Search Results (146)

This study establishes a rigorous theoretical framework for Particle Swarm Optimization (PSO) convergence by introducing a novel symmetry assumption governing the algorithm’s stochastic components and a monotonicity condition between function values and Euclidean distance to the global optimum. Under this assumption, we prove linear convergence in expectation and almost sure linear convergence for a modified PSO algorithm with symmetric zero-mean random coefficients when parameters satisfy the explicit condition $w+{\displaystyle \frac{8(c_1^2+c_2^2){\sigma }_r^2}{1-w}}<1$. This provides the first closed-form relationship between inertia weight w, learning factors $c_1,c_2$, and random variance ${\sigma }_r^2$ that guarantees convergence. Building on this theoretical foundation, we develop three hierarchical applications: (1) static parameter design that replaces empirical tuning with theoretical calculation from desired convergence rates; (2) symmetric random factor optimization that eliminates directional bias and stabilizes velocity dynamics while preserving exploration variance; and (3) dynamic adaptive strategies that adjust parameters in real-time based on particle dispersion feedback. By bridging the gap between empirical performance and theoretical guarantees, this work transforms PSO from an empirically driven heuristic into a provably convergent optimization tool with rigorous performance guarantees for objective functions satisfying strict monotonicity between fitness and distance to the optimum (e.g., strictly convex functions). Full article

To improve fine particle retention in cyclone clarifiers for mine water treatment, we developed three baffle-structured cyclone clarifiers based on the traditional design: flat-baffle cyclone clarifier, convex-baffle cyclone clarifier, and concave-baffle cyclone clarifier. Using numerical simulation, a comparative analysis was conducted on the differences in flow field characteristics and particle separation performance between the traditional cyclone clarifier and the three types of baffle-structured cyclone clarifiers. The convex-baffle cyclone clarifier showed the highest pressure drop. At Section II-II, low tangential velocity minimized internal swirl, while Section I-I exhibited high axial velocity near the wall. The low upward axial velocity in the central region of Section II-II enhanced fine particle settling. The convex baffle also promoted uniform streamlines and efficient space utilization. The concave-baffle cyclone clarifier exhibited a larger flow angle relative to the baffle than the flat-baffle cyclone clarifier, causing stronger impingement and turbulence that transported particles to the overflow outlet. In contrast, the convex-baffle cyclone clarifier’s smaller flow angle yielded weaker impingement and more stable flow, reducing particle escape. Simulations confirmed that baffle-structured cyclone clarifiers improve particle removal. The removal efficiency of the convex-baffle cyclone clarifier reaches 78.19%, representing a 5.22% improvement compared to the traditional cyclone clarifier. Furthermore, the convex-baffle cyclone clarifier demonstrated the most effective removal of 5 μm particles compared with both the flat-baffle and concave-baffle cyclone clarifier. Full article

Portfolio optimization is fundamental to modern finance, enabling investors to construct allocations that balance risk and return while satisfying practical constraints. When transaction costs and cardinality limits are incorporated, the problem becomes a computationally demanding mixed-integer quadratic program. This work demonstrates how principles from biomimetics—specifically, the computational strategies employed by biological neural systems—can inspire efficient algorithms for complex optimization problems. We demonstrate that this problem can be reformulated as a constrained quadratic program and solved using dynamics inspired by spiking neural networks. Building on recent theoretical work showing that leaky integrate-and-fire dynamics naturally implement projected gradient descent for convex optimization, we develop a solver that alternates between continuous gradient flow and discrete constraint projections. By mimicking the event-driven, energy-efficient computation observed in biological neurons, our approach offers a biomimetic pathway to solving computationally intensive financial optimization problems. We implement the approach in Python and evaluate it on portfolios of 5 to 50 assets using five years of market data, comparing solution quality against mixed-integer solvers (ECOS_BB), convex relaxations (OSQP), and particle swarm optimization. Experimental results demonstrate that the SNN solver achieves the highest expected return (0.261% daily) among all evaluated methods on the 50-asset portfolio, outperforming exact MIQP (0.225%) and PSO (0.092%), with runtimes ranging from 0.5 s for small portfolios to 8.4 s for high-quality schedules on large portfolios. While current Python runtimes are comparable to existing approaches, the key contribution is establishing a path to neuromorphic hardware deployment: specialized SNN processors could execute these dynamics orders of magnitude faster than conventional architectures, enabling real-time portfolio rebalancing at institutional scale. Full article

Energy management in smart microgrids is critical to achieving sustainable and efficient energy utilization. This study introduces a hybrid optimization framework combining neural networks (NNs) and multi-objective genetic algorithms (MOGAs) (hybrid NN-MOGA) to address the dual objectives of minimizing total energy cost and maximizing customer satisfaction. The hybrid NN-MOGA approach leverages NNs for predictive modeling of load and renewable energy generation, feeding accurate inputs to the MOGA for enhanced Pareto-optimal solutions. The performance of the proposed method is benchmarked against traditional optimization techniques, including MOGA, multi-objective particle swarm optimization (MOPSO), and the multi-objective firefly algorithm (MOFA). The simulation results demonstrate that hybrid NN-MOGA outperforms the alternative model. The proposed method produces uniformly distributed and highly convergent Pareto frontiers, ensuring robust trade-offs of USD 48.2817 and 81.7898 for total cost and customer satisfaction, respectively. Convexity analysis and the satisfaction of Karush–Kuhn–Tucker (KKT) conditions further validate the optimization model. Full article

Coarse aggregate shape plays a critical role in determining surface performance and durability in pavement systems. Traditional manual shape inspection is laborious and subjective, especially for bulk aggregates in overlapped state. In this work, we propose an automated digital image-based evaluation method for stacked coarse aggregates, combining preprocessing (grayscale conversion, histogram equalization, Gaussian filtering), segmentation, and contour reconstruction via the Graham scan convex hull algorithm. Morphological parameters such as equivalent ellipse major/minor axes, area, and perimeter are then extracted to compute individual particle shape factors. To assess batch-level quality, shape factor standard deviations (σ) and mean shape factors were computed from 50 aggregate images. Comparison with manual measurement results shows mean relative errors below 15%. Our analysis reveals a strong correlation between σ and overall shape quality: lower σ indicates more uniform geometry, while higher σ suggests greater irregularity. Based on experimental data, we define three σ-based categories: excellent (σ ≤ 0.32), good (0.32 < σ ≤ 0.42), and poor (σ > 0.42). This σ-driven evaluation framework enables rapid, quantitative, and objective assessment of aggregate morphology in practical aggregate production and pavement quality control. Full article

In the process of operating subsoiling implements on sloping red soil in Southwest China, the subsoiler tip faces significant challenges due to strong soil adhesion and severe compaction. By employing engineering bionics, integrating bionic geometric structures and surfaces, this study focuses on the subsoiler tip and designs four types of bionic geometric surface structures: bionic convex hull, bionic micro-spike convex hull, bionic scales, and bionic micro-spike scales. Finite element force analysis and discrete element simulation experiments reveal that bionic surfaces and geometric structures exhibit significant advantages in terms of total deformation, equivalent elastic strain, and stress. These structures are less prone to deformation and fracture under loads, demonstrating a stronger bearing capacity. A discrete element simulation analysis indicates interference phenomena among the subsoilers during multi-subsoiler operations. Based on bionic multi-subsoiler implements, optimized designs were developed through discrete element simulations and soil bin tests. The optimized bionic multi-subsoiler implement features a micro-spike convex hull surface, with micro-spike scale surfaces arranged equidistantly along the edge corners of the shovel face: six on each side wing and three in the middle. The optimal operating parameters were a subsoiling speed of 1.25 m/s, an entry angle of 23.917°, and an entry depth of 280.167 mm. The relative errors between the simulated and experimental values for the soil looseness and soil disturbance coefficients were 19.7% and 18.1%, respectively. The soil bin test results showed soil looseness and soil disturbance coefficients of 19.5% and 17.6%, respectively. At this point, the resistance reduction and wear resistance performance were optimal. This study proposes a bionic design approach for reducing resistance and enhancing wear resistance during the subsoiling process in the viscous red soil of Southwest China, providing a reference for the design and development of new equipment for working in this soil environment. This study is the first to implement a composite biomimetic surface—combining crayfish-like micro-spike convex hulls and sandfish-like micro-scale scales—on multi-shank subsoiler tips, and to validate it using FEA, DEM, and soil tank testing. Under an optimized configuration and operating conditions, the mean particle disturbance velocity increased from 1.52 m/s to 2.399 m/s (+57.8%), and the simulation/experiment relative errors for the soil loosening and disturbance coefficients were approximately 1.03% and 2.84%, respectively. These results demonstrate an engineering-acceptable trade-off between disturbance efficiency and wear resistance and indicate a clear potential for industrial application. Full article

With the increasing sophistication of electromagnetic environments in modern combat platforms, joint sensing and communication (JSAC) technology has emerged as a critical research frontier. Among these, JSAC waveform design plays a crucial role, as it enables the simultaneous achievement of both sensing and communication functions using the same transmit waveform. This paper presents a novel waveform design for a multi-input multi-output (MIMO) JSAC system. The proposed design leverages orthogonal frequency division multiplexing (OFDM) to reduce signal interference through low cross-correlation characteristics. Linear frequency modulation (LFM) is used as the carrier waveform, effectively narrowing the main lobe width of the autocorrelation function. We introduce phase perturbation into binary phase shift keying (BPSK) signals to enhance waveform performance, formulating the resulting problem as a high-dimensional, non-convex optimization challenge. To address this, we propose a hybrid optimization algorithm QGPV combining a quantum genetic algorithm (QGA), quantum particle swarm optimization (QPSO), and variable neighborhood search (VNS). The simulation results demonstrate that the proposed algorithm achieves superior performance compared with several typical methods. Notably, the peak sidelobe level (PSL) can be suppressed to around −21 dB with five iterations, highlighting the efficiency of the optimization process. These results validate the effectiveness of the proposed approach, showing improved waveform characteristics with an acceptable trade-off in communication performance. Full article

A covert communication scheme is designed for pinching antenna (PA)-enabled integrated sensing and communication (ISAC) systems. The base station (BS) emits sensing signals to detect the potential eavesdropper while opportunistically performing covert multicast transmissions. To enhance covertness, the inherent power uncertainty of the sensing signals is exploited to confuse eavesdroppers, thereby creating protective coverage for the legitimate transmission. For the considered systems, we design an alternating optimization framework to iteratively optimize the baseband, beamforming, and PA positionson the two waveguides, in which successive convex approximation and particle swarm optimization methods are introduced. Simulated results confirm that the proposed scheme achieves the highest covert communication rates with different numbers of multicast users compared to benchmark methods. Furthermore, increasing the transmit power and the number of PAs can further improve the covertness performance. Full article

Particle shape analysis is essential in sedimentological research, as it offers vital insights into the sedimentary environment and transport history. However, little is known about the particle shape variation across different sand fractions, as well as the differences between particle shape data based on volume and number weighting. In this study, we investigate the grain shape variation of different sand-sized particles (fine, medium, and coarse sand fractions) in aeolian dune (11 samples) and lake beach (12 samples) environments around Poyang Lake, China, using dynamic image analysis (DIA). The shape data results based on both volume-weighted and number-weighted methods reveal significant differences in shape parameters (circularity, symmetry, aspect ratio, and convexity) among different sand fractions, especially between coarse and fine sand. This highlights the critical need for size-fractionated analysis when employing particle shape as an environmental discriminant. By integrating 86 sets of published particle shape data from different depositional environments, we found that volume-weighted shape data has limited ability to differentiate beach and dune sands, although it distinguished the fluvial, desert dune, and coastal beach sand well. In contrast, number-weighted shape data effectively distinguished the beach and dune sands, as fine sand particles are typically transported in suspension during fluvial processes and in saltation during aeolian processes. This demonstrates the role of integrating both volume-weighted and number-weighted shape data in future studies to accurately distinguish sedimentary environments. Full article

In response to China’s “Dual Carbon” goals, this paper proposes a Transmission Network Expansion Planning (TNEP) model that explicitly incorporates the operational flexibility of Virtual Power Plants (VPPs). Unlike conventional approaches that focus mainly on transmission investment, the proposed method accounts for the aggregated dispatchable capability of VPPs, providing a more accurate representation of distributed resources. The VPP aggregation model is characterized by the inclusion of electric vehicles, which act not only as load-side demand but also as flexible energy storage units through vehicle-to-grid interaction. By coordinating EV charging/discharging with photovoltaics, wind generation, and other distributed resources, the VPP significantly enhances system flexibility and provides essential support for grid operation. The vertex search method is employed to delineate the boundary of the VPP’s dispatchable feasible region, from which an equivalent model is established to capture its charging, discharging, and energy storage characteristics. This model is then integrated into the TNEP framework, which minimizes the comprehensive cost, including annualized line investment and the operational costs of both the VPP and the power grid. The resulting non-convex optimization problem is solved using the Quantum Particle Swarm Optimization (QPSO) algorithm. A case study based on the Garver-6 bus and Garver-18 bus systems demonstrates the effectiveness of the approach. The results show that, compared with traditional planning methods, strategically located VPPs can save up to 6.65% in investment costs. This VPP-integrated TNEP scheme enhances system flexibility, improves economic efficiency, and strengthens operational security by smoothing load profiles and optimizing power flows, thereby offering a more reliable and sustainable planning solution. Full article

The reconfiguration of distribution networks plays a crucial role in minimizing active power losses and enhancing reliability, but the problem becomes increasingly complex with the integration of distributed generation (DG). Traditional optimization methods and even earlier hybrid metaheuristics often suffer from premature convergence or require problem reformulations that compromise feasibility. To overcome these limitations, this paper proposes a novel hybrid algorithm that couples Particle Swarm Optimization (PSO) with Simulated Annealing (SA) through an adaptive inertia weight mechanism derived from the Lundy–Mees cooling schedule. Unlike prior hybrid approaches, our method directly addresses the original non-convex, combinatorial nature of the Distribution Network Reconfiguration (DNR) problem without convexification or post-processing adjustments. The main contributions of this study are fourfold: (i) proposing a PSO-SA hybridization strategy that enhances global exploration and avoids stagnation; (ii) introducing an adaptive inertia weight rule tuned by SA, more effective than traditional schemes; (iii) applying a stagnation-based stopping criterion to speed up convergence and reduce computational cost; and (iv) validating the approach on 5-, 33-, and 69-bus systems, with and without DG, showing robustness, recurrence rates above 80%, and low variability compared to conventional PSO. Simulation results confirm that the proposed PSO-SA algorithm achieves superior performance in both loss minimization and solution stability, positioning it as a competitive and scalable alternative for modern active distribution systems. Full article

This study investigates how process manufacturing enterprises can optimize semi-finished inventory (SFI) distribution in delayed production models, with particular attention to differences in cost volatility between single- and multi-period planning scenarios. To address this research gap, we develop a mixed-integer programming model that determines optimal customer order decoupling point (CODP)/product differentiation point (PDP) positions and SFI quantities (both generic and dedicated) for each production period, employing particle swarm optimization for solution derivation and validating findings through a comprehensive case study of a steel manufacturer with characteristic long-period production processes. The analysis yields two significant findings: (1) single-period operations demonstrate marked cost sensitivity to service level requirements and delay penalties, necessitating end-stage inventory buffers, and (2) multi-period optimization generates a distinctive cost-smoothing effect through strategic order deferrals and cross-period inventory reuse, resulting in remarkably stable total costs (≤2% variation observed). The study makes seminal theoretical contributions by revealing the convex cost sensitivity of short-term inventory decisions versus the near-flat cost trajectories achievable through multi-period planning, while establishing practical guidelines for process industries through its empirically validated two-period threshold for optimal order deferral and inventory positioning strategies. Full article

Economic dispatch using wind power plants plays a role in reducing the price of electricity production by dispatching power among different generating units for thermal and wind power plants, and supplying load demand while meeting the power system equality and inequality constraints. Adding wind power plants to the economic dispatch model can significantly reduce electricity production costs and reduce carbon dioxide emissions. In this paper, fuel cost and emission minimization are considered as the objective function of the economic dispatch problem, taking into account transmission loss using the B matrix. The quadratic model of the fuel cost and emission criterion functions is modeled without considering a valve-point loading effect. The real power generation limits for both wind and conventional generating units are considered. In addition, a closed-form expression based on the incomplete gamma function is provided to define the impact of wind power, which includes the cost of wind energy, including overestimation and underestimation of available wind power using a Weibull-based probability density function. In this research work, Lagrange’s algorithm is proposed to solve the Wind–Thermal Economic Emission Dispatch (WTEED) problem. The developed Lagrange classical optimization algorithm for the WTEED problem is validated using the IEEE test systems with 6-, 10-, and 40-generation unit systems. The proposed Lagrange optimization method for WTEED problem solutions demonstrates a notable improvement in both economic and environmental performance compared to other heuristic optimization methods reported in the literature. Specifically, the fuel cost was reduced by an average of 4.27% in the IEEE 6-unit system, indicating more economical power dispatch. Additionally, the emission cost was lowered by an average 22% in the IEEE 40-unit system, reflecting better environmental compliance and sustainability. These results highlight the effectiveness of the proposed approach in achieving a balanced trade-off between cost minimization and emission reduction, outperforming several existing heuristic techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) under similar test conditions. The research findings report that the proposed Lagrange classical method is efficient and accurate for the convex wind–thermal economic emission dispatch problem. Full article

Background/Objectives: The resistance mutations EGFR^{L858R/T790M/C797S} in epidermal growth factor receptor (EGFR) are key factors in the reduced efficacy of Osimertinib. Predicting the inhibitory effects of Osimertinib derivatives against these mutations is crucial for the development of more effective inhibitors. This study aims to predict the inhibitory effects of Osimertinib derivatives against EGFR^{L858R/T790M/C797S} mutations. Methods: Six models were established using heuristic method (HM), random forest (RF), gene expression programming (GEP), gradient boosting decision tree (GBDT), polynomial kernel function support vector machine (SVM), and mixed kernel function SVM (MIX-SVM). The descriptors for these models were selected by the heuristic method or XGBoost. Comprehensive learning particle swarm optimizer was adopted to optimize hyperparameters. Additionally, the internal and external validation were performed by leave-one-out cross-validation ($Q_{LOO}^2$), 5-fold cross validation ($Q_{5-fold}^2$) and concordance correlation coefficient (CCC), $Q_{F1}^2$, and $Q_{F2}^2$. The properties of novel EGFR inhibitors were explored through molecular docking analysis. Results: The model established by MIX-SVM whose kernel function is a convex combination of three regular kernel functions is best: $R^2$ and RMSE for training set and test set are 0.9445, 0.1659 and 0.9490, 0.1814, respectively; $Q_{LOO}^2$, $Q_{5-fold}^2$, CCC, $Q_{F1}^2$, and $Q_{F2}^2$ are 0.9107, 0.8621, 0.9835, 0.9689, and 0.9680. Based on these results, the IC₅₀ values of 162 newly designed compounds were predicted using the HM model, and the top four candidates with the most favorable physicochemical properties were subsequently validated through PEA. Conclusions: The MIX-SVM method will provide useful guidance for the design and screening of novel EGFR^{L858R/T790M/C797S} inhibitors. Full article

A meshless, quasi-convex reproducing kernel particle framework for three-dimensional steady-state thermomechanical coupling problems is presented in this paper. A meshfree, second-order, quasi-convex reproducing kernel scheme is employed to approximate field variables for solving the linear Poisson equation and the elastic thermal stress equation in sequence. The quasi-convex reproducing kernel approximation proposed by Wang et al. to construct almost positive reproducing kernel shape functions with relaxed monomial reproducing conditions is applied to improve the positivity of the thermal matrixes in the final discreated equations. Two numerical examples are given to verify the effectiveness of the developed method. The numerical results show that the solutions obtained by the quasi-convex reproducing kernel particle method agree well with the analytical ones, with a slightly better-improved numerical accuracy than the element-free Galerkin method and the reproducing kernel particle method. The effects of different parameters, i.e., the scaling parameter, the penalty factor, and node distribution on computational accuracy and efficiency, are also investigated. Full article