Search Results (536)

Existing metaheuristic approaches often struggle to maintain an effective exploration–exploitation balance and are prone to premature convergence when addressing highly conflicting time–cost–safety–risk trade-off problems (TCSRTPs) under complex construction project constraints, which can adversely affect project productivity, safety, and the provision of decent jobs in the construction sector. To overcome these limitations, this study introduces a hybrid metaheuristic called the Q-Learning Inspired Mountain Gazelle Optimizer (QL-MGO) for solving multi-objective TCSRTPs in construction project management, supporting the delivery of resilient infrastructure and resilient building projects. QL-MGO enhances the original MGO by integrating Q-learning with an opposition-based learning strategy to improve the balance between exploration and exploitation while reducing computational effort and enhancing resource efficiency in construction scheduling. Each gazelle functions as an adaptive agent that learns effective search behaviors through a state–action–reward structure, thereby strengthening convergence stability and preserving solution diversity. A dynamic switching mechanism represents the core innovation of the proposed approach, enabling Q-learning to determine when opposition-based learning should be applied based on the performance history of the search process. The performance of QL-MGO is evaluated using 18- and 37-activity construction scheduling problems and compared with NDSII-MGO, NDSII-Jaya, NDSII-TLBO, the multi-objective genetic algorithm (MOGA), and NDSII-Rao-2. The results demonstrate that QL-MGO consistently generates superior Pareto fronts. For the 18-activity project, QL-MGO achieves the highest hypervolume (HV) value of 0.945 with a spread of 0.821, outperforming NDSII-Rao-2, MOGA, and NDSII-MGO. Similar results are observed for the 37-activity project, where QL-MGO attains the highest HV of 0.899 with a spread of 0.674, exceeding the performance of NDSII-Jaya, NDSII-TLBO, and NDSII-MGO. Overall, the integration of Q-learning significantly enhances the search capability of MGO, resulting in faster convergence, improved solution diversity, and more reliable multi-objective trade-off solutions. QL-MGO therefore serves as an effective and computationally efficient decision-support tool for construction scheduling that promotes safer, more reliable, and resource-efficient project delivery. Full article

Pragmatic sustainability emphasizes that policies must adapt to the reality of multi-level governance to balance targets and feasibility. To explore how this concept is embodied in China’s carbon peaking policies, this study adopted natural language processing (NLP) and machine learning methods to conduct a systematic quantitative analysis of 316 carbon peaking policy documents spanning from the national to county levels in China. The findings reveal that the policy system presented a distinct logic of pragmatic coordination. The application of legal instruments decreased with descending administrative levels, whereas that of supervision instruments showed the opposite trend; central-level targets were more flexible, while local governments demonstrated higher policy intensity in specific targets and livelihood-related sectors. The regional differences in policy intensity were closely associated with local economic development and energy structure, indicating that future policy optimization should more thoroughly implement the principle of common but differentiated responsibilities in target decomposition and dynamic adjustment. This study not only provides a novel quantitative perspective for investigating pragmatic sustainability in carbon peaking policy texts but also offers critical empirical evidence for synergistically advancing SDG 13 (climate action) with other SDGs. Full article

Accurate prediction of shallow soil temperature is essential for agriculture, geotechnical design, and ground-coupled energy systems. This study proposes a novel hybrid machine-learning framework in which four tree-based regressors (Decision Tree, Random Forest, XGBoost, and Bagging) are optimized using a newly developed Tri-phase Opposition Adaptive Random Search (TOARS) algorithm. Soil temperature measurements collected in 2024 at depths of 1.0 m and 2.0 m were combined with meteorological variables to train and evaluate the models. TOARS optimization reduced prediction errors by up to 32% for MAE and 28% for RMSE compared with default hyperparameters. At 1.0 m, the optimized Decision Tree achieved MAE = 0.29 °C, RMSE = 0.41 °C, and R² = 0.9993, while at 2.0 m, XGBoost reached MAE = 0.35 °C, RMSE = 0.47 °C, and R² = 0.9991. The TOARS-based hybrid ensemble provided the most stable performance across both depths. The results demonstrate that integrating TOARS with tree-based models substantially enhances predictive accuracy and offers a robust solution for soil-temperature forecasting in shallow layers. Full article

Adaptive decision-making requires flexible responses to environmental feedback and integration of information over time. Win–stay/lose–shift strategies describe immediate responses to outcomes: repeating a choice after a win (win–stay) or switching after a loss (lose–shift). Although these strategies have been examined using the Preschool Gambling Task, no study has investigated them in the widely used Children’s Gambling Task (CGT) to our knowledge. Our primary aim was to examine whether preschoolers adjust these strategies as they learn environmental contingencies. Using a shortened (40-trial) CGT with one advantageous deck (smaller rewards, smaller losses, net gains) and one disadvantageous deck (bigger rewards, bigger losses, net losses), we investigated strategy use in typically developing 3–5-year-old children (N = 98; 63% female; 88% white; 96% college-educated caregivers). A secondary aim examined whether higher cognitive self-regulation—executive function (EF) and metacognition—improves children’s effective deck-specific strategy use. Results showed preschoolers increasingly adopted win–stay in the advantageous deck but showed reduced lose–shift over time regardless of deck. Three-year-olds used significantly less lose–shift than 4-to-5-year-olds. Critically, metacognition—but not EF—uniquely predicted deck-specific strategies: children who knew which deck was better used more win–stay in the advantageous deck and more lose–shift in the disadvantageous deck, controlling for age, verbal ability, and strategy use in the opposite deck. These findings illuminate preschoolers’ strategic adaptation and highlight metacognition as a key driver of adaptive decision-making. Full article

With the explosive growth of data across various fields, effective data preprocessing has become increasingly critical. Evolutionary and swarm intelligence algorithms have shown considerable potential in feature selection. However, their performance often deteriorates in large-scale problems, due to premature convergence and limited exploration ability. To address these limitations, this paper proposes an algorithm named IHBOFS, a biomimetics-inspired optimization framework that integrates multiple adaptive strategies to enhance performance and stability. The introduction of the Good Point Set and Elite Opposition-Based Learning mechanisms provides the population with a well-distributed and diverse initialization. Furthermore, adaptive exploitation–exploration balancing strategies are designed for each subpopulation, effectively mitigating premature convergence. Extensive ablation studies on the CEC2022 benchmark functions verify the effectiveness of these strategies. Considering the discrete nature of feature selection, IHBOFS is further extended with continuous-to-discrete mapping functions and applied to six real-world datasets. Comparative experiments against nine metaheuristic-based methods, including Harris Hawk Optimization (HHO) and Ant Colony Optimization (ACO), demonstrate that IHBOFS achieves an average classification accuracy of 92.57%, confirming its superiority and robustness in high-dimensional feature selection tasks. Full article

This work proposes an Enhanced Chimp Optimization Algorithm (EChOA) for solving continuous and constrained data science and engineering optimization problems. The EChOA integrates a self-adaptive DE/current-to-pbest/1 (with jDE-style parameter control) variation stage with the canonical four-leader ChOA guidance and augments the search with three lightweight modules: (i) L’evy flight refinement around the incumbent best, (ii) periodic elite opposition-based learning, and (iii) stagnation-aware partial restarts. The EChOA is compared with more than 35 optimizers on the CEC2022 single-objective suite (12 functions). The results shows that the EChOA attains state-of-the-art results at both $D=10$ and $D=20$. At $D=10$, it ranks first on all functions (average rank $1.00$; $12/12$ wins) with the lowest mean objective and the smallest dispersion relative to the strongest competitor (OMA). At $D=20$, the EChOA retains the best overall rank and achieves top scores on most functions, indicating stable scalability with problem dimension. Pairwise Wilcoxon signed-rank tests ($\alpha =0.05$) against the full competitor set corroborate statistical superiority on the majority of functions at both dimensions, aligning with the aggregate rank outcomes. Population size studies indicate that larger populations primarily enhance reliability and time to improvement while yielding similar terminal accuracy under a fixed iteration budget. Four constrained engineering case studies (including welded beam, helical spring, pressure vessel, and cantilever stepped beam) further confirm practical effectiveness, with consistently low cost/weight/volume and tight dispersion. Full article

Digital microfluidic biochips (DMFBs) find extensive applications in biochemical experiments, medical diagnostics, and safety-critical domains, with their reliability dependent on efficient online testing technologies. However, traditional random search algorithms suffer from slow convergence and susceptibility to local optima under complex fluidic constraints. This paper proposes a hybrid optimization method based on priority strategy and an improved sparrow search algorithm for DMFB online test path planning. At the algorithmic level, the improved sparrow search algorithm incorporates three main components: tent chaotic mapping for population initialization, cosine adaptive weights together with Elite Opposition-based Learning (EOBL) to balance global exploration and local exploitation, and a Gaussian perturbation mechanism for fine-grained refinement of promising solutions. Concurrently, this paper proposes an intelligent rescue strategy that integrates global graph-theoretic pathfinding, local greedy heuristics, and space–time constraint verification to establish a closed-loop decision-making system. The experimental results show that the proposed algorithm is efficient. On the standard 7 × 7–15 × 15 DMFB benchmark chips, the shortest offline test path length obtained by the algorithm is equal to the length of the Euler path, indicating that, for these regular layouts, the shortest test path has reached the known optimal value. In both offline and online testing, the shortest paths found by the proposed method are better than or equal to those of existing mainstream algorithms. In particular, for the 15 × 15 chip under online testing, the proposed method reduces the path length from 543 and 471 to 446 compared with the IPSO and IACA algorithms, respectively, and reduces the standard deviation by 53.14% and 39.4% compared with IGWO in offline and online testing. Full article

Urban waterfront spaces are key venues for residents’ physical activity, and their spatial environment significantly impacts usage efficiency. Existing studies predominantly employ linear models and focus on single waterfront types, making it difficult to reveal differences across various types and the nonlinear mechanisms of influencing factors. To address this, this study investigates three types of waterfront spaces in Shenzhen—riverfront, lakeshore, and seafront spaces—integrating multi-source data and machine learning techniques to systematically analyze the differential impacts of the same elements on physical activity. The results indicate: (1) In terms of transportation accessibility, public transport is the most important factor for riverfront and lakeshore spaces, while road network accessibility is most critical for seafront spaces. (2) Regarding natural landscapes, the dominant factors are normalized difference vegetation index (NDVI) for riverfront spaces, green view index for lakeshore spaces, and distance to the shoreline for seafront spaces. (3) For facility services, the core factors are building density (riverfront), number of sports facilities (lakeshore), and number of leisure facilities (seafront). (4) The study further reveals nonlinear relationships and threshold effects of multiple elements. For instance, a turning point in physical activity intensity occurs when the distance to a subway station reaches 2–2.5 km. The green view index shows a threshold of 30% in the overall model, while dual-threshold phenomena are observed in the lakeshore and seafront models. (5) Synergistic effects between elements vary by waterfront type: in riverfront and seafront spaces, activity is more vibrant when areas are close to subway stations and have a low sky view index, whereas the opposite pattern is observed in lakeshore spaces. A combination of a high green view index and greater distance to the shoreline promotes activity in lakeshore spaces, while a high green view index combined with proximity to the shoreline has the most significant promotional effect in riverfront and seafront spaces. This study provides a scientific basis for health-oriented, precise planning and design of urban waterfront spaces. Full article

Dam displacement monitoring is crucial for assessing structural safety; however, conventional models often prioritize single-task prediction, leading to an inherent difficulty in balancing monitoring data quality with model performance. To bridge this gap, this study proposes a novel two-stage analytical framework that synergistically integrates an improved isolation forest (iForest) with a metaheuristic-optimized random forest (RF). The first stage focuses on data cleaning, where Kalman filtering is applied for denoising, and a newly developed Dynamic Threshold Isolation Forest (DTIF) algorithm is introduced to effectively isolate noise and outliers amidst complex environmental loads. In the second stage, the model’s predictive capability is enhanced by first employing the LASSO algorithm for feature importance analysis and optimal subset selection, followed by an Improved Reptile Search Algorithm (IRSA) for fine-tuning RF hyperparameters, thereby significantly boosting the model’s robustness. The IRSA incorporates several key improvements: Tent chaotic mapping during initialization to ensure population diversity, an adaptive parameter adjustment mechanism combined with a Lévy flight strategy in the encircling phase to dynamically balance global exploration and convergence, and the integration of elite opposition-based learning with Gaussian perturbation in the hunting phase to refine local exploitation. Validated against field data from a concrete hyperbolic arch dam, the proposed DTIF algorithm demonstrates superior anomaly detection accuracy across nine distinct outlier distribution scenarios. Moreover, for long-term displacement prediction tasks, the IRSA-RF model substantially outperforms traditional benchmark models in both predictive accuracy and generalization capability, providing a reliable early risk warning and decision-support tool for engineering practice. Full article

Affordable optical data from Unmanned Aerial Vehicles (UAVs) coupled with process-based models could constitute an integrative platform to map complex spatio-temporal patterns of nitrate leaching and reduce uncertainties in tightening the nitrogen (N) cycle of silvopastoral systems. This study uses field data from a commercial farm in Denmark with lactating sows housed in paddocks with pastures flanking a central zone of poplars, either pruned (P) or unpruned (tall, T), each with resources (feed and hut) on the same (S) or opposite side (O) of the tree zone. The poplar leaf area index derived from canopy cover using a computer vision approach on true-colour UAV imagery was fed to a process-based model alongside soil data and geostatistical analyses to derive the soil water balance across the paddocks and explicitly map the variation in soil nitrate leaching. The results showed clear patterns not seen before of nitrate leaching hotspots shifting from high values in the pre-study year without animals to diluted lower values in the main study year involving the pigs. The results also showed a seasonal and spatial variation of 7 to 860 kg N ha⁻¹ year⁻¹, a wide leaching range otherwise difficult to capture, by employing only a process-based model using mean effective parameters. Nitrate leaching was in the order PO > PS > TO > TS. The N cycle was tightened with T regardless of S/O. The approach could be improved with more machine learning-aided process-based modelling to operationally monitor complex silvopastoral systems to alleviate nitrate leaching in outdoor pig systems. Full article

To improve the performance of particle swarm optimization in solving large-scale problems, a High-Performance Learning Particle Swarm Optimization (HPLPSO) based on the knowledge of individuals is proposed. In HPLPSO, two strategies are designed to balance global exploration and local exploitation according to the principle of symmetry, which emphasizes balance and consistency during the optimization process. A strategy for elite individuals to guide population updates is proposed to reduce the impact of local optimal positions. Meanwhile, a synchronous opposition-based learning strategy for multiple elite and poor individuals in the current iteration population is proposed to help individuals quickly jump out of the non-ideal search areas. Based on classical test functions for large-scale problems, HPLPSO performance is tested in 100, 200, 500 and 1000 dimensions. The results show that HPLPSO can converge to the theoretical optimal value in each of its 30 independent runs in 11 functions. Moreover, the values of mean variation from dimension 100 to 1000 present that HPLPSO is little affected by dimensional changes. The case application further validates the performance of the algorithm in solving practical problems. Therefore, the paper provides a method with high optimization performance to solve large-scale problems. Full article

This study presents a novel softsign-function-based fractional-order proportional–integral–derivative (softsign-FOPID) controller optimized using the fungal growth optimizer (FGO) for the stabilization and precise position control of an unstable magnetic ball suspension system. The proposed controller introduces a smooth nonlinear softsign function into the conventional FOPID structure to limit abrupt control actions and improve transient smoothness while preserving the flexibility of fractional dynamics. The FGO, a recently developed bio-inspired metaheuristic, is employed to tune the seven controller parameters by minimizing a composite objective function that simultaneously penalizes overshoot and tracking error. This optimization ensures balanced transient and steady-state performance with enhanced convergence reliability. The performance of the proposed approach was extensively benchmarked against four modern metaheuristic algorithms (greater cane rat algorithm, catch fish optimization algorithm, RIME algorithm and artificial hummingbird algorithm) under identical conditions. Statistical analyses, including boxplot comparisons and the nonparametric Wilcoxon rank-sum test, demonstrated that the FGO consistently achieved the lowest objective function value with superior convergence stability and significantly better (p < 0.05) performance across multiple independent runs. In time-domain evaluations, the FGO-tuned softsign-FOPID exhibited the fastest rise time (0.0089 s), shortest settling time (0.0163 s), lowest overshoot (4.13%), and negligible steady-state error (0.0015%), surpassing the best-reported controllers in the literature, including the sine cosine algorithm-tuned PID, logarithmic spiral opposition-based learning augmented hunger games search algorithm-tuned FOPID, and manta ray foraging optimization-tuned real PIDD². Robustness assessments under fluctuating reference trajectories, actuator saturation, sensor noise, external disturbances, and parametric uncertainties (±10% variation in resistance and inductance) further confirmed the controller’s adaptability and stability under practical non-idealities. The smooth nonlinearity of the softsign function effectively prevented control signal saturation, while the fractional-order dynamics enhanced disturbance rejection and memory-based adaptability. Overall, the proposed FGO-optimized softsign-FOPID controller establishes a new benchmark in nonlinear magnetic levitation control by integrating smooth nonlinear mapping, fractional calculus, and adaptive metaheuristic optimization. Full article

Wireless sensor networks (WSNs) represent an emerging technology, among which coverage optimization remains a fundamental challenge. In specific application scenarios such as intelligent urban management, three-dimensional (3D) coverage models better reflect real-world requirements and thus hold greater research significance. To maximize the coverage performance of 3DWSNs, this study proposes a Three-Dimensional Confident Information Coverage (3DCIC) model based on the concept of multi-node cooperative information reconstruction, effectively extending the perceptual domain of sensor nodes. Furthermore, by incorporating adaptive dimension learning and opposition-based learning metchanisms into the wolf pack update strategy, we have developed the Hopping Adaptive Grey Wolf Optimizer (HAGWO) based on the GWO to optimize node deployment. Experimental results demonstrate the superior performance of the 3DCIC model, achieving coverage ranges 2.78 times, 4.41 times, and 4.00 times greater than those of conventional binary spherical models under regular tetrahedral, hexahedral, and octahedral node deployments, respectively. The proposed scheduling algorithm proves highly effective in both classical test functions and three-dimensional coverage problems. Full article

Accurate prediction of forage digestibility is essential for efficient livestock management and feed formulation. This study evaluated the performance of machine learning (ML) models to estimate the in vitro digestibility of leaf and stem components of Brachiaria hybrid cv. Ipyporã, using three datasets composed of pasture management variables, chemical composition variables, and their combination. Artificial neural network (Multilayer Perceptron, MLP), decision trees (REPTree and M5P), Random Forest (RF), and Multiple Linear Regression (LR) were tested. The principal component analysis revealed that 61.3% of the total variance was explained by two components, highlighting a strong association between digestibility and crude protein content and an opposite relationship with lignin and neutral detergent fiber. Among the evaluated models, MLP, LR, and RF achieved the best performance for leaf digestibility (r = 0.76), while for stem digestibility the highest accuracy was obtained with the LR model (r = 0.79; MAE = 2.42; RMAE = 2.87). The REPTree algorithm presented the lowest predictive performance regardless of the input data. The results indicate that chemical composition variables alone are sufficient to develop reliable prediction models. These findings demonstrate the potential of ML techniques as a non-destructive and cost-effective approach to predict the nutritional quality of tropical forage grasses and support precision livestock management. Full article

Accurate offshore wind speed forecasting is crucial for ensuring stable energy production and safe offshore operations. However, the strong nonlinearity, non-stationarity, and chaotic behavior of offshore wind speed series make precise prediction extremely difficult. To overcome these difficulties, a two-stage synergistic prediction framework is proposed. In the first stage, a multi-strategy Black-winged Kite Algorithm (MBKA) is designed, incorporating quantum population initialization, improved migration behavior, and oppositional–mutual learning to reinforce global optimization performance under complex coastal conditions. On this basis, an entropy-driven adaptive Variational Mode Decomposition (VMD) method is implemented, where MBKA optimizes decomposition parameters using envelope entropy as the objective function, thereby improving decomposition robustness and mitigating parameter sensitivity. In the second stage, the denoised intrinsic mode functions are used to train an adaptive Neuro-Fuzzy Inference System (ANFIS), whose membership function parameters are optimized by MBKA to enhance nonlinear modeling capability and prediction generalization. Finally, the proposed framework is evaluated using offshore wind speed data from two coastal regions in Shanghai and Fujian, China. Experimental comparisons with multiple state-of-the-art models demonstrate that the MBKA–VMD–ANFIS framework yields notable performance improvements, reducing RMSE by 57.14% and 30.68% for the Fujian and Shanghai datasets, respectively. These results confirm the effectiveness of the proposed method in delivering superior accuracy and robustness for offshore wind speed forecasting. Full article