Search Results (2,171)

This study addresses the challenges of high-dimensional data, such as the curse of dimensionality and feature redundancy, which can be viewed as an inherent asymmetry in the data space. To restore a balanced symmetry and build a more complete feature representation, we propose an enhanced feature engineering model (EFEM) that employs a novel dual-strategy approach. First, we present a symmetrical feature selection algorithm that combines an improved Dolphin Swarm Algorithm (DSA) with the Maximum Relevance–Minimum Redundancy (mRMR) criterion. This method not only selects an optimal, high-relevance feature subset, but also identifies the remaining features as a complementary, redundant subset. Second, an ensemble learning-based feature reconstruction algorithm is introduced to mine potential information from these redundant features. This process transforms fragmented, redundant information into a new, synthetic feature, thereby establishing a form of information symmetry with the selected optimal subset. Finally, the EFEM constructs a high-performance feature space by symmetrically integrating the optimal feature subset with the synthetic feature. The model’s superior performance is extensively validated on nine standard UCI regression datasets, with comparative analysis showing that it significantly outperforms similar algorithms and achieves an average goodness-of-fit of 0.9263. The statistical significance of this improvement is confirmed by the Wilcoxon signed-rank test. Comprehensive analyses of parameter sensitivity, robustness, convergence, and runtime, as well as ablation experiments, further validate the efficiency and stability of the proposed algorithm. The successful application of the EFEM in a real-world product demand forecasting task fully demonstrates its practical value in complex scenarios. Full article

Multi-drone operations face significant efficiency challenges when launch pad locations are predetermined without optimization, leading to suboptimal route configurations and increased travel distances. This research addresses launch pad positioning as a continuous planar location-routing problem (PLRP), developing a genetic algorithm framework integrated with multiple Traveling Salesman Problem (mTSP) solvers to optimize launch pad coordinates within operational areas. The methodology was evaluated through extensive experimentation involving over 17 million test executions across varying problem complexities and compared against brute-force optimization, Particle Swarm Optimization (PSO), and simulated annealing (SA) approaches. The results demonstrate that the genetic algorithm achieves 97–100% solution accuracy relative to exhaustive search methods while reducing computational requirements by four orders of magnitude, requiring an average of 527 iterations compared to 30,000 for PSO and 1000 for SA. Smart initialization strategies and adaptive termination criteria provide additional performance enhancements, reducing computational effort by 94% while maintaining 98.8% solution quality. Statistical validation confirms systematic improvements across all tested scenarios. This research establishes a validated methodological framework for continuous launch pad optimization in UAV operations, providing practical insights for real-world applications where both solution quality and computational efficiency are critical operational factors while acknowledging the simplified energy model limitations that warrant future research into more complex operational dynamics. Full article

Accurate scrap forecasting is essential for advancing green manufacturing, as reducing defective output not only lowers production costs but also prevents unnecessary resource consumption and environmental impact. Effective scrap prediction enables manufacturers to take proactive measures to minimize waste generation, thereby supporting sustainability goals and improving production efficiency. This study proposes a hybrid ensemble framework that integrates CatBoost and XGBoost, combined with Particle Swarm Optimization (PSO), to enhance prediction accuracy in industrial applications. The model exploits the complementary strengths of both algorithms by applying weighted averaging and stacked generalization, allowing it to process heterogeneous datasets containing both categorical and numerical variables. A case study in the aerospace manufacturing sector demonstrates the effectiveness of the proposed approach. Compared to standalone models, the PSO-enhanced hybrid ensemble achieved more than a 30% reduction in Root Mean Squared Error (RMSE), confirming its ability to capture complex interactions among diverse process parameters. Feature importance analysis further showed that categorical attributes, such as machine type and operator, are as influential as numerical parameters, underscoring the need for hybrid modeling. Although the model requires higher computational effort, the integration of PSO significantly improves robustness and scalability. By reducing scrap and optimizing resource utilization, the proposed framework provides a data-driven pathway toward greener, more resource-efficient, and resilient manufacturing systems. Full article

Due to the limited and fixed field of view of the onboard camera, the guiding beacons gradually drift out of sight as the AUV approaches the docking station, resulting in unreliable positioning and intermittent data. This paper proposes an underwater autonomous docking visual localization method based on a cage-type dual-layer guiding light array. To address the gradual loss of beacon visibility during AUV approach, a rationally designed localization scheme employing a cage-type, dual-layer guiding light array is presented. A dual-layer light array localization algorithm is introduced to accommodate varying beacon appearances at different docking stages by dynamically distinguishing between front and rear guiding light arrays. Following layer-wise separation of guiding lights, a robust tag-matching framework is constructed for each layer. Particle swarm optimization (PSO) is employed for high-precision initial tag matching, and a filtering strategy based on distance and angular ratio consistency eliminates unreliable matches. Under extreme conditions with three missing lights or two spurious beacons, the method achieves 90.3% and 99.6% matching success rates, respectively. After applying filtering strategy, error correction using backtracking extended Kalman filter (BTEKF) brings matching success rate to 99.9%. Simulations and underwater experiments demonstrate stable and robust tag matching across all docking phases, with average detection time of 0.112 s, even when handling dual-layer arrays. The proposed method achieves continuous visual guidance-based docking for autonomous AUV recovery. Full article

This study proposes an integrated framework for the optimal sizing of off-grid hybrid energy systems, combining photovoltaic panels, wind turbines, battery storage, a diesel generator, and an inverter. The methodology uniquely integrates long-term meteorological forecasting through a hybrid approach based on the Discrete Wavelet Transform and Long Short-Term Memory networks, together with metaheuristic optimization techniques (Particle Swarm Optimization and Genetic Algorithm), to minimize the system’s total annual cost. A case study was conducted in Guanambi, Brazil, using ten years (2012–2021) of hourly data on wind speed, solar irradiance, and ambient temperature. Forecasting results show that the hybrid Discrete Wavelet Transform–Long Short-Term Memory model outperforms the conventional Long Short-Term Memory approach, reducing error metrics and improving predictive accuracy. In the optimization stage, Particle Swarm Optimization consistently achieved lower costs and more stable convergence compared to the Genetic Algorithm. The optimal configuration comprised 450 photovoltaic panels, 10 wind turbines, 66 lithium iron phosphate battery, and 1 diesel generator, yielding a total annual cost of $105,381.17, a cost of energy of $0.1243/kWh, and minimal diesel dependence ($8825.89 annually). The proposed framework demonstrates robustness, economic viability, and applicability for providing sustainable and reliable electricity in isolated regions with high renewable energy potential. Full article

This study examines the effects of injection timing and cerium oxide (CeO₂) nanoparticle (NP) size on NOx emissions and brake thermal efficiency (BTE) in a compression ignition engine, contributing to Sustainable Development Goals 7 and 13. Experiments were conducted at four load conditions (25–100%) using NP sizes of 10 nm, 30 nm, and 80 nm. An artificial neural network integrated with multi-objective particle swarm optimization (ANN-PSO) was employed to identify optimal operating parameters. The optimized configurations improved BTE and reduced NOx emissions across all loads; for example, at 75% load, BTE increased from 30.38% (average) to 32.13% (optimum), while simultaneously reducing the NOx emissions from 1322 ppm (average) to 1272 ppm (optimum). Analysis of variance (ANOVA) confirmed load as the most significant factor (p < 0.001), followed by injection timing and NP size. The model predictions closely matched experimental results, validating the optimization approach. The optimization suggests an interpolated optimal NP size of approximately 45 nm, highlighting the potential for further exploration. This integrated experimental and computational approach offers a promising framework for improving combustion efficiency and reducing emissions, thereby advancing cleaner and more sustainable fuel technologies. Full article

With the large-scale deployment of 5G technology, the rationality of communication base station siting is crucial for network performance, construction costs, and operational efficiency. Traditional site selection methods rely heavily on manual experience, exhibiting strong subjectivity and difficulty in balancing multi-objective optimization. Existing heuristic algorithms suffer from slow convergence speeds and susceptibility to local optima. To address these challenges, this paper constructs a multi-objective base station site selection model that simultaneously minimizes costs, maximizes coverage contributions, and minimizes interference. It achieves quantitative balance among objectives through normalization and weight fusion, while introducing constraints to ensure engineering feasibility. Concurrently, the genetic algorithm underwent targeted optimization by introducing an adaptive migration strategy based on population diversity and a cosine-type parameter adjustment strategy. This approach was integrated with the particle swarm optimization algorithm to balance exploration and exploitation while mitigating premature convergence. Experimental validation demonstrates that the improved algorithm achieves faster convergence and greater stability compared to traditional genetic algorithms and particle swarm optimization, while satisfying engineering constraints such as base station quantity, coverage, and interference. This research provides an efficient and feasible solution for intelligent base station site planning. Full article

Educational data mining (EDM) plays a crucial role in developing intelligent early warning systems that enable timely interventions to improve student outcomes. This study presents a novel approach to student performance prediction by integrating metaheuristic hyperparameter optimization with explainable artificial intelligence for enhanced learning analytics. While Light Gradient Boosting Machine (LightGBM) demonstrates efficiency in educational prediction tasks, achieving optimal performance requires sophisticated hyperparameter tuning, particularly for complex educational datasets where accuracy, interpretability, and actionable insights are paramount. This research addressed these challenges by implementing and evaluating five nature-inspired metaheuristic algorithms: Fox Algorithm (FOX), Giant Trevally Optimizer (GTO), Particle Swarm Optimization (PSO), Sand Cat Swarm Optimization (SCSO), and Salp Swarm Algorithm (SSA) for automated hyperparameter optimization. Using rigorous experimental methodology with 5-fold cross-validation and 20 independent runs, we assessed predictive performance through comprehensive metrics including Coefficient of Determination (R²), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Relative Absolute Error (RAE), and Mean Error (ME). Results demonstrate that metaheuristic optimization significantly enhances educational prediction accuracy, with SCSO-LightGBM achieving superior performance with R² of 0.941. SHapley Additive exPlanations (SHAP) analysis provides crucial interpretability, identifying Attendance, Hours Studied, Previous Scores, and Parental Involvement as dominant predictive factors, offering evidence-based insights for educational stakeholders. The proposed SCSO-LightGBM framework establishes an intelligent, interpretable system that supports data-driven decision-making in educational environments, enabling proactive interventions to enhance student success. Full article

To address the tool calibration challenge in robotic systems equipped with grinding tools, this paper proposes a high-precision method utilizing a stylus assembly and the Particle Swarm Optimization (PSO) algorithm. A global optimization strategy is implemented, which simultaneously identifies and compensates for coupled error sources, including the robot’s kinematic (DH) parameters, the tool coordinate frame (TCF), and the stylus tip’s spatial position. This approach transforms the complex calibration task into a constrained, high-dimensional optimization problem. The experimental results demonstrate the method’s effectiveness, reducing the final calibration Root Mean Square Error (RMSE) to below 0.1 mm. Validation through a practical grinding experiment confirmed a significant improvement in machining accuracy, with the workpiece’s axis deviation from the ideal model decreasing from 1.477° to 0.326°, and the maximum contour error being reduced from 1.4 mm to under 0.3 mm. This study provides a robust, low-cost technical solution for tool calibration in complex industrial applications. Full article

Environmental pollution caused by shipping has always received great attention from the international community. Currently, due to the difficulty of fully electrifying medium- and large-scale ships, the hybrid energy ship power system (HESPS) will be the main type in the future. Considering the economic and long-term energy efficiency of ships, as well as the uncertainty of the output power of renewable energy units, this paper proposes an improved design for an integrated power system for large cruise ships, combining renewable energy and a hybrid energy storage system. An energy management strategy (EMS) based on time-gradient control and considering load dynamic response, as well as an energy storage power allocation method that considers the characteristics of energy storage devices, is designed. A bi-level power capacity optimization model, grounded in comprehensive scenario planning and aiming to optimize maximum return on equity, is constructed and resolved by utilizing an improved particle swarm optimization algorithm integrated with dynamic programming. Based on a large-scale cruise ship, the aforementioned method was investigated and compared to the conventional planning approach. It demonstrates that the implementation of this optimization method can significantly decrease costs, enhance revenue, and increase the return on equity from 5.15% to 8.66%. Full article

Aggregate masonry buildings in historic urban centers constitute tangible testimony of collective identity and historical continuity. They encompass both simple terraced configurations and more intricate clusters, which are inherently vulnerable to earthquake-induced damage, due to their typological features and the transformations that occurred in the course of time. Strategies aimed at the protection and valorization of such typical architectural heritage should be based on the recognition of their peculiarities, so that the intangible values embedded within the historic fabric can be preserved. A simplified approach able to identify the effect of facade layout on the vulnerability of terraced buildings was validated on a historical center struck by the Central Italy earthquake. It is based on the evaluation of vulnerability factors derived by the application of a multi-level procedure on a large scale, which integrates data on typological and structural aspects, as well as on the condition state and previous interventions. In the center in question, the evidence of prevalent shear damage in the continuous frontage of the buildings facing the main street suggested the in-depth analysis of the facade’s characteristics, and its relationship with the main direction of the seismic swarm. Starting from a preliminary abacus of twelve vulnerability factors, 16 archetypes of facades at increasing vulnerability defined by a combination of the most significant geometrical features of building aggregates were identified. These virtual models encompass typical features that can be found in similar buildings in different contexts, thus enabling preventive actions based on parametric assessment. Full article

This study pioneers a fully remote sensing-based framework for mapping heatwave susceptibility, integrating the TabTransformer deep learning model with Particle Swarm Optimization (PSO) for robust hyperparameter tuning. The central question addressed is whether a fully remote sensing-driven, PSO-optimized TabTransformer can achieve accurate, scalable, and spatially detailed heatwave susceptibility mapping in data-scarce regions such as Central Asia. Utilizing ERA5-derived heatwave evidence and thirteen environmental and socio-economic predictors, the workflow produces high-resolution susceptibility maps spanning five Central Asian countries. Comparative analysis evidences that the PSO-optimized TabTransformer model outperforms the baseline across multiple metrics. On the test set, the optimized model achieved an RMSE of 0.123, MAE of 0.034, and R² of 0.938, outperforming the standalone TabTransformer (RMSE = 0.132, MAE = 0.038, R² = 0.93). Discriminative capacity also improved, with AUROC increasing from 0.933 to 0.940. The PSO-tuned model delivered faster convergence, lower final loss, and more stable accuracy during training and validation. Spatial outputs reveal heightened susceptibility in southern and southwestern sectors—Turkmenistan, Uzbekistan, southern Kazakhstan, and adjacent lowlands—with statistically significant improvements in spatial precision and class delineation confirmed by Chi-squared, Friedman, and Wilcoxon tests, all with congruent p-values of <0.0001. Feature importance analysis consistently identifies maximum temperature, frequency of hot days, and rainfall as dominant predictors. These advancements validate the potential of data-driven, deep learning approaches for reliable, scalable environmental hazard assessment, crucial for climate adaptation planning in vulnerable regions. Full article

The A* algorithm is a cornerstone in mobile robot navigation. However, the traditional A* suffers from key limitations such as poor path smoothness, lack of adaptability to dynamic environments, and high computational costs in large-scale maps. This review presents a comprehensive analysis of 20 recent studies (2020–2025) on improved A* variants and their hybrid integrations with complementary algorithms. The improvements are categorized into two core strategies: (i) geometric and structural optimization, heuristic weighting and adaptive search schemes in A* algorithm, and (ii) hybrid models combining A* with local planners such as Dynamic Window Approach (DWA), Artificial Potential Field (APF), and Particle Swarm Optimization (PSO). For each group, the mathematical formulations of evaluation functions, smoothing techniques, and constraint handling mechanisms are detailed. Notably, hybrid frameworks demonstrate improved robustness in dynamic or partially known environments by leveraging A* for global optimality and local planners for real-time adaptability. Case studies with simulated grid maps and benchmark scenarios show that even marginal improvements in path length can coincide with substantial gains in safety and directional stability. This review not only synthesizes the state of the art in A*-based planning but also outlines design principles for building intelligent, adaptive, and computationally efficient navigation systems. Full article

The optimal utilization of UAV-integrated photovoltaic (PV) systems demands accurate modeling that accounts for dynamic flight conditions. This paper introduces a novel computational intelligence-based framework that models the behavior of a moving PV system mounted on a UAV. A unique mathematical approach is developed to translate UAV flight dynamics, specifically roll, pitch, and yaw, into the tilt and azimuth angles of the PV module. To adaptively estimate the diode ideality factor under varying conditions, the Grey Wolf Optimization (GWO) algorithm is employed, outperforming traditional methods like Particle Swarm Optimization (PSO). Using a one-year environmental dataset, multiple machine learning (ML) models are trained to predict maximum power point (MPP) parameters for a commercial PV panel. The best-performing model, Rational Quadratic Gaussian Process Regression (RQGPR), demonstrates high accuracy and low computational cost. Furthermore, the proposed ML-based model is experimentally integrated into an incremental conductance (IC) MPPT technique, forming a hybrid MPPT controller. Hardware and experimental validations confirm the model’s effectiveness in real-time MPP prediction and tracking, highlighting its potential for enhancing UAV endurance and energy efficiency. Full article

Personnel selection represents a two-sided matching problem in which firms compete for qualified candidates by designing job-offer packages. While traditional models assume fully rational agents, real-world decision-makers often face bounded rationality due to limited information and cognitive constraints. This study develops a matching framework that incorporates bounded rationality through the Quantal Response Equilibrium, where firms and candidates act as probabilistic rather than perfect optimizers under uncertainty. Using Maximum Likelihood Estimation and organizational hiring data, we validate that both sides display bounded rational behavior and that rationality increases as the selection process advances. Building on these findings, we propose a two-stage stochastic optimization approach to determine optimal job-offer packages that balance organizational policies with candidate competencies. The optimization problem is solved using particle swarm optimization, which efficiently explores the solution space under uncertainty. Data analysis reveals that only 23.10% of low-level hiring decisions align with rational choice predictions, compared to 64.32% for high-level positions. In our case study, bounded rationality increases package costs by 26%, while modular compensation packages can reduce costs by up to 25%. These findings highlight the cost implications of bounded rationality, the advantages of flexible offers, and the systematic behavioral differences across job levels. The framework provides theoretical contributions to matching under bounded rationality and offers practical insights to help organizations refine their personnel selection strategies and attract suitable candidates more effectively. Full article