Search Results (1,566)

Roof stability plays a crucial role in maintaining the overall stability of surrounding rocks to ensure safety of tunnel construction and operation. In this work, tension cut-off (TC) technique is introduced to modify the Hoek–Brown (HB) criterion to describe the tensile failure of rock strata. Thereafter, stability analysis of anchor bolt-reinforced tunnel roofs in rock strata subjected to a hybrid tensile-shear fracture is performed. The work balance equation is established by equating the external work rates of the falling block and the anchor bolts to the internal energy dissipation rate. Two stability indicators, that is the stability number (N) and the factor of safety (FoS) are proposed to quantitatively analyze the stability of tunnel roofs. Optimization algorithms combining genetic algorithm and particle swarm optimization are programmed to capture the optimal upper bound solutions. The influences of TC, strength criterion parameters, and anchor bolt-reinforcement strength on roof stability are explored in this work. It was found that increasing the anchor tension T improves the FoS of reinforced tunnel roofs, with an increase of up to 68% observed for rectangular tunnel roofs under the selected representative case, while the improvement is relatively less pronounced for circular tunnel roofs. Regarding anchor support, as ξ increases, the N for rectangular tunnels nearly doubles. This work provides a theoretical basis for preliminary designing of tunnels in reinforced rock strata. Full article

This study proposes an experimental data-driven hybrid prediction framework for hydraulic turbine performance using a Particle Swarm Optimization-enhanced Extreme Learning Machine (PSO-ELM). The performance of three hydraulic turbines, namely Pelton, Kaplan, and Francis turbines, was experimentally investigated under different jet-opening and guide-vane conditions. The experimental results showed that the Pelton turbine (PT) achieved its highest efficiency at low jet opening, whereas the Kaplan and Francis turbines performed better at higher guide-vane openings. The measured data includes 36 tests, which were then used to evolve and evaluate hybrid ML models for predicting hydraulic power and efficiency. Jet-opening or guide-vane position (25%, 50%, 75% and 100%) and rotational speed were used as input variables, while brake power and efficiency were used as output variables. The proposed PSO-ELM model was compared with other optimized ELM models, including Genetic Algorithms Extreme Learning Machine (GA-ELM), Differential Evolution Extreme Learning Machine (DE-ELM), and Whale Optimization Algorithm Extreme Learning Machine (WOA-ELM), as well as Particle Swarm Optimization–Adaptive Neuro-Fuzzy Inference System (PSO-ANFIS) and Particle Swarm Optimization–Multi-Layer Perceptron (PSO-MLP) models. The suggested method presents a hopeful structure for tackling the difficulties linked to performance evaluation, thus enabling a more dependable and effective use of energy resources. The main findings validate that a PSO-based structure reaching an R² value of 0.997 is more efficient in predictive tool performance optimization for hydropower systems. Full article

High-dimensional small-sample data are widely encountered in medical diagnosis, bioinformatics, and industrial inspection, where traditional feature selection methods often suffer from premature convergence and local optima. To address these issues, this paper proposes a Hybrid Breeding-based Co-evolutionary Ant Colony Optimization method (HBACO) for feature selection. Inspired by the principle of hybrid breeding, in which individuals with distinct traits produce superior offspring through cross recombination, inheritance of desirable genes and continuous evolution, the proposed algorithm establishes a three-population collaborative framework. It consists of an ACO-based search population, an HRO-based evolutionary population and a cooperative feedback population that evolve iteratively together. Furthermore, we devise a heuristic strategy integrating correlation and genetic characteristics to help mine high-value feature subsets. Meanwhile, a collaborative pheromone updating mechanism is adopted to realize efficient knowledge sharing among populations. Experiments conducted on 13 high-dimensional datasets, including Colon and Lung, demonstrate that HBACO achieves superior classification accuracy, feature reduction performance, and convergence behavior compared with 10 representative algorithms. Specifically, HBACO improves the average classification accuracy by 3.9% and achieves an average feature dimensionality reduction rate of 91.4%. Statistical tests further confirm the significance of the proposed method. The results indicate that HBACO provides an effective and robust solution for high-dimensional feature selection problems. Full article

Current urban logistics models often struggle to reconcile diurnal traffic dynamics with rigid spatial–temporal regulations. This decoupling causes “cascading infeasibility,” where traffic delays trigger structural regulatory violations and UAV energy depletion. This study formulates a time-dependent vehicle–UAV joint routing problem that strictly couples time-varying speeds with vehicle-restricted zones and no-fly zones. The mixed-integer program minimizes a composite cost by integrating speed curves, geometric detour models, and coupled energy functions. To solve large-scale instances, we propose a hybrid metaheuristic solver (IHGA-VNS-SL) combining genetic algorithms, variable neighborhood search, simulated annealing, and self-learning. Tested on calibrated Wuhan instances, IHGA-VNS-SL quantitatively outperforms baseline heuristics (GA and ALNS). It achieves a tight 2.31% optimality gap against exact solvers (CPLEX) and up to a 20% cost reduction over ALNS, alongside near-zero tardiness. Results demonstrate that this strict coupling effectively mitigates synchronization failures, confirming the framework’s robustness for megacity distribution. Full article

Structured Query Language injection (SQLi) remains a persistent threat to web applications due to the obfuscation, diversity, and evolving structure of malicious payloads, which limit the effectiveness of conventional rule and signature-based Web Application Firewalls (WAFs). Although prior studies have reported high detection performance using individual feature extraction methods or offline classification models, limited work has addressed deployment-oriented SQLi prevention through an integrated real-time inspection framework. This paper proposes a Machine Learning (ML)-based SQLi detection and prevention framework that combines hybrid feature representation, supervised dimensionality reduction, Genetic Algorithm (GA)-based hyperparameter optimization, and real-time WAF validation. Multiple public SQLi datasets were merged, cleaned, and deduplicated to improve exposure to diverse query patterns. SQL queries were encoded using Term Frequency–Inverse Document Frequency (TF-IDF), Word2Vec, and FastText features, which were fused and transformed through a Supervised Autoencoder into a compact discriminative representation. GA was then employed to optimize multiple classifiers, including Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), and Multi-Layer Perceptron (MLP). The MLP achieved the best overall performance, with an accuracy of 0.998681. The optimized model was deployed within a lightweight Flask-based WAF for real-time Hypertext Transfer Protocol (HTTP) request inspection and malicious input blocking. SQLMap v1.8.4-based robustness testing and runtime analysis demonstrate that the proposed framework provides effective SQLi prevention with practical deployment efficiency beyond conventional offline benchmark evaluation. Full article

The Flexible Job-shop Scheduling Problem (FJSP) plays an important role in production and processing in Smart Manufacturing Systems. Unlike the traditional Job-shop Scheduling Problem (JSP), the additional flexibility in machine selection enlarges the search space and increases scheduling difficulty, particularly for large-scale instances. Existing algorithms improve either convergence speed or solution quality, but maintaining both remains difficult as problem size grows. This paper presents a Hybrid Local Search Algorithm (HLS-FJSP), integrating Greedy Search, Genetic Algorithm, and Tabu Search into a two-phase optimization framework. Control parameters and a process monitoring mechanism are used to adjust the search behavior during different optimization stages. Computational experiments on benchmark instances show that the proposed method obtains competitive makespan results compared with several existing algorithms. The results also show stable improvement capability when used for further optimization of existing schedules. Full article

The intermittent nature of renewable energy systems and the mismatch between power generation and load demand necessitate the integration of efficient energy storage systems (ESSs). Among large-scale energy storage technologies, pumped hydro-energy storage systems (PHESs) are widely recognized as one of the most cost-effective and longest-lifetime storage solutions under favorable geographical conditions. This study proposes and optimizes a hybrid renewable energy system (HRES) for the Wadi Baish region in Saudi Arabia as a real case study, where the significant elevation difference between the nearby mountains and the existing lake provides favorable conditions for PHES implementation. A nested optimization framework is developed to determine the optimal sizing and operation of the HRES components. The external optimization loop employs the non-dominated sorting genetic algorithm II (NSGA-II) to optimize system sizing, while the internal optimization loop uses mixed-integer linear programming (MILP) to optimally dispatch the PHES, battery energy storage system (BESS), and hydrogen energy storage system (HESS). In addition, demand-side management (DSM) is coordinated with the MILP dispatch strategy to improve system performance and reliability. The results show that the optimized system can supply a 10 MW average load with a renewable energy penetration of 98.7%. The proposed configuration achieves a total lifecycle cost of USD 231.37 million and avoids approximately 898.58 kt of CO₂ emissions over the project lifetime. PHES operates as the primary bulk energy storage technology due to its high storage capacity and low degradation characteristics. Furthermore, the degradation-aware model predicts battery replacement every 12 years and HESS replacement every 5 years. Compared with rule-based control, the MILP-based dispatch strategy reduces grid dependency by 87%. The coordinated DSM and MILP operation also reduces the levelized cost of energy to USD 0.066/kWh while improving overall system reliability. These findings demonstrate the importance of coordinated energy management and accurate degradation modeling in the optimal design and operation of renewable-based HRES configurations. Full article

Maize hybrid seed production is a core factor in increasing maize yield. It is the key to ensure seed purity to remove tassels from female plants. This paper analyzes the inherent connections between seed production agronomy, biomechanics, computer vision, and intelligent devices at the system engineering level. The paper first elaborates on the role of crop growth models and genetic male sterility techniques in expanding the time window of mechanical operations. Secondly, based on the perception decision execution framework, this paper discusses how the biomechanical characteristics of male spikes directly determine the dynamic parameter design of the male removal actuator; in-depth analysis was conducted on the performance and limitations of deep learning algorithms in handling lighting changes, leaf occlusion, and high-throughput recognition in unstructured field environments. In addition, this paper compares the technical game between the detasseling success rate and leaf damage rate of two mainstream execution paths, cutting and extraction. This review highlights that future research should focus on the development of lightweight intelligent operation platforms and full-life-cycle digital decision-making systems, to realize high-efficiency and low-damage precision detasseling of seed maize. Full article

This study proposes a hybrid portfolio optimization framework integrating financial network analysis, Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and GA for asset allocation in BIST. The empirical analysis focuses on constituent firms within the BIST 30, BIST 50, and BIST 100 indices using daily stock market data covering the period 2000–2025. Financial network centrality indicators and technical analysis variables were employed to identify structurally influential assets and model nonlinear investment decision dynamics under market uncertainty. The ANFIS framework was utilized to capture complex relationships between technical indicators and portfolio allocation decisions, while Genetic Algorithms optimized portfolio weights under return maximization and downside-risk minimization constraints. To reduce overfitting risk, Principal Component Analysis (PCA) and K-fold cross-validation procedures were implemented during model training. The proposed framework was additionally evaluated using out-of-sample backtesting over the 2021–2024 period and compared against benchmark portfolio strategies, including Equal Weight and Minimum Variance portfolios. Empirical findings indicate that the ANFISGA framework achieved superior risk-adjusted performance, higher Sharpe and Sortino ratios, and lower maximum drawdown during volatile market conditions. The study contributes to the portfolio optimization literature by integrating financial network indicators with adaptive fuzzy decision systems and evolutionary optimization techniques within an emerging market context. The proposed framework is intended primarily as an adaptive portfolio decision-support system rather than a purely predictive forecasting model. Full article

Reinforced concrete (RC) slabs, as the core load-bearing components in construction engineering, are prone to internal damage induced by impact loads, and accurate positioning of impact locations is a key task in structural health monitoring. The proposed method was developed for typical RC slabs such as building floors, bridge decks, and road slabs. Traditional acoustic emission (AE) positioning methods suffer from low positioning accuracy and a tendency to fall into local optimum when applied to RC slabs, which is attributed to the material’s heterogeneity, the complex propagation characteristics of stress waves and ambient noise interference. In this study, a GA-PSO hybrid algorithm is proposed, which integrates the global search capability of the Genetic Algorithm (GA) with the superior local convergence performance of the Particle Swarm Optimization (PSO) algorithm. The premature convergence issue of the traditional PSO algorithm is alleviated by adopting strategies including tournament selection, α hybrid crossover, boundary-constrained mutation, and linearly decreasing inertia weight. Based on the Time Difference of Arrival (TDOA) principle, the root mean square error between the theoretical and measured time differences is taken as the fitness function, and a boundary penalty mechanism is incorporated to ensure the physical validity of positioning results. AE data were acquired through drop weight impact tests to verify the performance of the proposed algorithm. Compared with traditional TDOA grid search, pure GA, and pure PSO methods under the same conditions, the proposed GA-PSO algorithm achieves an average localization error of only 54.95 mm, which is 61.0% lower than that of pure GA, while reducing the error standard deviation from approximately 114 mm to 24.87 mm. The average positioning error for all impact sources on the RC slab is within 100 mm, with the error in the central area as low as 42.97 mm. These results demonstrate that the GA-PSO algorithm significantly outperforms existing methods in terms of accuracy, stability, and maximum error control, verifying its high potential for impact source localization in complex heterogeneous materials. Full article

With the rapid growth of edge computing applications, optimizing both edge server placement and task offloading decisions is critical for minimizing system latency in edge–cloud environments. However, these two problems are tightly coupled and jointly form a binary non-linear programming (BNLP) problem that is NP-hard. To address this challenge, this paper proposes a novel hybrid meta-heuristic algorithm with alternating iteration, which decouples the joint optimization into two interdependent subproblems: edge server placement and task offloading. These subproblems are solved alternately using particle swarm optimization (PSO) for placement and a genetic algorithm (GA) for offloading, respectively. PSO efficiently explores the discrete placement space under bound constraints, while GA effectively navigates the high-dimensional binary offloading space. Compact encoding schemes are designed to inherently satisfy problem constraints, reducing search overhead and improving convergence. The overall algorithm exhibits polynomial-time complexity, making it scalable for practical deployments. Extensive experiments comparing the proposed method against ten baseline algorithms demonstrate that it achieves the best latency with the smallest standard deviation. The results validate the effectiveness, robustness, and scalability of the proposed alternating iterative hybrid meta-heuristic approach for joint edge server placement and task offloading optimization. Full article

Background: In the era of digital transformation, public health systems increasingly rely on digital technologies to improve accessibility, efficiency, and patient outcomes. However, policymakers face significant challenges in allocating limited resources across competing digital health investments characterized by uncertainty and dynamic impacts. Methods: This study introduces the Adaptive Impact–Cost Optimization Theory (AICOT), a hybrid framework integrating fuzzy logic and genetic algorithms to optimize digital health investment portfolios. The model defines the Investment Priority Score (IPS) as a function of cost, expected impact, and implementation feasibility, enabling structured evaluation under uncertainty. A fuzzy inference system with centroid-based defuzzification is used to convert qualitative assessments into quantitative scores, while optimization techniques identify optimal portfolios across different fiscal scenarios. The empirical analysis covers 15 OECD countries (2018–2024) using publicly available datasets. Sensitivity analyses assess robustness under inflation, cost shocks, and changing system priorities. Results: The findings show that blended investment strategies combining routine digital health tools with pandemic-oriented infrastructures yield the highest resilience-adjusted efficiency. Results remain stable across sensitivity scenarios, with pandemic surveillance consistently ranking as a top priority even under increased cost conditions. The model effectively captures cross-country heterogeneity, demonstrating adaptability to different levels of digital maturity. Conclusions: AICOT provides a transparent and policy-relevant decision-support framework that improves resource allocation efficiency and reduces unnecessary expenditures. These contributions support long-term financial sustainability and align with global health objectives, including Universal Health Coverage and Sustainable Development Goal 3 (Good Health and Well-being). Full article

The recent emergence of conversational Artificial Intelligence (AI) agents has profoundly transformed learning and teaching practices in higher education. These tools offer multiple advantages, ranging from cognitive assistance to enhanced student autonomy and efficiency. However, their actual impact on academic performance remains understudied, and the existing research often presents contradictory findings. To address this gap, the present study is the first to employ a Genetic Algorithm (GA) and Multi-Layer Perceptron Neural Networks (MLPNNs) to evaluate the influence of Generative AI Tools (GAITs) on students’ academic outcomes. A structured questionnaire was administered to 294 students from three Moroccan engineering schools in order to collect data on their use of these tools. An initial attempt to predict their grades using a statistical approach showed that familiarity with GAITs contributed positively to academic performance but had limited accuracy (39%), highlighting the need for more robust methods. Therefore, a hybrid model based on neural networks optimized with a GA was developed to better capture the complex relationships between the explanatory variables and academic performance. The results indicate that the GAIT-related variables considered in this study, taken in isolation, have a limited predictive capacity for students’ academic outcomes. This finding suggests that the available data does not capture the full complexity of the factors shaping academic success in contexts involving GAITs use. Full article

Agricultural cold chain logistics is characterized by inherent challenges—product perishability, high carbon emissions, and stringent time windows—which are further exacerbated by dynamic disruptions. Existing methods suffer from slow adaptability, unstable multi-objective convergence, and severe cold-start issues. This work falls within the broad scope of biomimetics—the science of emulating nature’s time-tested strategies to solve complex engineering problems—and bio-inspired data-driven methods and their applications in engineering control, optimization, and artificial intelligence. The proposed H-MODRL framework embodies core biomimetic principles: the Genetic Algorithm (GA) mimics Darwinian natural selection and genetic inheritance, the Sparrow Search Algorithm (SSA) abstracts the cooperative foraging and anti-predation behaviors of sparrow populations in nature, and the Arrhenius-based freshness-decay model captures the biochemical kinetics governing perishable biological products. By synergistically integrating these biological evolution principles, swarm intelligence, and deep learning, the framework tackles real-world logistics complexity in a manner directly inspired by living systems. This study presents a well-organized hybrid optimization framework (H-MODRL) that couples a three-stage hybrid evolutionary mechanism, synergistically integrating heuristic warm-start, evolutionary policy guidance, and deep reinforcement learning decision-making. First, an improved genetic algorithm combined with the earliest deadline first strategy constructs a feasible initial population satisfying hard time-window constraints. Second, a large neighborhood search-enhanced chaotic sparrow search algorithm builds a high-quality elite guidance set for policy learning. Third, a physics-based multi-objective proximal policy optimization model embedded with Arrhenius equation-derived freshness-decay kinetics performs online decision-making. Experiments demonstrate that pre-computed all-pairs shortest paths and an O(1) hash-based dynamic-disruption indexing mechanism support fast online replanning. On heterogeneous simulated terrains based on real Chinese geospatial data, H-MODRL outperforms state-of-the-art algorithms across four objectives—logistics cost, carbon emissions, terminal freshness, and delivery time—while exhibiting compact, low-variance performance distributions, thereby validating its engineering robustness and practical value in complex agricultural cold chain environments. Full article

Temples are tourist attractions that represent the history and culture of Thailand, especially in Chiang Mai province—a city with a rich history that has become a prominent destination attracting visitors from around the world. Many temples remain undiscovered yet are ready for tourists to visit; however, due to unfamiliarity, tourists tend to visit only the well-known temples, as other visitors do, missing great opportunities to engage with new cultural heritage tourism experiences. To address this issue, we propose a Hybrid Three-Stage Route Planning Recommendation (HTS-RPR), a novel method for tourist route planning that delivers recommended routes based on tourists’ preferred constraints. This model contains three-stage route recommendations providing an optimal single-day route with mandatory and recommended points of interest (POIs) through a metaheuristic integrating Mixed Integer Programming (MIP), heuristic-based POI recommendation filtering, and Genetic Algorithm route optimization with Bayesian reward and peak-time awareness, ensuring that users can effectively travel cultural routes with high popularity and satisfaction while avoiding attractions during periods of high traffic. To validate the efficacy of the proposed model, experiments with three baseline methods were conducted. The results demonstrate that HTS-RPR achieves the best fitness score in 55 out of 60 scenarios and the best reward in 54 out of 60 scenarios, with a median fitness score 28.34% and 103.67% higher than the Genetic Algorithm and Multi-Start Simulated Annealing baselines, respectively, and a median total reward exceeding all three baselines by up to 40.74%. Although HTS-RPR’s median execution time is approximately 2.6 times that of the Genetic Algorithm, it remains 84.5% faster than the Multi-Start Simulated Annealing baseline, offering a favorable trade-off between solution quality and computational cost. Moreover, the framework’s pluggable reward function enables destination managers to configure recommendation priorities, including the promotion of undiscovered tourist attractions, while the peak-time-aware optimization mitigates congestion at specific POIs. Full article