Search Results (178)

To reduce charging time and improve operational efficiency at integrated energy stations (IESs) for electric vehicles (EVs), this paper develops a sustainability-oriented collaborative optimization model by coupling vehicle routing behavior with charging decision-making. Firstly, a dynamic road network model is established to simulate vehicle arrivals at IESs from different network nodes. Then, considering grid peak–valley electricity prices, station electricity procurement costs and EV charging demand, a dynamic pricing strategy for IESs is proposed to guide EVs to charge at off-peak hours so as to realize peak shaving and valley filling for the power grid. Meanwhile, the NSGA-III algorithm is improved through the introduction of Good Point Set initialization and an adaptive crossover mechanism, and the Good Point Set initialization and Adaptive Crossover NSGA-III (GPS-AC-NSGA-III) algorithm is proposed to solve the scheduling optimization problem. Finally, the CRITIC-based TOPSIS method is employed to identify the optimal compromise solution from the Pareto-optimal set. Case studies further prove the effectiveness of the proposed multi-objective collaborative optimization model for EVs and IESs. Compared with scenarios without dynamic Dijkstra-based navigation and dynamic pricing, the IES daily revenue increased by 39.83%, pollutant emissions decreased by 0.4%, and the peak-to-valley load difference ratio was reduced by 4.94%. The results indicate that dynamic Dijkstra-based vehicle routing improves travel efficiency, while the proposed dynamic pricing strategy enhances station profitability and smooths grid load fluctuations. Overall, the proposed framework contributes to sustainable transportation and energy systems by reducing pollutant emissions, improving energy efficiency, and enhancing the operational stability of integrated energy infrastructure, thereby supporting the transition toward low-carbon and sustainable urban energy systems. Full article

Low solution accuracy and efficiency are two bottleneck problems in the existing models and methodologies for spatial distance calculations to verify the minimal electrical clearance of overhead transmission lines if a dynamic windage yaw is considered. To address these two issues, the accurate numerical models and the corresponding efficient solution methodologies tailored for different scenarios are proposed. First, a conductor windage yaw surface model incorporating a horizontal specific load coefficient is established, transforming the wire-to-wire minimal distance determination into a multi-dimensional nonlinear constrained optimization problem. An improved gradient-guided crossover genetic algorithm (GGA) is subsequently developed to solve this optimization problem. By integrating the gradient information to guide the crossover operator and combining an adaptive mutation with a dimension mutation strategy, the solution efficiency is enhanced. For the wire-to-tower minimal distance determination, a simplified tower model and a hybrid optimization methodology combining an oriented octree with the GGA are proposed. Numerical results on typical case studies show that, for a wire-to-wire minimal distance calculation, the GGA outperforms both the basic genetic algorithm and particle swarm optimization in terms of both convergence speed and solution accuracy. For a wire-to-tower minimal distance calculation, the oriented octree improves the spatial utilization, and the proposed hybrid methodology substantially improves the computational performance. Full article

With the deepening implementation of the dual-carbon strategy, the penetration rates of distributed power sources and flexible loads in new distribution grids continue to rise, posing significant challenges to system security and stability due to output fluctuations and randomness. To enhance voltage quality and achieve low-carbon economic operation in distribution grids, this paper proposes a multi-objective optimization model for Distributed Energy Storage System allocation. The model integrates power quality, economic benefits, and net carbon emissions. To efficiently solve this high-dimensional nonlinear problem, an improved Multi-Objective Gray Wolf Optimization algorithm is proposed. It employs a chaotic map to initialize the population, enhancing global distribution uniformity. A nonlinear convergence factor is introduced to dynamically balance global exploration and local exploitation. A dynamic grouping collaboration strategy is designed, combining Lévy flight and the elite crossover strategy to enhance search capability and convergence accuracy. Simulations on an IEEE 33-node system show that the improved MOGWO-optimized energy storage scheme reduces average voltage deviation by 37.0%, total operating costs by 7.0%, and net carbon emissions by 4.1%, compared to a no-storage scenario. Compared to the standard MOGWO algorithm, the proposed method achieves further optimization across all objectives, validating its effectiveness and superiority in realizing coordinated energy storage planning that balances safety, economy, and low-carbon goals. Full article

To address the issues of low initial seed efficiency and a large number of ineffective mutations, this paper proposes an innovative fuzz testing seed optimization method combining neural networks and genetic algorithms. Traditional fuzz testing seed generation typically relies on random selection and the number of covered paths. In contrast, our method significantly improves seed generation efficiency and coverage by incorporating neural network models and genetic algorithms. First, the AFL tool is used to generate seed coverage path data, which is then used to train the neural network model. This model is employed to construct a fitness function to assess the potential of each seed. Subsequently, new seeds are generated through genetic algorithm crossover and mutation operations, with fitness evaluations based on the predictions of the neural network. Ultimately, the genetic algorithm optimizes the seeds through multiple generations, progressively improving coverage and vulnerability discovery capabilities. The experimental results demonstrate that the proposed method achieves significant improvements in fuzz testing performance, with path coverage increased by 28% compared to AFL and 23% compared to AFL++, and vulnerability discovery enhanced by over 200%. Full article

The scarcity of global wind field data limits the accuracy of numerical weather prediction. The currently operational spaceborne Doppler lidar (the European Space Agency’s Aeolus) measures only a single line-of-sight (LOS) wind component, which leads to discrepancies between the measured results and the real wind field. The systems of the United States and Japan have provided additional LOS wind measurements. Yet residual errors in correcting for the satellite’s own velocity can still degrade the accuracy of the retrieved wind vectors. To enhance the accuracy and timeliness of global wind observations, we propose a dual-spaceborne Doppler lidar wind measurement system. Two satellite orbits with different inclinations each provide a LOS wind; combining these components at each crossover yields the horizontal wind vector. Thereby, within 12 h, the crossovers blanket the globe, yielding a global horizontal wind-vector field. Orbital simulations show that inclinations summing to 180° produce the most uniform crossover-point distribution. As Satellite-1’s inclination (prograde orbit) increases, the latitudinal coverage of crossover points expands accordingly. The preferred configuration is when the two satellites have inclinations of 70° and 110°, respectively. Their ground tracks cover nearly all major global landmasses, with a symmetrical distribution of intersection points and a balanced grid resolution. As satellite technology further matures, this dual-spaceborne approach is expected to supplement global horizontal wind-field data. Full article

The Artificial Bee Colony (ABC) algorithm is a simple and effective population-based optimization method, but it may exhibit unstable convergence and weak exploitation capability in discrete and highly constrained problems. This study proposes an improved ABC framework that integrates a probabilistic Uniform crossover operator and a gene-level lock mechanism to enhance convergence stability and local refinement. The framework is applied to an integrated multi-resource allocation problem in liquid transportation, which has not previously been addressed within the ABC literature. The problem requires the simultaneous assignment of drivers, trucks, trailers, and ISO tanks under operational and regulatory constraints. Comparative analysis of different ABC configurations shows that integrating only Uniform crossover reduced the mean cost to 17.78, adding only the lock mechanism reduced it to 29.78, and combining both further decreased it to 14.94, indicating a complementary effect between the two mechanisms. The proposed configuration consistently achieved the lowest mean costs across small, medium, and large datasets. Compared with established metaheuristic algorithms and expert manual planning (34.72), the method produced lower-cost and feasible solutions, demonstrating both algorithmic robustness and practical relevance. Full article

Additive Manufacturing (AM) and printing-based fabrication technologies have emerged as powerful enablers for next-generation electronic integration and packaging, addressing the growing limitations of conventional subtractive manufacturing techniques. As electronic systems continue to scale toward higher operating frequencies (10–110 GHz and beyond) and increased functional density (>10⁴ interconnects/cm²), traditional packaging approaches struggle with rigid design constraints, complex processing steps (>15–25 fabrication steps), high tooling costs ($10,000–$100,000 for mask and molds) and limited compatibility with heterogeneous integration. In this review, a comprehensive and critical overview of major additive manufacturing and printing technologies including aerosol jet printing, inkjet printing, vat polymerization, fused filament fabrication (FFF) and nScrypt printing is presented from the perspective of electronic assembly and packaging. The fundamental working mechanisms, material compatibility, resolution limits, scalability, and reliability considerations of each technique are systematically discussed. From a manufacturing standpoint, AM reduces material waste by 50–90% compared to subtractive PCB processing and eliminates tooling costs, enabling low-volume prototyping with per-unit fabrication costs reduced by 30–70% for small batches (<100 units). Production throughput varies widely, from 1 to 20 cm²/min for high-resolution direct write systems to >100 cm²/min for scalable inkjet systems. Moreover, it is discussed how these technologies enable advanced packaging architectures such as printed signal crossovers, three-dimensional interconnects, ramps, and embedded chip assemblies. Recent research efforts and reported demonstrations are analyzed to highlight the advantages and current limitations of additive manufacturing for high-frequency, RF, and system-on-package (SoP) applications. Finally, future directions and remaining challenges are discussed, including advances in materials, custom and on-demand manufacturing, enhanced design freedom, integration of multifunctionality, cost-effectiveness, and smart packaging solutions. This review aims to serve as a reference for researchers and engineers seeking to leverage additive manufacturing for high-performance electronic integration and next-generation electronic packaging solutions. Full article

Multi-model ensembles and multi-objective evolutionary algorithms provide a systematic approach to reconciling competing criteria in time-series forecasting. However, most existing methods are tailored to specific tasks and lack essential mathematical details. This study introduces a general multi-objective ensemble framework based on a Multi-Objective Enhanced Crisscross Optimization (MOECSO) algorithm, exemplified through Brent crude oil price forecasting. Initially, ensemble-weight selection is framed as a bi-objective optimization problem, where the two objectives penalize Mean Absolute Error (MAE) and the Sample Standard Deviation of the Validation Residuals (SSDVRs), both assessed on the original United States Dollar (USD) scale under a leakage-free rolling-origin protocol. Subsequently, a Variational Mode Decomposition (VMD) reconstruction operator is defined, which adaptively reconstructs the raw series by integrating intrinsic mode functions with weights derived from their entropy and center-frequency characteristics, while adhering to nonnegativity and normalization constraints. Furthermore, horizontal and vertical crossover operators, along with a hypervolume–ideal-distance archive rule, are introduced, collectively forming a comprehensive MOECSO scheme for bi-objective ensemble weighting. Utilizing a public Brent crude oil dataset, the proposed ensemble demonstrates superior performance compared to robust statistical, machine-learning, and deep-learning benchmarks in terms of MAE, Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), while also reducing error dispersion and enhancing robustness during crisis periods. Diebold–Mariano (DM) and superior predictive ability tests with multiple-comparison control validate that these improvements are statistically significant. In summary, this paper presents a mathematically transparent framework for constructing and analyzing multi-objective ensembles in univariate time-series forecasting. Full article

When a fixed-wing Unmanned Aerial Vehicle (UAV) conducts All-Weather Post-Disaster Coverage Path Planning (PDCPP), the commonly used Sequential Path Coverage (SPC) method tends to generate redundant flight distance during turning transitions between adjacent coverage paths, which in turn increases the UAV’s flight energy consumption and thereby compromises the timeliness of rescue information acquisition. To address these challenges, this paper proposes a Multi-Selector Genetic Algorithm with Reinforcement Learning (MSGA-RL). It enhances population diversity through a distance-priority heuristic greedy initialization strategy, employs a multi-selector crossover operator to improve both solution diversity and convergence speed, and integrates a reinforcement learning-based individual retention mechanism with an elite pool protection strategy to prevent premature convergence. To simulate post-disaster scenarios, the disaster-affected area is modeled as a convex polygonal region with obstacles, while the flight energy consumption and stability of MSGA-RL are evaluated under different numbers of coverage paths. Simulation results indicate that, across all coverage path settings, MSGA-RL consistently achieves lower flight energy consumption than SPC, the Genetic Algorithm (GA), and the Dubins-based Enhanced Genetic Algorithm (DEGA), while exhibiting superior stability. In particular, in the convex quadrilateral scenario with 50 coverage paths, the flight energy consumption of MSGA-RL is reduced by 52.80%, 32.06%, and 15.96% compared with SPC, GA, and DEGA, respectively. Full article

To address the issue of traditional Particle Swarm Optimization (PSO) being prone to local optima and insufficient global search capability in sparse phased array optimization, a hybrid optimization algorithm integrating PSO with a Genetic Algorithm (GA) is proposed. Within the PSO framework, the proposed algorithm incorporates the adaptive crossover and mutation operations of the GA to enhance population diversity. It combines an adaptive weighting factor and a constriction factor to balance global exploration and local exploitation capabilities. Furthermore, a density-weighted method is employed to generate a high-quality initial population, thereby accelerating convergence. The proposed algorithm is applied to an 8 × 8 planar sparse array. On the E-plane (φ = 0°) and H-plane (φ = 90°), simulation results indicate that the achieved normalized maximum sidelobe level is −23.14 dB, which is significantly superior to those obtained by standalone PSO and GA. Based on these simulation results, microstrip patch antennas are introduced for array constitution and analysis. Full-wave electromagnetic simulation proves that the proposed sparse array has the ability of wide-angle scanning and low sidelobe. Our work demonstrates that the PSO-GA hybrid algorithm effectively enhances search capability and convergence performance, providing a reliable solution for sparse array design. Full article

Global optimization represents a fundamental challenge in computer science and engineering, as it aims to identify high-quality solutions to problems spanning from moderate to extremely high dimensionality. The Differential Evolution (DE) algorithm is a population-based algorithm like Genetic Algorithms (GAs) and uses similar operators such as crossover, mutation and selection. The proposed method introduces a set of methodological enhancements designed to increase both the robustness and the computational efficiency of the classical DE framework. Specifically, an adaptive termination criterion is incorporated, enabling early stopping based on statistical measures of convergence and population stagnation. Furthermore, a population sampling strategy based on k-means clustering is employed to enhance exploration and improve the redistribution of individuals in high-dimensional search spaces. This mechanism enables structured population renewal and effectively mitigates premature convergence. The enhanced algorithm was evaluated on standard large-scale numerical optimization benchmarks and compared with established global optimization methods. The experimental results indicate substantial improvements in convergence speed, scalability and solution stability. Full article

Non-stationary, multi-component vibration signals in rotating machinery are easily contaminated by strong background noise, which masks weak fault features and degrades diagnostic reliability. This paper proposes a joint denoising method that combines an improved cordyceps fungus optimization algorithm (ICFO), successive variational mode decomposition (SVMD), and an improved wavelet thresholding scheme. ICFO, enhanced by Chebyshev chaotic initialization, a longitudinal–transverse crossover fusion mutation operator, and a thinking innovation strategy, is used to adaptively optimize the SVMD penalty factor and number of modes. The optimized SVMD decomposes the noisy signal into intrinsic mode functions, which are classified into effective and noise-dominated components via the Pearson correlation coefficient. An improved wavelet threshold function, whose threshold is modulated by the sub-band signal-to-noise ratio, is then applied to the effective components, and the denoised signal is reconstructed. Simulation experiments on nonlinear, non-stationary signals with different noise levels (SNR = 1–20 dB) show that the proposed method consistently achieves the highest SNR and lowest RMSE compared to VMD, SVMD, VMD–WTD, CFO–SVMD, and WTD. Tests on CWRU bearing data and gearbox vibration signals with added −2 dB Gaussian white noise further confirm that the method yields the lowest residual variance ratio and highest signal energy ratio while preserving key fault characteristic frequencies. Full article

This paper addresses the optimization challenges in urban logistics with the aim of enhancing the sustainability of last-mile delivery. By focusing on the collaborative delivery between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), we propose a novel approach to reducing energy consumption and operational inefficiencies. A bilevel mixed-integer linear programming (Bilevel-MILP) model is developed, integrating road network topology with dynamic energy constraints. Departing from traditional single-delivery modes, the paper establishes a multi-task continuous delivery framework. By incorporating a dynamic charging point selection strategy and path–energy coupling constraints, the model effectively mitigates energy limitations and the issue of repeated returns for UAV charging in complex urban road networks, thereby promoting more efficient resource utilization. At the algorithmic level, a Collaborative Delivery Path Optimization (CDPO) framework is proposed, which embeds an Improved Sparrow Search Algorithm (ISSA) with directional initialization and a Hybrid Genetic Algorithm (HGA) with specialized crossover strategies. This enables the synergistic optimization of UAV delivery sequences and UGV charging decisions. The simulation results demonstrate that, in scenarios with a task density of 20 per 100 km², the proposed CDPO algorithm reduces the total delivery time by 33.9% and shortens the UAV flight distance by 24.3%, compared to conventional fixed charging strategies (FCSs). These improvements directly contribute to lowering energy consumption and potential emissions. The road network discretization approach and dynamic candidate charging point generation confirm the method’s adaptability in high-density urban environments, offering a spatiotemporal collaborative optimization paradigm that supports the development of sustainable and intelligent urban logistics systems. The obtained results provide practical insights for the design and deployment of efficient UAV–UGV collaborative logistics systems in urban environments, particularly under high-task-density and energy-constrained conditions. Full article

This paper develops a new conceptual and operational understanding of cultural heritage transformation, interpreting it as a systemic and dynamic process rather than a static state. It explores the realities and opportunities for action when cultural heritage is understood and managed as a complex, adaptive system. The study builds on a critical review of contemporary literature to identify the multi-scalar challenges currently facing urban heritage systems, such as climate change, disaster risks, social fragmentation, and unsustainable urban development. To respond to these challenges, the paper introduces a metamodel for heritage-based urban transformation, designed to apply systems thinking to heritage management that was developed based on cases from the Western European context. This metamodel integrates key variables—actors, resources, tools, and processes—and is used to test the hypothesis that a systems-oriented approach to cultural heritage can enhance the capacity of stakeholders to connect, adapt, use, and safeguard heritage in the face of complex urban transitions. The hypothesis is operationalized through scenario-based applications in the fields of disaster risk management (DRM), circular economy, and broader sustainability transitions, demonstrating how the metamodel supports the design of cross-over resilience strategies. These strategies not only preserve heritage but activate it as a resource for innovation, cohesion, identity, and adaptive reuse. Thus, cultural heritage is reframed as a strategic investment—generating spillover benefits such as improved quality of life, economic opportunities, environmental mitigation, and enhanced social capital. In light of the transition toward a greener and more resilient society, this paper argues for embracing heritage as a driver of transformation—capable of engaging with well-being, behavior change, innovation, and education through cultural crossovers. Heritage is thus positioned not merely as something to be protected, but as a catalyst for systemic change and future-oriented urban regeneration. Full article

The rapid growth of electric vehicle (EV) adoption poses significant challenges for the rational planning of charging infrastructure, where economic efficiency and service quality are inherently conflicting. To support scientific decision-making in charging station siting, this study proposes an integrated multi-objective optimization and decision-support framework that combines an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with an entropy-weighted TOPSIS method. A bi-objective siting model is developed to simultaneously minimize total operator costs and maximize user satisfaction. User satisfaction is explicitly characterized by a nonlinear charging distance perception function and a queuing-theoretic waiting time model, enabling a more realistic representation of user service experience. To enhance convergence performance and solution diversity, the NSGA-II algorithm is improved through variable-wise random chaotic initialization, opposition-based learning, and adaptive crossover and mutation operators. The resulting Pareto-optimal solutions are further evaluated using an improved entropy-weighted TOPSIS approach to objectively identify representative compromise solutions. Simulation results demonstrate that the proposed framework achieves superior performance compared with the standard NSGA-II algorithm in terms of operating cost reduction, user satisfaction improvement, and multi-objective indicators, including hypervolume, inverted generational distance, and solution diversity. The findings confirm that the proposed NSGA-II–TOPSIS framework provides an effective, robust, and interpretable decision-support tool for EV charging station planning under conflicting objectives. Full article