Search Results (1,203)

Electrically driven compressors are a primary energy consumer in natural gas storage facilities. Formulating an optimal gas injection allocation strategy considering their nonlinear characteristics and time-of-use (TOU) electricity prices is crucial. However, single-model optimizations struggle with this due to high dimensionality and strongly coupled variables. To overcome these challenges, we propose a two-stage “instantaneous load allocation—day-ahead scheduling” framework. Stage I employs a hybrid algorithm (ICSA-WOA) to optimize load allocations across various flow rates, generating a lookup table that effectively decouples the underlying physical model. Stage II utilizes this table alongside TOU prices to perform rapid day-ahead scheduling via dynamic programming (DP). Results demonstrate that ICSA-WOA achieves superior comprehensive performance compared to seven classical swarm intelligence algorithms. Furthermore, joint optimization of the pressure ratio and load via ICSA-WOA reduces the total power consumption by 9.7–10.9% relative to traditional fixed-ratio modes. Most significantly, while rigorously ensuring daily injection targets and safety, the proposed method reduces daily electricity costs by 3.3–14.2% compared to single-model approaches, providing a reasonable strategy for economic gas storage operations. Full article

To address the challenges of time-dependent error divergence in Strapdown Inertial Navigation Systems (SINS) and the insufficient accuracy of traditional terrain matching algorithms in feature-sparse flat terrain environments, this paper proposes an intelligent terrain-aided navigation method integrating an Improved Osprey Optimization Algorithm (IOOA), Distribution Estimation, and Q-learning. Utilizing terrain information entropy as a robust matching metric, the algorithm establishes a two-phase evolutionary framework comprising Lévy flight-based random search (exploration phase) and elite-guided Gaussian Estimation of Distribution (exploitation phase). By introducing a Q-learning mechanism to adaptively regulate exploration parameters, an intelligent balance between population diversity and convergence speed is achieved. Under a unified computational benchmark, systematic multi-scenario simulations were conducted using datasets from simulated moderately undulating foothill terrain, the Libyan Sahara, and the real Digital Elevation Model (DEM) of the Junggar Basin in Xinjiang, China. Experimental results demonstrate that, compared to traditional TERCOM and mainstream swarm intelligence algorithms, the proposed algorithm drastically reduces positioning errors in the aforementioned complex terrains and significantly enhances matching accuracy. Robustness and real-time performance tests indicate that the algorithm achieves an average single-match processing time of only 0.08 s and maintains error variability as low as ±0.83 m under random perturbations. Furthermore, an ablation study confirms the necessity of the multi-strategy fusion mechanism in suppressing local optima entrapment and non-convergent oscillations. This study validates the engineering feasibility of the algorithm under conditions of low computational dependency, providing an effective technical approach for high-precision autonomous navigation in GPS-denied environments. Full article

Owing to their reliance on detailed mathematical modeling, traditional control methods encounter challenges such as high control complexity and low precision when applied to high-speed flight vehicle control under strongly disturbed atmospheric conditions. To address this limitation, this study introduces a data-driven neural network mapping approach into the field of flight vehicle control. By excavating the underlying patterns in operational data and leveraging the nonlinear mapping capability of neural networks, accurate prediction and generation of control commands are achieved, thereby eliminating the dependence on precise mathematical models and offering a novel solution for complex control problems. Building on this foundation, a self-organizing map (SOM) radial basis function (RBF) neural network is proposed. Leveraging the competitive learning mechanism of SOM, it performs adaptive clustering on input samples, dynamically optimizes the number of clusters to determine the number of hidden-layer nodes in RBF, and adopts the SOM cluster centers as the centers of RBF basis functions. This design enables the one-click data-driven determination of both the number of nodes and their corresponding center vectors, significantly simplifying the network structure design process. Meanwhile, to address inherent limitations of this network, such as suboptimal output weights, unoptimized width functions, and the inherent drawbacks of the traditional Moth-Flame Optimization (MFO) algorithm, an Adaptive Enhanced Moth-Flame Optimization (AEMFO) algorithm is developed, drawing inspiration from biological swarm intelligence. By integrating strategies such as adaptive spiral update and elite opposition-based learning, it balances the global exploration and local exploitation capabilities, and performs targeted optimization of the RBF width parameters and output-layer weights. This optimization significantly enhances the accuracy of the network in mapping attitude-control commands in strongly disturbed environments, providing robust support for the stable attitude control of high-speed flight vehicles. Finally, simulation results demonstrate that the proposed method achieves high control accuracy for flight vehicle attitude control under strongly disturbed environments. Full article

Accurate and efficient rock mass quality classification is a prerequisite for assessing slope stability, designing support schemes, and ensuring mining safety in open-pit mines. However, traditional empirical classification methods rely heavily on expert judgment and often struggle to capture the complex, nonlinear relationships among factors influencing slope stability. Existing intelligent classification models also suffer from limitations, including sensitivity to incomplete data, insufficient feature interaction learning, and unstable performance on small-scale datasets. To address these issues, this study develops a deep forest (DeepForest) model optimized by three metaheuristic algorithms—brown bear optimizer (BBO), tuna swarm optimizer (TSO), and sparrow search algorithm (SSA)—to intelligently classify slope rock mass quality. A rock mass quality dataset containing 204 groups of slope and non-slope cases was established to train and evaluate the classification performance of the DeepForest models. Six influencing factors were set as input parameters: uniaxial compressive strength (UCS) of rock, rock quality designation (RQD), spacing of discontinuities (Sd), rock mass integrity coefficient (Kv), groundwater conditions (W), and site type (St). Multivariate imputation by chained equations (MICE), isolation forest (IsoForest), and synthetic minority over-sampling technique (SMOTE) were used to handle missing values, outliers, and imbalance in the dataset, respectively. The performance of the proposed models was evaluated using five metrics: accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The experimental results indicate that the BBO-DeepForest model performed best on the independent test set, with accuracy, precision, recall, F1-score, and average AUC values of 0.878, 0.682, 0.678, 0.678, and 0.961, respectively. A comparison with seven well-known imputation algorithms revealed the superiority of the selected imputation algorithm in recovering incomplete rock mass quality datasets. Model interpretation results showed that RQD and UCS are critical feature parameters for classifying slope rock mass quality. At last, the proposed BBO-DeepForest model was employed to verify the rock mass quality of three slopes at the Luming molybdenum mine, resulting in classifications consistent with on-site observations. It demonstrates that combining DeepForest with metaheuristic optimization algorithms is a feasible and accurate approach for intelligently classifying the rock mass quality of slopes. Full article

The rapid advancement of unmanned aerial vehicles (UAVs) has greatly increased the application of various swarm intelligence algorithms in UAV path planning. To address the potential issues with the dung beetle optimizer (DBO) in UAV trajectory planning, such as low convergence accuracy, tendency to get trapped in local optima, and imbalance between global search and local exploration, a hybrid algorithm termed DBO-PSO is proposed by integrating DBO with particle swarm optimization (PSO) to solve the UAV path planning model. The Kent chaotic map is introduced to enhance population diversity and distribution uniformity, and the velocity–position update mechanism of PSO is incorporated into DBO to strengthen its global search capability. Comparative experiments are conducted on CEC2022 benchmark functions, and multiple classical swarm intelligence algorithms are selected for comparison using six evaluation metrics, along with Wilcoxon rank-sum and Friedman statistical tests. An ablation study is also performed to evaluate the contribution of each improvement component. The path planning experimental results demonstrate that compared to DBO, PSO, IDBO, and ECFDBO under the population size of 50, DBO-PSO reduces the total path cost by 44.2%, 17.3%, 8.9%, and 45.1%, respectively. The ablation study verifies that both improvement components contribute positively, which demonstrates its competitive performance and practical applicability in UAV three-dimensional path planning. The source codes to support the presented results are publicly available on GitHub. Full article

Cooperative multi-UAV path planning under dynamic and adversarial conditions demands simultaneous satisfaction of safety, efficiency, and coordination constraints, yet existing swarm-intelligence and RL–swarm hybrids rely on deterministic switching rules, tabular states, and ad hoc training schedules. This paper proposes RL-JSO, a hybrid framework in which a dueling double deep Q-network with prioritized experience replay adaptively selects among the drift, passive, and active phases of a jellyfish search optimizer, replacing the deterministic time-control rule with a learned policy. The framework integrates a five-layer hierarchical safety control mechanism, a mastery-gated nine-stage curriculum, and a shared reward module that architecturally enforces fairness between RL-JSO and a paired RL-PSO counterpart. Evaluation across four progressive campaigns with 160 independent runs per algorithm shows that, within the evaluated JSO/PSO family, RL-JSO is the only method that sustains a 100% collision-free rate across all four progressive difficulty campaigns, its Cliff’s delta over standard JSO grows monotonically with difficulty from medium to large, and under a composite cooperation metric its coordination score remains nearly invariant while comparators degrade by 17–23%. A paired inference-time ablation on the trained checkpoint provides controlled inference-time evidence that adaptive phase switching is a principal contributor to the observed test-time performance within the trained system, rather than the heuristic fallback layers. Full article

This research aimed to develop a rapid, non-destructive, and accurate method for detecting adulteration in turmeric using Visible–Near-Infrared (UV/Vis and NIR) spectroscopy combined with machine learning algorithms. Spectral data from the samples were collected and analyzed in two ranges: 170–870 nm (UV/Vis) and 900–2170 nm (NIR). Four supervised learning algorithms, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), the Multilayer Perceptron (MLP) neural network, and Decision Tree, were evaluated for modeling. To quantitatively assess model performance, we employed not only the accuracy metric but also complementary performance indicators including precision, recall, and the F1-score to provide a more comprehensive evaluation of classification effectiveness. The models developed in the 900–2170 nm spectral range demonstrated highly significant performance, with most models achieving 100% accuracy on the independent test set. To reduce data dimensionality and enhance computational efficiency, a hybrid feature selection method combining SVM with five algorithms—League Championship Algorithm (LCA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Imperialist Competitive Algorithm (ICA)—was employed. Upon evaluation of each method, the SVM-LCA was selected as the optimal feature selection technique. This algorithm successfully extracted the most effective wavelengths with the highest correlation and lowest error, which maintained or improved the accuracy of the classification models. This study confirms the high potential of UV/Vis and NIR spectroscopy as rapid, non-destructive, and precise tools for detecting adulteration in turmeric. The findings can pave the way for the development of intelligent quality control systems in the food and pharmaceutical industries, playing a crucial role in ensuring consumer health and safety. Full article

Gas well production optimization is an effective way to improve the efficiency and economic performance of natural gas development. Multiphase flow in wellbores and reservoir seepage make the process highly nonlinear, strongly coupled, and complex. Traditional simulation software can provide accurate predictions, but the high computational cost limits its use in iterative and large-scale optimization. This paper presents an integrated framework that combines numerical simulation, surrogate modeling, and intelligent optimization for gas well production parameter optimization, particularly under continuous gas lift conditions. A simulator is used to generate datasets, which are then used to train a neural network surrogate model for fast prediction of gas well production response. An improved particle swarm optimization algorithm is applied to perform global search and obtain the optimal production parameter combination. Results show that the surrogate model can reliably replace the simulator in repeated prediction tasks while substantially reducing computational cost, and the improved algorithm performs better than traditional methods in both convergence speed and optimization accuracy. Case studies confirm that the optimized parameters effectively increase gas well production. The proposed framework provides an efficient and practical approach for intelligent production optimization in gas wells under complex wellbore multiphase-flow conditions. Full article

To address the issues with the Crocodile Ambush Optimization Algorithm (CAOA) in UAV trajectory planning—such as its tendency to get stuck in local optima, the difficulty in balancing global search and local exploration, and low convergence accuracy—this study proposes a three-dimensional trajectory planning method based on the Hybrid Particle Swarm and Crocodile Ambush Optimization Algorithm (HPSOCAOA). First, a collaborative search structure combining the Particle Swarm Optimization (PSO) algorithm and the Crocodile Ambush Optimization Algorithm (CAOA) is established; second, an adaptive energy consumption coefficient is designed to address the issues of premature individual elimination in the early stages and insufficient convergence momentum in the later stages, thereby further balancing global exploration and local exploitation; finally, crossover learning is introduced. Using a cross-group replacement mechanism for superior individuals, PSO’s fine-tuning identifies high-quality individuals, which are then substituted for lower-quality individuals in CAOA. This resolves the problems of redundant low-quality individuals within the population and low search efficiency, and enhances overall optimization performance. Standard test functions demonstrate that HPSOCAOA outperforms the comparison algorithms in terms of optimization accuracy and stability. In simulation experiments for path planning in complex 3D mountainous environments, HPSOCAOA was compared with classical intelligent algorithms, verifying its superiority and practicality in complex 3D scenarios. Full article

Late deliveries represent one of the most persistent operational disruptions in global supply chains, eroding service reliability, triggering contractual penalties, and undermining the resilience of logistics networks. As supply chains become increasingly digitalized, the integration of advanced predictive analytics into operational decision-making offers a pathway toward proactive rather than reactive disruption management. This study develops and evaluates a digital analytics framework in which eXtreme Gradient Boosting (XGB), a high-performance ensemble learning algorithm, is optimized by three recent population-based metaheuristic algorithms: the weighted mean of vectors algorithm (INFO), Harris Hawks Optimization (HHO), and the Red-Billed Blue Magpie Optimizer (RBMO). Four critical XGB hyperparameters, number of estimators, maximum tree depth, learning rate, and complexity penalty, are tuned on a supply chain dataset. A population-size sensitivity analysis at two swarm configurations reveals that all three optimizers converge to functionally equivalent solutions at sufficient population diversity, providing practical guidance for computational resource allocation. The best-performing configuration, HHO-XGB, achieves a test accuracy of 97.47% and a Matthews correlation coefficient of 0.949, substantially outperforming the baseline XGB and other benchmark classifiers. To ensure transparency and support data-driven decision-making, SHapley Additive exPlanations (SHAP) analysis is applied to the optimized model, revealing that shipping mode, scheduled shipment days, shipping date, order day, order status, and order month are the dominant predictive features, confirming that late-delivery risk is primarily driven by shipment configuration and temporal patterns. The proposed framework demonstrates that integrating metaheuristic intelligence with machine learning delivers better predictive performance. Interpretability is essential to trustworthy, resilient supply chain decision-support systems. Full article

Numerous engineering problems can be represented as minimax optimization problems, including machine learning, classification, robust optimal control, signal processing, game theory, and more. Typically, minimax problems are considered challenging, especially constrained ones. The recently introduced artificial rabbits optimization (ARO) is inspired by the natural behaviour of rabbits. ARO exhibits robust effectiveness in tackling optimization challenges. Despite its advantages, ARO converges early to local optima, especially in complex or multi-modal optimization problems, and it struggles to balance exploration and exploitation, often leading to premature convergence and reduced accuracy. In this paper, we present a chaotic ARO that employs five maps exhibiting randomization behaviour to refresh candidate solutions. We assess the performance of the suggested CARO by applying it to 46 benchmark functions (25 unconstrained and 21 non-smooth minimax) and 15 constrained test functions with diverse characteristics. We evaluate its performance against six swarm intelligence algorithms. Also, we employ the chaotic maps to ARO and the six compared algorithms, and we perform a non-parametric statistical test, the Friedman test, on all outcomes. The findings show that the proposed algorithm can solve both unconstrained and constrained minimax problems more effectively and efficiently than other swarm intelligence methods. Full article

The rapid evolution of intelligent cluster systems—such as UAV swarms and networked autonomous agents—has brought spatial optimization and decision-making to the forefront of intelligent systems research. This paper provides a systematic and critical review of recent advances in spatial optimization for multi-agent intelligent clusters, focusing on four core domains: UAV swarm path planning, resource allocation, traffic network analysis, and visualization technologies. A bibliometric analysis based on the Web of Science Core Collection (2000–2024) identifies two major methodological transitions. In path planning, research has moved from traditional algorithms (A*, Dijkstra, dynamic programming), effective in static settings but limited in dynamic and large-scale applications, to bio-inspired optimization and deep reinforcement learning methods that improve adaptability and efficiency. In resource allocation, studies have shifted from centralized single-algorithm models to distributed, self-organizing hybrid frameworks that enhance robustness and real-time responsiveness. Moreover, intelligent cluster technologies are increasingly applied to urban traffic management and visualization, where analysis has advanced from static 2D mapping to interactive 3D and immersive VR/AR environments. A comparative framework is proposed to evaluate existing algorithms by adaptability, computational complexity, and scalability. The review concludes that future research should emphasize hybrid algorithm integration, cross-disciplinary data-driven modeling, and immersive visualization to support real-time decision-making. This study consolidates the evolutionary trajectory of intelligent cluster optimization, identifies critical research gaps, and outlines a roadmap for the next generation of intelligent spatial optimization systems. Full article

To address the cross-sensitivity and non-linear coupling issues caused by the coexistence of hydrogen, carbon monoxide, ammonia, and nitrogen dioxide in industrial environments, a flow-through quantitative detection system based on a MEMS gas sensor array was designed and constructed. The steady-state peak sampling method was employed for feature extraction from high-dimensional time-series data, and regression prediction models were developed using a traditional BP neural network and BP neural networks optimized by four swarm intelligence algorithms (ALA, AOO, SFOA, and SDO). The experimental results indicate that the intelligent optimization algorithms excel in decoupling the “cross-response” phenomenon, with all optimized models outperforming the traditional BP network. Among them, the SDOBP (Sledge Dog Optimizer-BP) model demonstrated the best overall performance, achieving the highest accuracy in carbon monoxide and hydrogen detection, with the Root Mean Square Error for hydrogen reduced to 2.17, an 84.2% improvement over the traditional model. The system achieves high-precision quantitative inversion of multi-component gases in complex environments, providing an effective means for industrial environmental safety monitoring. Full article

Determining the compatible prestress and geometry under self-weight constitutes a key challenge in the form-finding of cable–truss structures. To overcome the limitations of experience-dependent trial methods and enhance computational efficiency, this paper proposes an automated and integrated methodology by synergistically combining a simplified mechanical model with an improved Particle Swarm Optimization (PSO) algorithm. The core of the method lies in formulating the form-finding process as an optimization problem, where the horizontal inclination angles of the lower-chord cables serve as the design variables for all radial cable–truss frames. To efficiently solve this high-dimensional optimization problem, an improved PSO algorithm is proposed, which introduces logistic chaotic mapping for particle initialization and a mutation operator within the iterative loop. Ablation studies confirm the individual contribution of each algorithmic enhancement. The algorithm intelligently searches for the optimal angle set, thereby simultaneously resolving the prestress and geometry. The proposed approach is rigorously validated through two representative numerical examples: a circular Type I and an elliptical Type II cable–truss, considering both cases with and without self-weight. The results demonstrate that the improved PSO-based solution achieves prestress distributions and nodal coordinates in excellent agreement with established benchmark data. More importantly, it attains this high precision with significantly reduced computational cost in terms of particle swarm size and iteration number. In conclusion, this improved PSO-based approach provides an efficient, accurate, and automated tool for the integrated prestress-geometry design of cable–truss structures, demonstrating strong potential for practical engineering application. Full article

Process parameter control is critical for reducing casting defects in ZTA2 alloy pump body investment casting. However, there exists a complex nonlinear relationship between parameters such as pouring temperature, pouring time, and shell preheating temperature, and defects including total defect volume, shrinkage porosity, and shrinkage cavities, posing significant challenges to accurate prediction and optimization. To address this issue, this study proposes an integrated strategy for defect prediction and process optimization that combines the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization (PSO), and Backpropagation Neural Network (BP neural network). First, an L₂₅(5³) orthogonal experiment was designed, and a dataset consisting of 25 orthogonal samples and 97 random samples was constructed by combining ProCAST simulations, covering the entire parameter domain of pouring temperature, pouring time, and shell preheating temperature. Subsequently, the PSO algorithm was used to optimize the initial weights and thresholds of the BP neural network, and Bayesian regularization and 5-fold cross-validation were introduced to build a high-precision defect prediction model. The SHapley Additive exPlanations (SHAP) analysis was employed to clarify parameter sensitivity and interaction mechanisms, and the NSGA-II was combined to realize multi-objective process optimization. The results show that: compared with the traditional BP neural network, the optimized PSO-BP model improves the coefficient of determination (R²) of the test set for total defect volume prediction by 20.82% and reduces the root mean square error (RMSE) by 33.34%; for shrinkage porosity volume prediction, the R² is increased by 7.93% and the RMSE is reduced by 22.71%, which effectively solves the problems of local optimization and weak generalization ability. Pouring time is the most sensitive parameter affecting defects, and the coupling effect between pouring temperature and pouring time is the strongest. Considering actual production conditions, the superior process parameters are determined as follows: pouring temperature of 1800 °C, pouring time of 4 s, and shell preheating temperature of 475 °C. Compared with the pre-optimization results, this parameter combination reduces the total defect volume by 38.92% and the shrinkage porosity volume by 51.62%. The intelligent optimization framework constructed in this study provides reliable technical support for the accurate control of defects in ZTA2 titanium alloy pump body investment casting, and has important engineering value for improving the quality of castings in industrial production and reducing costs. Full article