Search Results (135)

Short-term wind speed forecasting is challenged by the nonlinear, non-stationary, and highly volatile characteristics of wind speed series, which hinder the performance of traditional prediction models. To improve forecasting capability, this study proposes a hybrid modeling framework that integrates multi-strategy adaptive particle swarm optimization (MASPSO), a convolutional neural network (CNN), a gated recurrent unit (GRU), and an attention mechanism. Within this modeling architecture, the CNN extracts multi-scale spatial patterns, the GRU captures dynamic temporal dependencies, and the attention mechanism highlights salient feature components. MASPSO is further incorporated to perform global hyperparameter optimization, thereby improving both prediction accuracy and generalization. Evaluation on real wind farm data confirms that the proposed modeling framework delivers consistently superior forecasting accuracy across different wind speed conditions, with significantly reduced prediction errors and improved robustness in multi-step forecasting tasks. Full article

This paper proposes a grating Moiré fringe signal compensation method based on Variational Mode Decomposition (VMD) to address signal errors in grating encoders. VMD decomposes Moiré fringe signals into multiple amplitude-modulated and frequency-modulated components, and realizes noise compensation through parameter optimization and signal reconstruction. The Multi-Strategy Particle Swarm Optimization (MSPSO) enhances optimization performance via adaptive inertia weight adjustment and chaotic perturbation, solving the problems of mode mixing or over-decomposition caused by blind parameter selection in traditional VMD. A hardware-software co-design test system based on ZYNQ FPGA is developed, which optimally allocates tasks between the Processing System and Programmable Logic, resolving issues of large data volume and long computation time in traditional systems. The compensation scheme provides excellent signal processing performance. The experimental tests on random periodic signals, triangular waves and square waves with different duty cycles have demonstrated the robustness of this scheme. After compensation, the output signal exhibits excellent sinuosity and orthogonality, with harmonic components and noise in the frequency domain almost negligible. It provides a practical solution for high-precision measurement in ultra-precision machining, semiconductor manufacturing, and automated control. Full article

Ultra-deep fault-controlled carbonate reservoirs exhibit strong heterogeneity, multi-scale fracture–cavity systems, and complex geological controls, which render conventional analytical well testing methods inadequate. This study proposes a geological model-based numerical well testing framework incorporating adaptive meshing, noise reduction, and machine-learning-assisted inversion. A multi-step workflow was established, including (i) single-well geological model extraction with localized grid refinement to capture near-wellbore flow behavior, (ii) pressure data denoising and preprocessing using low-pass filtering, and (iii) surrogate-assisted parameter inversion and sensitivity analysis using particle swarm optimization (PSO) to construct diagnostic type curves for different fracture–cavity control modes. The methodology was applied to different wells, yielding inverted fracture permeabilities ranging from approximately 140 to 480 mD and cavity permeabilities between about 110 and 220 mD. Results show that the numerical well testing method achieved an 85.7% interpretation accuracy, outperforming conventional approaches. Distinct parameter sensitivities were identified for single-, double-, and multi-cavity systems, providing a systematic basis for production allocation strategies. This integrated approach enhances the reliability of reservoir characterization and offers practical guidance for efficient development of ultra-deep carbonate reservoirs. Full article

To address the limitations of the standard Sparrow Search Algorithm (SSA) in complex optimization problems—such as insufficient convergence accuracy and susceptibility to local optima—this paper proposes a Composite Strategy Sparrow Search Algorithm (CSSA) for multidimensional optimization. The algorithm first employs chaotic mapping during initialization to enhance population diversity; second, it integrates coordinate axis pattern search to strengthen local exploitation capabilities; third, it applies intelligent crossover operations to promote effective information exchange among individuals; and finally, it introduces an adaptive vigilance mechanism to dynamically balance exploration and exploitation throughout the optimization process. Compared with seven state-of-the-art algorithms, CSSA demonstrates superior performance in both 30-dimensional low-dimensional and 100-dimensional high-dimensional test scenarios. It achieves optimal solutions in three real-world engineering applications: thermal management of electric vehicle battery packs, photovoltaic power system configuration, and data center cooling systems. Wilcoxon rank-sum tests further confirm the statistical significance of these improvements. Experimental results show that CSSA significantly outperforms mainstream optimization methods in terms of convergence accuracy and speed, demonstrating substantial theoretical value and practical engineering significance. Full article

As the adoption of electric vehicles increases, hybrid energy storage systems (HESS) combining batteries and supercapacitors mitigate the conflict between high energy capacity and power demand, particularly during acceleration and transient loads. However, frequent current fluctuations accelerate battery degradation, reducing long-term performance. This study presents a multi-objective Whale–Particle Swarm Optimization Algorithm (MOWPSO) for tuning the control parameters of a HESS composed of a lithium-ion battery and a supercapacitor. The proposed full-active configuration with dual bidirectional DC converters enables precise current sharing and independent regulation of energy and power flow. The optimization framework minimizes four objectives: mean battery current amplitude, cumulative aging index, final state-of-charge deviation, and an auxiliary penalty term promoting consistent battery–supercapacitor cooperation. The algorithm operates offline to identify Pareto-optimal controller settings under the Federal Test Procedure 75 cycle, while the selected compromise solution governs real-time current distribution. Robustness is assessed through multi-seed hypervolume analysis, and results demonstrate over 20% reduction in battery aging and approximately 25% increase in effective cycle life compared to battery-only, rule-based and metaheuristic algorithm strategies control. Cross-cycle validation under highway and worldwide driving profiles confirms the controller’s adaptability and stable current-sharing performance without re-tuning. Full article

Efficient truck–shovel allocation is essential for optimizing open-pit mining operations, but the integration of heterogeneous diesel and electric fleets introduces complex scheduling challenges, including charging requirements, range limitations, and equipment capacity constraints. This study proposes an integrated allocation framework tailored to heterogeneous fleets, formulating a multi-objective optimization model that minimizes transportation cost and waiting time under realistic constraints. An enhanced multi-objective particle swarm optimization algorithm with adaptive penalty mechanisms is developed, providing superior convergence and computational efficiency compared to traditional methods. A case study demonstrates that heterogeneous fleets achieve a better trade-off, with a balanced fleet configuration reducing transportation cost by 26.1% and waiting time by 19.2% compared to pure diesel and electric fleets, respectively. Sensitivity analyses reveal that fluctuations in fuel and electricity prices reshape the trade-off, while faster charging enhances electric truck competitiveness but increases diesel idle time. These findings offer practical insights for configuring heterogeneous fleets and adapting scheduling strategies in dynamic energy and technology environments, supporting sustainable mining operations. Full article

Aiming to solve the bottlenecks of the traditional Grey Wolf Optimizer (GWO) in UAV three-dimensional path planning—including uneven initial population distribution, slow convergence speed, and proneness to local optima—this paper proposes an improved algorithm (CPS-GWO) that integrates the Kent chaotic map with Particle Swarm Optimization (PSO) to mitigate these limitations. To enhance the diversity of the initial population, the Kent chaotic map is employed, as ergodicity ensures the symmetric distribution of the initial population, expanding search coverage; meanwhile, a nonlinear adaptive strategy is adopted to dynamically adjust the control parameter a, enabling flexible search behaviour. Furthermore, the grey wolf position update rule is optimized by incorporating the inertia weight and social learning mechanism of PSO, which strengthens the algorithm’s ability to balance exploration and exploitation. Additionally, a multi-objective comprehensive cost function is constructed, encompassing path length, collision penalty, height constraints, and path smoothness, to fully align with the practical demands of UAV path planning. To validate the performance of CPS-GWO, a three-dimensional urban simulation environment is established on the MATLAB platform. Comparative experiments with different population sizes are conducted, with the traditional GWO as the benchmark. The results demonstrate that, compared with the original GWO, (1) the average fitness of CPS-GWO is significantly reduced by 31.30–38.53%; (2) the path length is shortened by 15.62–22.12%; (3) path smoothness is improved by 43.44–51.52%; and (4) the fitness variance is only 9.58–12.16% of that of the traditional GWO, indicating notably enhanced robustness. Consequently, the proposed CPS-GWO effectively balances global exploration and local exploitation capabilities, thereby providing a novel technical solution for efficient path planning in UAV logistics and distribution under complex urban environments, which holds important engineering application value. Full article

Optimizing chiller load (OCL) distribution in multi-chiller HVAC systems is critical for energy efficiency, yet existing algorithms often struggle with accuracy and convergence. This challenge is compounded by the fact that existing research predominantly focuses on chiller-centric optimization, often neglecting the significant energy consumption of auxiliary components. To address this gap, this study proposes a novel method utilizing Modelica/Simulink co-simulation to accurately model the entire refrigeration system, including chillers, pumps and cooling towers, thereby eliminating complex mathematical derivations and enhancing real-world applicability. To solve this holistic optimization problem, an Improved Particle Swarm Optimization (IPSO) algorithm is developed, which integrates a Phased Adaptive Decreasing Inertia Weight (PADIW) strategy, adaptive learning factors, and a mutation operator to enhance its global search capability and robustness. A case study of a shopping mall demonstrates the approach’s efficacy: over a six-month period, the optimization method reduces total refrigeration system consumption by 25.5% compared to the strategy of distributing the load equally and 15.5% compared to the human experience strategy. Notably, this case revealed that the water pumps, while accounting for less than 20% of total consumption, held a disproportionately large energy-saving potential of over 25%. Comparative experiments and Monte Carlo simulations further confirm the proposed IPSO’s superior convergence and robustness over standard PSO and other common metaheuristics. This study demonstrates that the synergy of Modelica/Simulink co-simulation and the IPSO algorithm is crucial for realizing the full energy-saving potential of the entire system, particularly from previously overlooked components like the water pumps. Full article

Unmanned underwater vehicles (UUVs) face significant challenges in achieving safe and efficient autonomous navigation in complex marine environments due to uncertain perception, dynamic obstacles, and nonlinear coupled motion control. This study proposes a hierarchical autonomous navigation framework that integrates improved particle swarm optimization (PSO) for 3D global route planning, and a deep deterministic policy gradient (DDPG) algorithm enhanced by noisy networks and proportional prioritized experience replay (PPER) for local collision avoidance. To address dynamic sideslip and current-induced deviations during execution, a novel 3D adaptive line-of-sight (ALOS) guidance method is developed, which decouples nonlinear motion in horizontal and vertical planes and ensures robust tracking. The global planner incorporates a multi-objective cost function that considers yaw and pitch adjustments, while the improved PSO employs nonlinearly synchronized adaptive weights to enhance convergence and avoid local minima. For local avoidance, the proposed DDPG framework incorporates a memory-enhanced state–action representation, GRU-based temporal processing, and stratified sample replay to enhance learning stability and exploration. Simulation results indicate that the proposed method reduces route length by 5.96% and planning time by 82.9% compared to baseline algorithms in dynamic scenarios, it achieves an up to 11% higher success rate and 10% better efficiency than SAC and standard DDPG. The 3D ALOS controller outperforms existing guidance strategies under time-varying currents, ensuring smoother tracking and reduced actuator effort. Full article

Real-time quality monitoring during oyster cold chain transportation is a critical component in ensuring food safety. Addressing the issues of high redundancy and insufficient environmental adaptability in existing electronic nose systems, this study proposes a multi-algorithm collaborative optimization strategy for sensor array optimization. The system integrates ten gas sensors (TGS series, MQ series), employing Random Forest (RFA), Simulated Annealing (SA), and Genetic Quantum Particle Swarm Optimization (GA-QPSO) for sensor selection. KNN combined with K-means analysis validates the optimization outcomes. Under cold chain environments at 4 °C, 12 °C, 20 °C, and 28 °C, a multidimensional dataset was constructed by extracting global variables using feature correlation functions. Experiments demonstrate that the optimized sensor count decreases from 10 to 5–6 units while maintaining recognition accuracy above 95%, with redundancy decreased by over 40%. This multi-algorithm collaborative optimization effectively balances sensor array recognition precision, resource efficiency, and environmental adaptability, providing an intelligent, high-precision technical solution for oyster cold chain monitoring. Full article

Seismic-induced vibration mitigation in multi-degree-of-freedom (MDOF) building structures calls for efficient and adaptive control strategies. Fractional-order PI^λD^μ controllers allow increased flexibility in tuning when compared with the conventional proportional integral derivative (PID) controllers. However, considering highly dynamic seismic conditions, selecting their optimal parameters remains challenging. A Particle Swarm Optimization (PSO)-based fractional order controller approach is presented in this paper for the optimal tuning of five key parameters of the PI^λD^μ controller using a two-story building model subjected to the 1940 El Centro earthquake. The controller structure is formulated using fractional-order calculus, while PSO is utilized to determine optimal gains and fractional orders without prior knowledge about the model. Simulation results indicate that the proposed fractional order proportional integral derivative (FOPID) controller is effective in suppressing structural vibrations, outperforming both classical PID control and the uncontrolled case. It is demonstrated that incorporating intelligent optimization techniques along with fractional-order control can be a promising approach toward enhancing seismic resilience in civil structures. Full article

Short-term load forecasting (STLF) is a core technical support for ensuring the safe and economic operation of power systems and efficient trading in electricity spot markets. To address the limitations of traditional forecasting models in handling the strong nonlinear and non-stationary characteristics of load data under electricity spot market conditions—where load is influenced by the coupling of multiple factors, such as meteorological conditions, electricity price signals, and seasonal patterns—we propose a hybrid forecasting model (VMD-PSO-LSTM-RF) that integrates Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM), Random Forest (RF), and Particle Swarm Optimization (PSO) to enhance the forecasting accuracy and market adaptability. First, VMD is applied to adaptively decompose the half-hourly power load data of a comprehensive user in Ningbo, Zhejiang Province, from July 2024 to June 2025. The original load series was decomposed into three components, effectively avoiding the mode aliasing problem common in traditional decomposition methods and providing high-quality inputs for subsequent forecasting. Simultaneously, meteorological data and temporal features were incorporated to construct a multi-dimensional input feature set, meeting the requirements of electricity spot markets for considering multiple influencing factors. Second, the PSO algorithm was used to optimize the key hyperparameters of LSTM and RF with seasonal differentiation. With the optimization, we aimed to maximize the Coefficient of Determination (R²) on the validation set, ensuring that the model parameters precisely matched the load fluctuation characteristics of each season. Finally, based on the feature differences of various frequency components, LSTM and RF were used to construct sub-models, and the final load value was obtained through weighted integration of the prediction results of each component. The results fully demonstrate that the proposed model can accurately capture the multi-scale fluctuation characteristics of load in electricity spot market environments, with forecasting performance superior to traditional single models and basic hybrid models; furthermore, the proposed model achieves precise extraction of multi-scale load features and in-depth temporal pattern mining, providing reliable technical support for efficient electricity spot market operation, as well as empirical references for formulating scenario-specific forecasting strategies and managing trading risks in electricity markets. 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

To enhance the operational performance of mobile manipulators in textile workshops and address the difficulty of inverse kinematics (IK) for this class of redundant manipulators, this paper leverages the robot’s structural symmetries and proposes a chaotic-mutation particle swarm optimization (CMPSO)-based IK algorithm for mobile manipulators, thus simplifying the solution process and ensuring balanced exploration of the search space. First, the coordinate–transformation relationships of the mobile manipulator are analyzed to establish its forward kinematic model. Then, a multi-objective constrained IK model is formulated according to the manipulator’s operating characteristics. The model incorporates a pose-error function, the ‘compliance’ principle, and joint-limit avoidance. To solve this model accurately, we refine the population initialization and boundary-violation handling of the particle swarm algorithm and introduce an asymmetric mechanism via an adaptive mutation strategy, culminating in a CMPSO-based IK solver. On this basis, single-pose IK tests and trajectory-planning experiments are conducted, and simulation results verify the effectiveness and stability of the proposed algorithm. Full article

Vehicle-mounted multi-UAV (VM-UAV) systems offer enhanced flexibility and rapid deployment for large-scale remote sensing tasks such as disaster response and land surveys. However, maximizing their operational efficiency remains challenging, as it requires the simultaneous resolution of task scheduling and coverage path planning—an NP-hard problem. This study presents a novel multi-objective genetic algorithm (GA) framework that jointly optimizes routing and scheduling for cost-constrained, load-balanced multi-UAV remote sensing missions. To improve convergence speed and solution quality, we introduce two innovative operators: a Multi-Region Edge Recombination Crossover (MRECX) to preserve superior path segments from parents and an Adaptive Hybrid Mutation (AHM) mechanism that dynamically adjusts mutation strategies to balance exploration and exploitation. The algorithm minimizes total flight distance while equalizing workload distribution among UAVs. Extensive simulations and experiments demonstrate that the proposed GA significantly outperforms conventional GA, particle swarm optimization (PSO), ant colony optimization (ACO), and clustering-based planning methods in both solution quality and robustness. The practical applicability of our framework is further validated through two real-world case studies. The results confirm that the proposed approach delivers an effective and scalable solution for vehicle-mounted multi-UAV scheduling and path planning, enhancing operational efficiency in time-critical remote sensing applications. Full article