Search Results (133)

Accurate prediction of concrete compressive strength is essential for ensuring structural safety in civil engineering, particularly in road and bridge construction, where inadequate strength can lead to deformation, cracking, or collapse. Traditional non-destructive testing (NDT) methods, such as the Rebound Hammer Test, estimate strength using regression-based formulas fitted with measurement data; however, these formulas, typically optimized via the least squares method, are highly sensitive to initial parameter settings and exhibit low robustness, especially for nonlinear relationships. Meanwhile, AI-based models, such as neural networks, require extensive datasets for training, which poses a significant challenge in real-world engineering scenarios with limited or unevenly distributed data. To address these issues, this study proposes a gradient-boosting artificial bee colony (GB-ABC) algorithm for robust regression curve fitting. The method integrates two novel mechanisms: gradient descent to accelerate convergence and prevent entrapment in local optima, and a point density-weighted strategy using Gaussian Kernel Density Estimation (GKDE) to assign higher weights to sparse data regions, enhancing adaptability to field data irregularities without necessitating large datasets. Following data preprocessing with Local Outlier Factor (LOF) to remove outliers, validation on 600 real-world samples demonstrates that GB-ABC outperforms conventional methods by minimizing mean relative error rate (RER) and achieving precise rebound-strength correlations. These advancements establish GB-ABC as a practical, data-efficient solution for on-site concrete strength estimation. Full article

This paper provides a comprehensive overview of the application of fractional calculus in modern optimization methods, with a focus on its impact in artificial intelligence (AI) and computational science. We examine how fractional-order derivatives have been integrated into traditional methodologies, including gradient descent, least mean squares algorithms, particle swarm optimization, and evolutionary methods. These modifications leverage the intrinsic memory and nonlocal features of fractional operators to enhance convergence, increase resilience in high-dimensional and non-linear environments, and achieve a better trade-off between exploration and exploitation. A systematic and chronological analysis of algorithmic developments from 2017 to 2025 is presented, together with representative pseudocode formulations and application cases spanning neural networks, adaptive filtering, control, and computer vision. Special attention is given to advances in variable- and adaptive-order formulations, hybrid models, and distributed optimization frameworks, which highlight the versatility of fractional-order methods in addressing complex optimization challenges in AI-driven and computational settings. Despite these benefits, persistent issues remain regarding computational overhead, parameter selection, and rigorous convergence analysis. This review aims to establish both a conceptual foundation and a practical reference for researchers seeking to apply fractional calculus in the development of next-generation optimization algorithms. Full article

Aiming at the problems of traditional calibrated visual servo systems relying on precise model calibration and the high training cost and low efficiency of online reinforcement learning, this paper proposes a Multi-Network Mean Delayed Deep Deterministic Policy Gradient Algorithm with Behavior Cloning (MN-MD3+BC) for uncalibrated visual adaptive control of robotic arms. The algorithm improves upon the Twin Delayed Deep Deterministic Policy Gradient (TD3) network framework by adopting an architecture with one actor network and three critic networks, along with corresponding target networks. By constructing a multi-critic network integration mechanism, the mean output of the networks is used as the final Q-value estimate, effectively reducing the estimation bias of a single critic network. Meanwhile, a behavior cloning regularization term is introduced to address the common distribution shift problem in offline reinforcement learning. Furthermore, to obtain a high-quality dataset, an innovative data recombination-driven dataset creation method is proposed, which reduces training costs and avoids the risks of real-world exploration. The trained policy network is embedded into the actual system as an adaptive controller, driving the robotic arm to gradually approach the target position through closed-loop control. The algorithm is applied to uncalibrated multi-degree-of-freedom robotic arm visual servo tasks, providing an adaptive and low-dependency solution for dynamic and complex scenarios. MATLAB simulations and experiments on the WPR1 platform demonstrate that, compared to traditional Jacobian matrix-based model-free methods, the proposed approach exhibits advantages in tracking accuracy, error convergence speed, and system stability. Full article

This study explores the convergence in greenhouse gas emissions (GHGs) and its determinants across 38 OECD countries during the period 1996–2022, employing the novel approach which combined club convergence method with supervised machine learning algorithm Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) method. The findings reveal the presence of three distinct convergence clubs shaped by structural economic and institutional characteristics. Club 1 exhibits low energy efficiency, high fossil fuel dependence, and weak governance structures; Club 2 features strong institutional quality, advanced human capital, and effective environmental taxation; and Club 3 displays heterogeneous energy profiles but converges through socio-economic foundations. While traditional growth-related drivers such as technological innovation, foreign direct investments, and GDP growth play a limited role in explaining emission convergence, energy structures, institutional and policy-related factors emerge as key determinants. These findings highlight the limitations of one-size-fits-all climate policy frameworks and call for a more nuanced, club-specific approach to emission mitigation strategies. By combining convergence theory with interpretable machine learning, this study contributes a novel empirical framework to assess the differentiated effectiveness of environmental policies across heterogeneous country groups, offering actionable insights for international climate governance and targeted policy design. Full article

This paper addresses the challenge of segmenting consumer behavior in contexts characterized by both numerical regularities and semantic variability. Traditional models, such as RFM-based segmentation, capture the transactional dimension but neglect the implicit meanings expressed through product descriptions, reviews, and linguistic diversity. To overcome this gap, we propose a hybrid clustering algorithm that integrates numerical and semantic distances within a unified scalar framework. The central element is a scalar objective function that combines Euclidean distance in the RFM space with cosine dissimilarity in the semantic embedding space. A continuous parameter λ regulates the relative influence of each component, allowing the model to adapt granularity and balance interpretability across heterogeneous data. Optimization is performed through a dual strategy: gradient descent ensures convergence in the numerical subspace, while genetic operators enable a broader exploration of semantic structures. This combination supports both computational stability and semantic coherence. The method is validated on a large-scale multilingual dataset of transactional records, covering five culturally distinct markets. Results indicate systematic improvements over classical approaches, with higher Silhouette scores, lower Davies–Bouldin values, and stronger intra-cluster semantic consistency. Beyond numerical performance, the proposed framework produces intelligible and culturally adaptable clusters, confirming its relevance for personalized decision-making. The contribution lies in advancing a scalarized formulation and hybrid optimization strategy with wide applicability in scenarios where numerical and textual signals must be analyzed jointly. Full article

The stratospheric airship is a vital platform in near-space applications, and achieving autonomous transfer has become a key research focus to meet the demands of diverse mission scenarios. The core challenge lies in planning feasible and efficient paths, which is difficult for traditional algorithms due to the time-varying environment and the highly coupled multi-system dynamics of the airship. This study proposes a deep reinforcement learning algorithm, termed reward-prioritized Long Short-Term Memory Twin Delayed Deep Deterministic Policy Gradient (RPL-TD3). The method incorporates an LSTM network to effectively capture the influence of historical states on current decision-making, thereby improving performance in tasks with strong temporal dependencies. Furthermore, to address the slow convergence commonly seen in off-policy methods, a reward-prioritized experience replay mechanism is introduced. This mechanism stores and replays experiences in the form of sequential data chains, labels them with sequence-level rewards, and prioritizes high-value experiences during training to accelerate convergence. Comparative experiments with other algorithms indicate that, under the same computational resources, RPL-TD3 improves convergence speed by 62.5% compared to the baseline algorithm without the reward-prioritized experience replay mechanism. In both simulation and generalization experiments, the proposed method is capable of planning feasible paths under kinematic and energy constraints. Compared with peer algorithms, it achieves the shortest flight time while maintaining a relatively high level of average residual energy. Full article

This paper introduces novel inverse optimization algorithms (RC and DC) for neural network training in stock price forecasting in an attempt to overcome the traditional gradient descent limitation of local minima convergence. The key novelty is a stochastic algorithm for inverse problems adapted to neural network training, where target function values decrease iteratively through selective weight modification. Experimental analysis used closing price data from 40 Russian companies, comparing traditional activation functions (linear, sigmoid, tanh) with specialized functions (sincos, cloglogm, mish) across perceptrons and single-hidden-layer networks. Key findings show the superiority of the DC method for single-layer networks, while RC proves most effective for hidden-layer networks. The linear activation function with the RC algorithm delivered optimal results in most experiments, challenging conventional nonlinear activation preferences. The optimal architecture, namely, a single hidden layer with two neurons, achieved the best prediction accuracy in 70% of cases. The research confirms that inverse optimization algorithms can provide higher training efficiency than classical gradient methods, offering practical improvements for financial forecasting. Full article

We propose a variant of Bayesian optimization in which probability distributions are constructed using uncertainty quantification (UQ) techniques. In this context, UQ techniques rely on a Hilbert basis expansion to infer probability distributions from limited experimental data. These distributions act as prior knowledge of the search space and are incorporated into the acquisition function to guide the selection of enrichment points more effectively. Several variants of the method are examined, depending on the distribution type (normal, log-normal, etc.), and benchmarked against traditional Bayesian optimization on test functions. The results show competitive performance, with selective improvements depending on the problem structure, and faster convergence in specific cases. As a practical application, we address a structural shape optimization problem. The initial geometry is an L-shaped plate, where the goal is to minimize the volume under a horizontal displacement constraint expressed as a penalty. Our approach first identifies a promising region while efficiently training the surrogate model. A subsequent gradient-based optimization step then refines the design using the trained surrogate, achieving a volume reduction of more than $30\%$ while satisfying the displacement constraint, without requiring any additional evaluations of the objective function. Full article

As network environments become increasingly dynamic and users’ Quality of Service (QoS) demands grow more diverse, efficient and adaptive routing strategies are urgently needed. However, traditional routing strategies suffer from limitations such as poor adaptability to fluctuating traffic, lack of differentiated service handling, and slow convergence in complex network scenarios. To this end, we propose a routing strategy based on multi-agent deep deterministic policy gradient for differentiated QoS services (RS-MADDPG) in a software-defined networking (SDN) environment. First, network state information is collected in real time and transmitted to the control layer for processing. Then, the processed information is forwarded to the intelligent layer. In this layer, multiple agents cooperate during training to learn routing policies that adapt to dynamic network conditions. Finally, the learned policies enable agents to perform adaptive routing decisions that explicitly address differentiated QoS requirements by incorporating a custom reward structure that dynamically balances throughput, delay, and packet loss according to traffic type. Simulation results demonstrate that RS-MADDPG achieves convergence approximately 30 training cycles earlier than baseline methods, while improving average throughput by 3%, reducing latency by 7%, and lowering packet loss rate by 2%. Full article

The energy management strategy of Hybrid Electric Vehicles (HEVs) plays a key role in improving fuel economy and reducing battery energy consumption. This paper proposes a Deep Reinforcement Learning-based energy management strategy optimized by the experience pool (P-HER-DDPG), aimed at improving the fuel efficiency of HEVs while accelerating the training speed. The method integrates the mechanisms of Prioritized Experience Replay (PER) and Hindsight Experience Replay (HER) to address the reward sparsity and slow convergence issues faced by the traditional Deep Deterministic Policy Gradient (DDPG) algorithm when handling continuous action spaces. Under various standard driving cycles, the P-HER-DDPG strategy outperforms the traditional DDPG strategy, achieving an average fuel economy improvement of 5.85%, with a maximum increase of 8.69%. Compared to the DQN strategy, it achieves an average improvement of 12.84%. In terms of training convergence, the P-HER-DDPG strategy converges in 140 episodes, 17.65% faster than DDPG and 24.32% faster than DQN. Additionally, the strategy demonstrates more stable State of Charge (SOC) control, effectively mitigating the risks of battery overcharging and deep discharging. Simulation results show that P-HER-DDPG can enhance fuel economy and training efficiency, offering an extended solution in the field of energy management strategies. Full article

This paper is concerned with the problem of a full formal dynamic linearized model-free adaptive control scheme based on pattern class variable (P-FFDL-MFAC) for a class of unknown non-affine nonlinear discrete-time systems. The concept of pattern class variable is defined as dynamic operating variables rather than state variables or output variables. The pattern classes is utilized as the system output conditions, and the purpose of the control is to ensure that the system output belongs to a certain pattern class or some desired pattern classes. The scheme of P-FFDL-MFAC mainly consists of an improved tracking control law, a bias estimation algorithm, and a pseudo-gradient vector estimation algorithm. Furthermore, based on the contraction mapping theorem, the bounded convergence of tracking error has been proved. Finally, numerical examples and the actual sintering process data are used, respectively, to verify the effectiveness of the proposed design techniques and are compared with the traditional MFAC method. The results are better than the traditional method. Full article

In complex urban wireless environments, unmanned aerial vehicle–mobile edge computing (UAV-MEC) systems face challenges like link blockage and single-antenna eavesdropping threats. The traditional single reconfigurable intelligent surface (RIS), limited in collaboration, struggles to address these issues. This paper proposes a double-RIS cooperative UAV-MEC optimization scheme, leveraging their joint reflection to build multi-dimensional signal paths, boosting legitimate link gains while suppressing eavesdropping channels. It considers double-RIS phase shifts, ground user (GU) transmission power, UAV trajectories, resource allocation, and receiving beamforming, aiming to maximize secure energy efficiency (EE) while ensuring long-term stability of GU and UAV task queues. Given random task arrivals and high-dimensional variable coupling, a dynamic model integrating queue stability and secure transmission constraints is built using Lyapunov optimization, transforming long-term stochastic optimization into slot-by-slot deterministic decisions via the drift-plus-penalty method. To handle high-dimensional continuous spaces, an end-to-end proximal policy optimization (PPO) framework is designed for online learning of multi-dimensional resource allocation and direct acquisition of joint optimization strategies. Simulation results show that compared with benchmark schemes (e.g., single RIS, non-cooperative double RIS) and reinforcement learning algorithms (e.g., advantage actor–critic (A2C), deep deterministic policy gradient (DDPG), deep Q-network (DQN)), the proposed scheme achieves significant improvements in secure EE and queue stability, with faster convergence and better optimization effects, fully verifying its superiority and robustness in complex scenarios. Full article

The efficient utilization of structural information in High-Range Resolution Profiles (HRRPs) is of great significance for improving recognition performance. This paper proposes a size estimation method based on L1-norm variable fractional-order gradient descent, which achieves size inversion in complex electromagnetic environments by establishing an HRRP projection model of ballistic targets. Specifically: First, through rigorous geometrical optics analysis, an analytical relationship model between the target’s projected size and actual size is established. Second, an error function under the L1-norm is constructed, and an adaptive order-adjusting fractional-order gradient descent method is employed for optimization, effectively overcoming the sensitivity to outliers inherent in traditional L2-norm methods. Finally, by introducing a dynamic order-switching mechanism, computational efficiency is improved while ensuring convergence accuracy. Experimental results show that at a measurement error of 0.4 m, the proposed method maintains excellent estimation performance with sensitivity to outliers reduced, and the actual size inversion error remains stable below 3.7%. Full article

Optimization of injection molding process parameters is essential for improving product quality and production efficiency. Traditional methods, which rely heavily on operator experience, often result in inconsistencies, high time consumption, high defect rates, and suboptimal energy consumption. In this study, an improved particle swarm optimization (IPSO) algorithm was proposed, integrating dynamic inertia weight adjustment, adaptive acceleration coefficients, and position constraints to address the issue of premature convergence and enhance global search capabilities. A dual-model architecture was implemented: a constraint validation mechanism based on support vector machine (SVM) was enforced per iteration cycle to ensure stepwise quality compliance, while a fitness function derived by extreme gradient boosting (XGBoost) was formulated to minimize cycle time as the optimization objective. The results demonstrated that the average injection cycle time was reduced by 9.41% while ensuring that the product was qualified. The SVM and XGBoost models achieved high performance metrics (accuracy: 0.92; R²: 0.93; RMSE: 1.05), confirming their robustness in quality classification and cycle time prediction. This method provides a systematic and data-driven solution for multi-objective optimization in injection molding, significantly improving production efficiency and energy utilization. Full article

In multi-user wireless communication scenarios, signal degradation caused by channel fading and co-channel interference restricts system capacity, while traditional enhancement schemes face challenges of high coordination complexity and hardware integration. This paper proposes an electromagnetic focusing method using a single-layer transmissive passive metasurface. A high-efficiency metasurface array is fabricated based on PCB technology, which utilizes subwavelength units for wide-range phase modulation to construct a multi-user energy convergence model in the WiFi band. By optimizing phase gradients through the geometric phase principle, the metasurface achieves collaborative wavefront manipulation for multiple target regions with high transmission efficiency, reducing system complexity compared to traditional multi-layer structures. Measurements in a microwave anechoic chamber and tests in an office environment demonstrate that the metasurface can simultaneously create signal enhancement zones for multiple users, featuring stable focusing capability and environmental adaptability. This lightweight design facilitates deployment in dense networks, providing an effective solution for signal optimization in indoor distributed systems and IoT communications. Full article