Search Results (166)

To improve path planning performance for mobile robots in complex environments, this study proposes a hybrid method combining an improved jump point search (JPS) algorithm with the dynamic window approach (DWA). In global planning, a quadrant pruning strategy guided by the target direction and a sine-enhanced heuristic function reduces the search space and accelerates planning. Natural jump points are retained for path continuity, and the path is smoothed using cubic B-spline curves. In local planning, DWA is enhanced by incorporating a target orientation factor, a safety distance penalty, and a normalization mechanism into the cost function. An adaptive weighting strategy dynamically balances goal-directed motion and obstacle avoidance. Simulation experiments in static and complex environments with unknown and dynamic obstacles demonstrate the method’s effectiveness. Compared to the standard approach, the improved JPS reduces search time by 36.7% and node expansions by 60.9%, with similar path lengths. When integrated with DWA, the robot adapts effectively to changing obstacles, ensuring safe and efficient navigation. The proposed method significantly enhances the real-time performance and safety of path planning in dynamic and uncertain environments. Full article

Linear models are not always able to sufficiently capture the structure of a dataset. Sometimes, combining predictors in a non-parametric method, such as deep neural networks (DNNs), would yield a more flexible modeling of the response variables in the predictions. Furthermore, the standard statistical classification or regression approaches are inefficient when dealing with more complexity, such as a high-dimensional problem, which usually suffers from multicollinearity. For confronting these cases, penalized non-parametric methods are very useful. This paper proposes two heuristic approaches and implements new shrinkage penalized cost functions in the DNN, based on the elastic-net penalty function concept. In other words, some new methods via the development of shirnkaged penalized DNN, such as ${\mathrm{DNN}}_{\mathrm{elastic-net}}$ and ${\mathrm{DNN}}_{\mathrm{ridge}\&\mathrm{bridge}}$, are established, which are strong rivals for ${\mathrm{DNN}}_{\mathrm{Lasso}}$ and ${\mathrm{DNN}}_{\mathrm{ridge}}$. If there is any dataset grouping information in each layer of the DNN, it may be transferred using the derived penalized function of elastic-net; other penalized DNNs cannot provide this functionality. Regarding the outcomes in the tables, in the developed DNN, not only are there slight increases in the classification results, but there are also nullifying processes of some nodes in addition to a shrinkage property simultaneously in the structure of each layer. A simulated dataset was generated with the binary response variables, and the classic and heuristic shrinkage penalized DNN models were generated and tested. For comparison purposes, the DNN models were also compared to the classification tree using GUIDE and applied to a real microbiome dataset. Full article

With the rise of the low-altitude urban traffic system, urban air mobility (UAM) has developed rapidly. As a critical component of the UAM system, the strategic layout of vertiports helps divert ground traffic pressure. To satisfy various demand patterns, different vertiport levels are needed, so we focus on the hierarchical vertiport location problem. Considering the capacity limitation, a median location model is established to minimize vertiport construction cost, passenger commuting cost, and penalty cost. For the nonlinear term in the objective function, the Big-M method is employed. Based on the reformulated model, we improve the branch-and-bound algorithm (LVBB) to solve it, where the Lagrange relaxation method is used to decompose the large-scale problem into parallel subproblems and compute the lower bound, and the variable neighborhood search algorithm is used to obtain the upper bound. Numerical experiments are performed in the 11 administrative districts of Nanjing, China. The results demonstrate that the proposed location scheme effectively balances vertiport construction cost and passenger commuting cost while satisfying capacity limitations. It also significantly reduces commuting time to improve passenger satisfaction. This scheme can offer strategic guidance for infrastructure planning in UAM. Full article

The all-weather imaging capability of synthetic aperture radar (SAR) confers unique advantages for maritime surveillance. However, ship detection under complex sea conditions still faces challenges, such as high-frequency noise interference and the limited computational power of edge computing platforms. To address these challenges, we propose a lightweight SAR small ship detection network, LWSARDet, which mitigates feature redundancy and reduces computational complexity in existing models. Specifically, based on the YOLOv5 framework, a dual strategy for the lightweight network is adopted as follows: On the one hand, to address the limited nonlinear representation ability of the original network, a global channel attention mechanism is embedded and a feature extraction module, GCCR-GhostNet, is constructed, which can effectively enhance the network’s feature extraction capability and high-frequency noise suppression, while reducing computational cost. On the other hand, to reduce feature dilution and computational redundancy in traditional detection heads when focusing on small targets, we replace conventional convolutions with simple linear transformations and design a lightweight detection head, LSD-Head. Furthermore, we propose a Position–Morphology Matching IoU loss function, P-MIoU, which integrates center distance constraints and morphological penalty mechanisms to more precisely capture the spatial and structural differences between predicted and ground truth bounding boxes. Extensive experiments conduct on the High-Resolution SAR Image Dataset (HRSID) and the SAR Ship Detection Dataset (SSDD) demonstrate that LWSARDet achieves superior overall performance compared to existing state-of-the-art (SOTA) methods. Full article

Driven by the rapid expansion of e-commerce in China, there is a growing demand for high-efficiency, sustainability-oriented logistics solutions in rural regions, particularly for the time-sensitive distribution of perishable agricultural commodities. Traditional logistics systems face considerable challenges in these geographically complex regions due to limited infrastructure and extended travel distances. To address these issues, this study proposes an intelligent cooperative delivery system that integrates automated drones with conventional trucks, aiming to enhance both operational efficiency and environmental sustainability. A mixed-integer linear programming (MILP) model is developed to account for the diverse terrain characteristics of rural China, including forest, lake, and mountain regions. To optimize distribution strategies, the model incorporates an improved Fuzzy C-Means (FCM) algorithm combined with a hybrid genetic simulated annealing algorithm. The performance of three transportation modes, namely truck-only, drone-only, and truck–drone integrated delivery, was evaluated and compared. Sustainability-related externalities, such as carbon emission costs and delivery delay penalties, are quantitatively integrated into the total transportation cost objective function. Simulation results indicate that the cooperative delivery model is especially effective in lake regions, significantly reducing overall costs while improving environmental performance and service quality. This research offers practical insights into the development of sustainable intelligent transportation systems tailored to the unique challenges of rural logistics. Full article

Path planning for autonomous vehicles is a core component of intelligent transportation systems, playing a key role in ensuring driving safety, improving driving efficiency, and optimizing the user experience. To address the challenges of safety, smoothness, and search efficiency in path planning for autonomous vehicles, this study proposes an improved hybrid A* algorithm based on the lemming optimization algorithm (LOA). Firstly, this study introduces a penalized graph search method, improves the distance heuristic function, and incorporates the Reeds–Shepp algorithm in order to overcome the insufficient safety and smoothness in path planning originating from the hybrid A* algorithm. The penalized graph search method guides the search away from dangerous areas through penalty terms in the cost function. Secondly, the distance heuristic function improves the distance function to reflect the actual distance, which makes the search target clearer and reduces the computational overhead. Finally, the Reeds–Shepp algorithm generates a path that meets the minimum turning radius requirement. By prioritizing paths with fewer reversals during movement, it effectively reduces the number of unnecessary reversals, thereby optimizing the quality of the path. Additionally, the lemming optimization algorithm (LOA) is combined with a two-layer nested optimization framework to dynamically adjust the key parameters of the hybrid A* algorithm (minimum turning radius, step length, and angle change penalty coefficient). Leveraging the LOA’s global search capabilities avoids local optima in the hybrid A* algorithm. By combining the improved hybrid A* algorithm with kinematic constraints within a local range, smooth paths that align with the actual movement capabilities are generated, ultimately enhancing the path search capabilities of the hybrid A* algorithm. Finally, simulation experiments are conducted in two scenarios to validate the algorithm’s feasibility. The simulation results demonstrate that the proposed method can efficiently avoid obstacles, and its performance is better than that of the traditional hybrid A* algorithm in terms of the computational time and average path length. In a simple scenario, the search time is shortened by 33.2% and the path length is reduced by 11.1%; at the same time, in a complex scenario, the search time is shortened by 23.5% and the path length is reduced by 13.6%. Full article

This study proposes an RBF-PSO hybrid framework for efficient inversion analysis of hydration heat parameters in mass concrete temperature fields, addressing the computational inefficiency and accuracy limitations of traditional methods. By integrating a Radial Basis Function (RBF) surrogate model with Particle Swarm Optimization (PSO), the method reduces reliance on costly finite element simulations while maintaining global search capabilities. Three objective functions—integral-type (F₁), feature-driven (F₂), and hybrid (F₃)—were systematically compared using experimental data from a C40 concrete specimen under controlled curing. The hybrid F₃, incorporating Dynamic Time Warping (DTW) for elastic time alignment and feature penalties for engineering-critical metrics, achieved superior performance with a 74% reduction in the prediction error (mean MAE = 1.0 °C) and <2% parameter identification errors, resolving the phase mismatches inherent in F₂ and avoiding F₁’s prohibitive computational costs (498 FEM calls). Comparative benchmarking against non-surrogate optimizers (PSO, CMA-ES) confirmed a 2.8–4.6× acceleration while maintaining accuracy. Sensitivity analysis identified the ultimate adiabatic temperature rise as the dominant parameter (78% variance contribution), followed by synergistic interactions between hydration rate parameters, and indirect coupling effects of boundary correction coefficients. These findings guided a phased optimization strategy, as follows: prioritizing high-precision calibration of dominant parameters while relaxing constraints on low-sensitivity variables, thereby balancing accuracy and computational efficiency. The framework establishes a closed-loop “monitoring-simulation-optimization” system, enabling real-time temperature prediction and dynamic curing strategy adjustments for heat stress mitigation. Robustness analysis under simulated sensor noise (σ ≤ 2.0 °C) validated operational reliability in field conditions. Validated through multi-sensor field data, this work advances computational intelligence applications in thermomechanical systems, offering a robust paradigm for parameter inversion in large-scale concrete structures and multi-physics coupling problems. Full article

In wireless sensor networks, evolutionary algorithms have emerged as pivotal tools for addressing complex localization challenges inherent in non-convex and nonlinear maximum likelihood estimation problems associated with received signal strength (RSS) measurements. While differential evolution (DE) has demonstrated notable efficacy in optimizing multimodal cost functions, conventional implementations often grapple with suboptimal convergence rates and susceptibility to local optima. To overcome these limitations, this paper proposes a novel enhancement of DE by integrating opposition-based learning (OBL) principles. The proposed method introduces an adaptive scaling factor that dynamically balances global exploration and local exploitation during the evolutionary process, coupled with a penalty-augmented cost function to effectively utilize boundary information while eliminating explicit constraint handling. Comparative evaluations against state-of-the-art techniques—including semidefinite programming, linear least squares, and simulated annealing—reveal significant improvements in both convergence speed and positioning precision. Experimental results under diverse noise conditions and network configurations further validate the robustness and superiority of the proposed approach, particularly in scenarios characterized by high environmental uncertainty or sparse anchor node deployments. Full article

This paper proposes a dynamic weight model predictive control (DWMPC) strategy for adaptive cruise control (ACC) in pure electric vehicles, aiming to enhance robustness, energy efficiency, and ride comfort under complex traffic conditions. Unlike conventional MPC with static weights, the proposed method integrates a fuzzy inference system that evaluates driving urgency based on real-time spacing and velocity errors. The resulting emergency coefficient is mapped through a nonlinear function to dynamically adjust the velocity tracking weight in the MPC cost function. Additionally, a four-mode coordination mechanism adaptively modifies acceleration and jerk penalties according to risk levels, enabling balanced responses between safety and comfort. A composite performance evaluation index (PEI) is formulated to quantitatively assess energy consumption, ride comfort, spacing accuracy, and emergency responsiveness. Simulation results under WLTC and typical urban driving scenarios demonstrate that DWMPC outperforms fixed-weight MPC and PI controllers, reducing energy consumption by 6.5%, jerk by 42.9%, and response time by 41.8% while improving coordination in speed tracking, inter-vehicle distance regulation, and energy-efficient control. Full article

The objective of this paper is to examine the game-theoretic relationship between local governments and coal mining enterprises with regard to the issue of coal mine intelligent construction. Firstly, this paper employs prospect theory to construct the value perception function and the decision weight function, which are then used to optimize the parameters of the traditional income matrix. The equilibrium point is then analyzed for stability under different conditions. Subsequently, Vensim PLE and MATLAB simulation software are employed to substantiate the impact of spillover penalties and associated parameters on the value perception equilibrium of the two parties. The results of the simulation demonstrate that, in addition to the initial strategy selected, the spillover penalty exerts a considerable inhibitory effect on the process of enterprise intelligence construction. Secondly, from the perspective of value perception, the lower the costs to enterprises of carrying out intelligent construction in terms of labor and mental effort, the more enterprises are inclined to engage in this construction. The higher the costs to enterprises of complying with strict government regulation, and the lower the costs to enterprises of deregulation, the more the government can govern by non-interference. Finally, the behavioral trends of local government departments are also correlated with additional revenue they receive from firms and the factor of fines linked to government performance. Full article

With the high-proportion integration of renewable energy into the power grid, the fast-response capabilities of demand-side flexible resources (DSFRs), such as electric vehicles (EVs) and thermostatic loads, have become critical for frequency stability. However, the diverse dynamic characteristics of heterogeneous resources lead to high modeling complexity. Traditional reinforcement learning methods, which rely on neural networks to approximate value functions, often suffer from training instability and lack the effective quantification of resource regulation costs. To address these challenges, this paper proposes a multi-agent reinforcement learning frequency control method based on a Consistency Model (CM). This model incorporates power, energy, and first-order inertia characteristics to uniformly characterize the response delays and dynamic behaviors of EVs and air conditioners (ACs), providing a reduced-order analytical foundation for large-scale coordinated control. On this basis, a policy gradient controller is designed. By using projected gradient descent, it ensures that control actions satisfy physical boundaries. A reward function including state deviation penalties and regulation costs is constructed, dynamically adjusting penalty factors according to resource states to achieve priority configuration for frequency regulation. Simulations on the IEEE 39-node system demonstrate that the proposed method significantly outperforms traditional approaches in terms of frequency deviation, algorithm training efficiency, and frequency regulation economy. Full article

Extrapolation of the asymptotic series with a cost functional imposed on iterated Borel summation is considered. The cost functionals are designed to determine the optimal control parameter, the role of which is performed by the number of iterations, which could be considered as fractional real or non-negative integers. New cost functional is inspired by a martingale with a penalty term written to penalize the solution to optimization with fractional number of iterations for deviations of the expected value of sought quantity from the results of a discrete iterated Borel summation. The optimization technique employed in the paper is unique since the penalty by itself is expressed through the sought quantity, such as critical amplitude dependent on the number of iterations. The solution to the extrapolation problem with the control parameter found by means of optimization with a new cost functional is accurate, robust and uniquely defined for a variety of extrapolation problems. Full article

Maritime carbon dioxide (CO₂) transport plays a pivotal role in facilitating carbon capture and storage (CCS) systems by connecting emission sources with appropriate storage sites. This process often incurs significant transportation costs, which must be carefully balanced against penalties for untransported CO₂ resulting from cost-driven decisions. This study addresses the CO₂ storage site location and transport assignment (CSSL-TA) problem, aiming to minimize total tactical costs, including storage site construction, ship chartering, transportation, and penalties for direct CO₂ emissions. We formulate the problem as a mixed-integer programming (MIP) model and demonstrate that the objective function exhibits submodularity, reflecting diminishing returns in facility investment and ship operations. A case study demonstrates the model’s effectiveness and practical value, revealing that optimal storage siting, strategic ship chartering, route allocation, and efficient transportation significantly reduce both transportation costs and emissions. To enhance practical applicability, a two-stage planning framework is proposed, where the first stage selects storage sites, and the second employs a genetic algorithm (GA) for transport assignment. The GA-based solution achieves a total cost only 2.4% higher than the exact MIP model while reducing computational time by 57.9%. This study provides a practical framework for maritime CO₂ transport planning, contributing to cost-effective and sustainable CCS deployment. Full article

This study establishes a novel mathematical framework for stochastic maintenance optimization in production systems by integrating Markov decision processes (MDPs) with convex programming theory. We develop a Z-transformation-based dual-space decomposition method to reconstruct MDPs into a solvable linear programming form, resolving the inherent instability of traditional models caused by uncertain initial conditions and non-stationary state transitions. The proposed approach introduces three mathematical innovations: (i) a spectral clustering mechanism that reduces state-space dimensionality while preserving Markovian properties, (ii) a Lagrangian dual formulation with adaptive penalty functions to handle operational constraints, and (iii) a warm start algorithm accelerating convergence in high-dimensional convex optimization. Theoretical analysis proves that the derived policy achieves stability in probabilistic transitions through martingale convergence arguments, demonstrating structural invariance to initial distributions. Experimental validations on production processes reveal that our model reduces long-term maintenance costs by 36.17% compared to Monte Carlo simulations (1500 vs. 2350 average cost) and improves computational efficiency by 14.29% over Q-learning methods. Sensitivity analyses confirm robustness across Weibull-distributed failure regimes (shape parameter $\beta \in$ [1.2, 4.8]) and varying resource constraints. Full article

This paper investigates the distributed time-varying (TV) resource management problem (RMP) for microgrids (MGs) within a multi-agent system (MAS) framework. A novel fixed-time (FXT) distributed optimization algorithm is proposed, capable of operating over switching communication graphs and handling both local inequality and global equality constraints. By incorporating a time-decaying penalty function, the algorithm achieves an FXT consensus on marginal costs and ensures asymptotic convergence to the optimal TV solution of the original RMP. Unlike the prior methods with centralized coordination, the proposed algorithm is fully distributed, scalable, and privacy-preserving, making it suitable for real-time deployment in dynamic MG environments. Rigorous theoretical analysis establishes FXT convergence under both identical and nonidentical Hessian conditions. Simulations on the IEEE 14-bus system validate the algorithm’s superior performance in convergence speed, plug-and-play adaptability, and robustness to switching topologies. Full article