platform confirms the real-time feasibility and robustness of the approach. These results establish the CBITSTSMC as a highly effective solution for next-generation EV hybrid energy management systems, enabling precise power-sharing, improved stability, and enhanced renewable energy utilization. Full article