Search Results (236)

Accurate forecasting of carbon emissions is not only essential for addressing the challenges of climate governance but also provides timely support for dynamic carbon quota adjustments and emergency emission reduction decisions. In this study, we take China’s daily carbon emission data from 2021 to 2024 as the research objects and propose a novel forecasting framework called STL-wLSTM-SVR based on seasonal-trend decomposition with Loess (STL), long short-term memory network (LSTM) and support vector regression (SVR). First, the original carbon emission sequence is decomposed via STL into seasonal, trend and residual components. Subsequently, LSTM is employed to predict the seasonal and trend components with hyper-parameters optimized by whale optimization algorithm (WOA), and SVR is used to predict the residual component with parameters optimized through grid search method. Then, the final results are obtained by accumulating the forecasted values of the three subsequences. The experimental results illustrate that the STL-wLSTM-SVR model achieved a high-precision forecast for China’s total daily carbon emissions (RMSE of 0.1129, MAPE of 0.28%, MAE of 0.0851) and demonstrated remarkable adaptability for five major sectors—from navigating the high volatility of ground transport (MAPE of 0.36%) to effectively handling the dramatic post-pandemic structural break in aviation (MAPE of 0.72%). These findings assess the effectiveness of the hybrid forecasting framework and provide a valuable methodological reference for similar prediction tasks, such as sector-specific pollutant emissions and regional energy consumption. Full article

Fluid Catalytic Cracking (FCC) constitutes a critical process in petroleum refining, facing increasing pressure to align with sustainable development goals by improving energy efficiency and reducing environmental impact. This study tackles a multi-objective optimization challenge in FCC operations, seeking to simultaneously maximize the gasoline production and minimize the coke yield—the latter being directly linked to $\mathrm{C}{\mathrm{O}}_2$ emissions in FCC. A data-driven optimization model leveraging a dual Long Short-Term Memory architecture is developed to capture complex relationships between operating variables and product yields. To efficiently solve the model, an Improved Multi-Objective Whale Optimization Algorithm (IMOWOA) is proposed, integrating problem-specific adaptive multi-neighborhood search and dynamic restart mechanisms. Extensive experimental evaluations demonstrate that IMOWOA achieves superior convergence characteristics and comprehensive performance compared to established multi-objective algorithms. Relative to the yields before optimization, the proposed methodology increases the gasoline yield by 0.32% on average, coupled with an average reduction of 0.11% in the coke yield. For the studied FCC unit with an annual processing capacity of 2.6 million tons, the coke reduction corresponds to an annual $\mathrm{C}{\mathrm{O}}_2$ emission reduction of approximately 10,277 tons, delivering benefits to sustainable FCC operations. Full article

On GPU-based clusters, the training workloads of machine learning (ML) models, particularly neural networks (NNs), are often structured as Directed Acyclic Graphs (DAGs) and typically deployed for parallel execution across heterogeneous GPU resources. Efficient scheduling of these workloads is crucial for optimizing performance metrics such as execution time, under various constraints including GPU heterogeneity, network capacity, and data dependencies. DAG-structured ML workload scheduling could be modeled as a Nonlinear Integer Program (NIP) problem, and is shown to be NP-complete. By leveraging a positive correlation between Scheduling Plan Distance (SPD) and Finish Time Gap (FTG) identified through an empirical study, we propose to develop a Running Time Gap Strategy for scheduling based on Whale Optimization Algorithm (WOA) and Reinforcement Learning, referred to as WORL-RTGS. The proposed method integrates the global search capabilities of WOA with the adaptive decision-making of Double Deep Q-Networks (DDQN). Particularly, we derive a novel function to generate effective scheduling plans using DDQN, enhancing adaptability to complex DAG structures. Comprehensive evaluations on practical ML workload traces collected from Alibaba on simulated GPU-enabled platforms demonstrate that WORL-RTGS significantly improves WOA’s stability for DAG-structured ML workload scheduling and reduces completion time by up to 66.56% compared with five state-of-the-art scheduling algorithms. Full article

With the widespread application of multi-view data across various domains, multi-view unsupervised feature selection (MUFS) has achieved remarkable progress in both feature selection (FS) and missing-view completion. However, existing MUFS methods typically rely on centralized servers, which not only fail to meet privacy requirements in distributed settings but also suffer from suboptimal FS quality and poor convergence. To overcome these challenges, we propose a novel federated incomplete MUFS method (Fed-IMUFS), which integrates a fractional Sparsity-Guided Whale Optimization Algorithm (SGWOA) and Tensor Alternating Learning (TAL). Within this federated learning framework, each client performs local optimization in two stages: in the first stage, SGWOA introduces an $L_{2,1}$ proximal projection to enforce row-sparsity in the FS weight matrix, while fractional-order dynamics and fractal-inspired elite kernel injection mechanisms enhance global search ability, yielding a discriminative and stable weight matrix; in the second stage, based on the obtained weight matrix, an alternating optimization framework with tensor decomposition is employed to iteratively complete missing views while simultaneously optimizing low-dimensional representations to preserve cross-view consistency, with the objective function gradually minimized until convergence. During federated training, the server employs an aggregation and distribution strategy driven by normalized mutual information, where clients upload only their local weight matrices and quality indicators, and the server adaptively fuses them into a global FS matrix before distributing it back to clients. This process achieves consistent FS across clients while safeguarding data privacy. Comprehensive evaluations on CEC2022 and several incomplete multi-view datasets confirm that Fed-IMUFS outperforms state-of-the-art methods, delivering stronger global optimization capability, higher-quality feature selection, faster convergence, and more effective handling of missing views. Full article

In the context of global efforts toward energy conservation and emission reduction, accurate short-term electric load forecasting plays a crucial role in improving energy efficiency, enabling low-carbon dispatching, and supporting sustainable power system operations. To address the growing demand for accuracy and stability in this domain, this paper proposes a novel prediction model tailored for power systems. The proposed method combines Spearman correlation analysis with modal decomposition techniques to compress redundant features while preserving key information, resulting in more informative and cleaner input representations. In terms of model architecture, this study integrates Bidirectional Gated Recurrent Units (BiGRUs) with dilated convolution. This design improves the model’s capacity to capture long-range dependencies and complex relationships. For parameter optimization, an Improved Beluga Whale Optimization (IBWO) algorithm is introduced, incorporating dynamic population initialization, adaptive Lévy flight mechanisms, and refined convergence procedures to enhance search efficiency and robustness. Experiments on real-world datasets demonstrate that the proposed model achieves excellent forecasting performance (RMSE = 26.1706, MAE = 18.5462, R² = 0.9812), combining high predictive accuracy with strong generalization. These advancements contribute to more efficient energy scheduling and reduced environmental impact, making the model well-suited for intelligent and sustainable load forecasting applications in environmentally conscious power systems. Full article

Accurate extraction of single-diode photovoltaic (PV) model parameters is essential for reliable performance prediction and diagnostics, yet five-parameter identification from I-V data is ill-posed and computationally expensive. To develop and validate a hybrid analytical–metaheuristic approach that derives the diode ideality factor, saturation current, and photocurrent analytically while optimizing only series and shunt resistances, thereby reducing computational cost without sacrificing accuracy. I-V datasets were collected from a 9.54 kW grid-connected PV installation in Algiers, Algeria (15 operating points; 747–815 W m⁻²; 25.4–28.4 °C). Nine metaheuristics—Stellar Oscillation Optimizer, Enzyme Action Optimization, Grey Wolf Optimizer, Whale Optimization Algorithm, Cuckoo Search, Owl Search Algorithm, Improved War Strategy Optimization, Rüppell’s Fox Optimizer, and Artificial Bee Colony—were benchmarked against full five-parameter optimization and a Newton–Raphson baseline, using root-mean-squared error (RMSE) as the objective and wall-time as the efficiency metric. The hybrid scheme reduced the decision space from five to two parameters and lowered computational cost by ≈60–70% relative to full-parameter optimization while closely reproducing measured I-V/P-V curves. Across datasets, algorithms achieved RMSE ≈ 2.49 × 10⁻² − 2.78 × 10⁻². Rüppell’s Fox Optimizer offered the best overall trade-off (lowest average RMSE and fastest runtime), with Whale Optimization Algorithm a strong alternative (typical runtimes ≈ 107–112 s). Partitioning identification between closed-form physics and light-weight optimization yields robust, accurate, and efficient PV parameter estimation suitable for time-sensitive or embedded applications. Dynamic validation using 1498 real-world measurements across clear-sky and cloudy conditions demonstrates excellent performance: current prediction $R^2=0.9882$, power estimation $R^2=0.9730$, and voltage tracking $R^2=0.9613$. Comprehensive environmental analysis across a 39.2 °C temperature range and diverse irradiance conditions ($0\text{–}1014 \mathrm{W}/{\mathrm{m}}^2$) validates the method’s robustness for practical PV monitoring applications. Full article

With the expansion of network scale, the distance between routing nodes increases, and various routing constraints cause significant interference to the optimization process of traditional routing algorithms. To address this issue, based on the DSR routing protocol, this paper proposes a multi-constraint QoS routing algorithm based on an improved whale optimization algorithm. Specifically, the linear convergence factor in the original WOA is adjusted to a nonlinear one, which balances the global exploration capability and local exploitation capability of the algorithm. Additionally, an inertia weight strategy is introduced: this not only accelerates the convergence of the population but also enables the algorithm to escape local optimal solutions in a timely manner. Simulation results demonstrate that the routing protocol based on the improved WOA ensures QoS routing optimization capability and improves the efficiency of optimal path search. 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

Carbon-aware AI demands clear links between algorithmic choices and verified emission outcomes. This study measures and steers the carbon footprint of swarm-based optimization in HPC by coupling a job-level Emission Impact Metric with sub-minute power and grid-intensity telemetry. Across 480 runs covering 41 algorithms, we report grams CO₂ per successful optimisation and an efficiency index $\eta$ (objective gain per kg CO₂). Results show faster swarms achieve lower integral energy: Particle Swarm emits 24.9 g CO₂ per optimum versus 61.3 g for GridSearch on identical hardware; Whale and Cuckoo approach the best $\eta$ frontier, while L-SHADE exhibits front-loaded power spikes. Conservative scale factor schedules and moderate populations reduce emissions without degrading fitness; idle-node suppression further cuts leakage. Agreement between CodeCarbon, MLCO2, and vendor telemetry is within 1.8%, supporting reproducibility. The framework offers auditable, runtime controls (throttle/hold/release) that embed carbon objectives without violating solution quality budgets. Full article

To address the difficulty of extracting thermal bending failure and centerline horizontal displacement fault feature signals when judging the operating status of cement rotary kilns, we propose a method for extracting fault features based on improved secretary bird optimization algorithm (ISBOA) and variational modal decomposition (VMD). First, a strategy of randomly consuming prey with inertial weights is proposed to enhance the randomness of search results and avoid local optima. Then, the whale algorithm’s encirclement strategy is introduced into the secretary bird’s camouflage strategy to coordinate the capabilities of local search and global exploration. Finally, ISBOA demonstrated superior performance to other optimization algorithms in VMD parameter selection, achieving a 75% improvement in convergence speed compared to pre-optimization. Through validation with experimental and simulation data, this method demonstrates good feasibility. By decomposing actual signals and comparing the mean energy of their characteristic signals, the severity of thermal bending faults in the cylinder and centerline horizontal displacement faults in cement rotary kilns is diagnosed. Verified against actual measurement results, the accuracy reached 96.7%. Full article

In field exploration, disaster rescue, and complex terrain operations, the accuracy of ground type recognition directly affects the walking stability and task execution efficiency of legged robots. To address the problem of terrain recognition in complex ground environments, this paper proposes a high-precision classification method based on single-leg triaxial force signals. The method first employs a one-dimensional convolutional neural network (1D-CNN) module to extract local temporal features, then introduces a long short-term memory (LSTM) network to model long-term and short-term dependencies during ground contact, and incorporates a convolutional block attention module (CBAM) to adaptively enhance the feature responses of critical channels and time steps, thereby improving discriminative capability. In addition, an improved whale optimization algorithm (iBWOA) is adopted to automatically perform global search and optimization of key hyperparameters, including the number of convolution kernels, the number of LSTM units, and the dropout rate, to achieve the optimal training configuration. Experimental results demonstrate that the proposed method achieves excellent classification performance on five typical ground types—grass, cement, gravel, soil, and sand—under varying slope and force conditions, with an overall classification accuracy of 96.94%. Notably, it maintains high recognition accuracy even between ground types with similar contact mechanical properties, such as soil vs. grass and gravel vs. sand. This study provides a reliable perception foundation and technical support for terrain-adaptive control and motion strategy optimization of legged robots in real-world environments. Full article

This study proposes an Improved Whale Migration Algorithm (IWMA) to overcome the shortcomings of the original Whale Migration Algorithm, which suffers from premature convergence and insufficient local exploitation in high-dimensional multimodal optimization. IWMA introduces three enhancements: circle chaotic initialization to improve population diversity, a three-layer cooperative search framework to achieve a stronger balance between exploration and exploitation, and a dynamic adaptive mechanism with t-distribution re-exploration to reinforce both global escaping and local refinement. On the CEC2017 benchmark suite, IWMA demonstrates clear superiority over seven representative algorithms, delivering the best results on 27 out of 29 functions by best, 25 by mean, and 23 by standard deviation in 30 dimensions, and on 25, 18, and 18 functions, respectively, in 50 dimensions. Compared with other migration-based optimizers, its average rank improves by more than 30 percent, while runtime analysis shows only a small additional overhead of 7 to 12 percent. These outcomes, supported by convergence curves, boxplots, radar charts, and Wilcoxon tests, confirm the effectiveness of the proposed improvements. In six multi-UAV path planning scenarios, IWMA reduces the average cost by 14.5 percent compared with WMA and achieves up to 32.1 percent reduction in the most complex case. Overall, its average cost decreases by 27.4 percent across seven competitors, with a 23.6 percent improvement in the best solutions. These results demonstrate that the proposed modifications are effective, enabling IWMA to transfer its performance gains from benchmark tests to practical multi-UAV cooperative mission planning, where it consistently produces safer and smoother trajectories under complex constraints. Full article

In fresh produce cold chain last-mile delivery, the highly dispersed customer base leads to exorbitant delivery costs, posing the greatest challenge for cold chain enterprises. Achieving a symmetrical balance between cost-efficiency, environmental sustainability, and service quality is a fundamental pursuit in logistics system optimization. This paper proposes integrating the crowd-shipping logistics model—characterized by internet platform sharing and flexibility—into the delivery service. It incorporates and extends features such as cold chain delivery, mixed fleets using gasoline and diesel vehicles (GDVs), electric vehicles (EVs), partial charging strategies for EVs, and time-of-use electricity pricing into the crowd-shipping model. A joint delivery mode combining traditional professional delivery (using GDVs and EVs) with crowd-shipping is proposed, creating a symmetrical collaboration between centralized fleet management and distributed social resources. The challenges associated with utilizing occasional drivers (ODs) are analyzed, along with the corresponding compensation decisions and allocation-related constraints. A route optimization model is constructed with the objective of minimizing total cost. To solve this model, an Improved Whale Optimization Algorithm (IWOA) is proposed. To further enhance the algorithm’s performance, an adaptive variable neighborhood search is embedded within the proposed algorithm, and four local search operators are applied. Using a case study of 100 customer nodes, the joint delivery mode with OD participation reduces total delivery costs by an average of 24.94% compared to the traditional professional vehicle delivery mode, demonstrating a more symmetrical allocation of logistical resources. The experiments fully demonstrate the effectiveness of the joint delivery model and the proposed algorithm. Full article

In recent years, the integration of flexible resources into active distribution networks (ADNs) has been significantly enhanced. By coordinating a variety of such resources, the economic efficiency, operational security, and overall stability of ADNs can be improved. In this study, a bi-level planning model is proposed for active distribution networks. The upper-level model aims to minimize the annual comprehensive cost, while the lower-level model focuses on reducing network losses. To solve the upper-level problem, a hybrid whale optimization algorithm (HWOA) is developed. The algorithm integrates adaptive mutation based on Gaussian–Cauchy distributions, a nonlinear cosine-based control strategy, and a dual-population co-evolution mechanism. These enhancements allow HWOA to achieve faster convergence, higher accuracy, and stronger global search capabilities, thereby reducing the risk of falling into local optima. The lower-level problem is addressed using the interior point method due to its nonlinear and continuous nature. The proposed model and algorithm are validated through simulations on the IEEE 33-bus system. The results show that DG consumption increases by 88.77 MWh, network losses decrease by 6.8 MWh, and the total system cost is reduced by CNY 3.62 million over the entire project lifecycle. These improvements contribute to both the economic and operational performance of the ADN. Compared with the polar fox optimization algorithm (PFA), HWOA improves algorithmic efficiency by 18.92%, lowers network loss costs by 6.22%, and reduces the total system costs by 0.71%, demonstrating its superior effectiveness in solving complex bi-level optimization problems in active distribution networks. These findings not only demonstrate the technical efficiency of the proposed method but also contribute to the long-term goals of sustainable energy systems by improving renewable energy utilization, reducing operational losses, and supporting carbon reduction targets in active distribution networks. Full article

In recent years, fractional-order controllers have garnered increasing attention due to their enhanced flexibility and superior dynamic performance in control system design. Among them, the fractional-order Proportional–Integral–Derivative (FOPID) controller offers two additional tunable parameters, $\lambda$ and $\mu$, expanding the design space and allowing for finer performance tuning. However, the increased parameter dimensionality poses significant challenges for optimisation. To address this, the present study investigates the application of FOPID controllers to a two-wheeled self-balancing robot (TWSBR), with controller parameters optimised using intelligent algorithms. A novel Multi-Strategy Improved Beluga Whale Optimization (MSBWO) algorithm is proposed, integrating chaotic mapping, elite pooling, adaptive Lévy flight, and a golden sine search mechanism to enhance global convergence and local search capability. Comparative experiments are conducted using several widely known algorithms to evaluate performance. Results demonstrate that the FOPID controller optimised via the proposed MSBWO algorithm significantly outperforms both traditional PID and FOPID controllers tuned by other optimisation strategies, achieving faster convergence, reduced overshoot, and improved stability. Full article