Search Results (6,712)

Metallothionein (MT) is a multifunctional metal-binding protein with broad applications in medicine, healthcare, and food industries, but its large-scale use is limited by inefficient industrial synthesis. To address this and obtain optimal fermentation parameters for large-scale MT production, this study used the recombinant marine-derived MT-producing Pichia pastoris strain SMD1168-MT. We first optimized the strain’s growth and induced fermentation conditions, then constructed a Back Propagation (BP) neural network model for in-depth parameter optimization and accurate MT expression prediction. Results showed the optimal growth conditions for SMD1168-MT were: 30 °C, initial pH 8.0, shaking speed 220 r/min, and 4% inoculum size. The BP model exhibited high accuracy (training set: R² = 0.8430, MAE = 0.0129, RMSE = 0.0175; validation set: R² = 0.8337, MAE = 0.0144, RMSE = 0.0174). Combined with Particle Swarm Optimization (PSO), the optimal fermentation conditions were: 7.7% methanol, initial OD₆₀₀ 8.2, 240 r/min, 50 h induction, and 125 μmol/L Zn²⁺. Validation confirmed MT expression reached 0.2141 mg/mL (2.93-fold). This study demonstrates that the BP neural network effectively optimizes recombinant P. pastoris-based marine-derived MT fermentation, improving yield and providing a basis for industrial scale-up. Full article

Dynamic cables, serving as the critical link between floating wind turbines and submarine cables, are subjected to significant tension fluctuations and bending deformations under environmental loading. While deep-water systems have been widely studied, investigations of large-capacity wind turbines in shallow water environments remain limited. This study establishes a coupled numerical model of an IEA 15 MW floating wind turbine and its dynamic cable system at a water depth of 50 m. The platform’s six-degree-of-freedom motions were calculated under 0°, 90°, and 180° loading directions, followed by a systematic analysis of lazy-wave dynamic cable response characteristics. Results indicate that platform motions and dynamic cable responses are strongly direction-dependent in shallow water, with the 0° loading direction identified as the governing design case due to peak curvature and tension levels. Analysis reveals that the touchdown point location is the primary driver of tension response, while cable length increments predominantly influence bending. Utilizing these insights, a multi-objective fitness function was integrated with a Particle Swarm Optimization (PSO) algorithm. The optimized configuration significantly reduced peak curvature and total cable length, providing a theoretical framework and engineering guidance for the design of high-capacity floating wind systems in shallow-water regions. Full article

A hybrid aquatic–aerial vehicle (HAAV) is a novel type of aircraft capable of both aerial flight and underwater navigation. Inspired by the swan’s gliding and landing motion on water surfaces, this study investigates the dynamic modeling and integrated optimization design of an HAAV equipped with a biomimetic skipping plate. By comprehensively accounting for the aerodynamic, impact, hydrodynamic, and frictional forces during the water entry process, a dynamic model for the HAAV’s gliding water entry is established. The reliability of the model is verified through comparisons between numerical simulations and theoretical predictions. Parametric modeling of the skipping plate’s configuration and layout is performed to analyze the influence of different parameters on the water entry dynamics. With the objectives of minimizing the overload and pitch angle variation, a hybrid infilling strategy based on a radial basis function neural network (RBFNN) surrogate model is constructed to improve optimization efficiency. This is combined with a quantum-behaved particle swarm optimization (QPSO) algorithm to conduct the multi-objective optimization of the biomimetic plate, thereby obtaining its optimal configuration and layout parameters. The results demonstrate that the established dynamic model is effective and can accurately capture the kinematic characteristics of the gliding water entry process. The error between the peak load and the pitch angle variation is less than 5%. Compared with the direct QPSO algorithm, the proposed method reduces the number of model evaluations by 66.7%, the computational time by 52.1%, and the optimal solution response value by 12.01%, demonstrating strong potential for engineering applications. Full article

Integrated Gate-Commutated Thyristor (IGCT) serves as the core power electronic device in high-voltage and high-power renewable energy conversion systems. Aiming at the problems of slow convergence, easy to fall into local optima, and difficulty in balancing multi-objective performance in traditional IGCT gate drive design under power fluctuation conditions, this paper proposes an IGCT gate drive optimization method based on the Improved Grey Wolf Optimization (IGWO) algorithm. A multi-objective optimization model is established with switching loss reduction, voltage overshoot suppression, current oscillation attenuation and driving capability guarantee as objectives and gate resistance and driving voltage as optimization variables. The traditional grey wolf algorithm is improved by adaptive weight adjustment and dynamic search step strategies to balance global exploration and local exploitation. Simulation and experimental results show that, compared with the traditional Grey Wolf Algorithm (GWO) and Particle Swarm Optimization (PSO), the convergence speed of IGWO is increased by 40.4% and 51.0%, and the optimization accuracy is improved by 12.7% and 18.1%, respectively. Compared with the conventional empirical design, the optimized drive circuit reduces the switching loss by 31.8%, suppresses the voltage overshoot by 33.7%, decreases the current oscillation by 38.6%, and shortens the driving rise time by 39.3%. The proposed method realizes the automatic and precise tuning of IGCT gate drive parameters, effectively improves the switching performance and operation stability of IGCT under renewable energy fluctuation conditions, and provides a practical intelligent optimization scheme for the high-performance gate drive design of high-power IGCT devices. Full article

Coking coal flotation is a typical nonlinear, multi-variable, and multi-objective process in which concentrate quality and combustible matter recovery must be balanced under fluctuating feed and operating conditions. To improve both predictive reliability and decision support, this study proposes an integrated data-driven framework that combines particle swarm optimization-back propagation (PSO-BP) prediction, SHapley Additive exPlanations (SHAP) based interpretation, Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimization, and entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (Entropy-TOPSIS) decision-making. After three-sigma outlier screening, 2000 valid distributed control system (DCS) samples were retained for model development and temporal holdout evaluation, and an additional 200 later-period industrial samples were used for independent validation. The data were partitioned chronologically, with months 1–4, month 5, and month 6 used for training, validation, and temporal holdout testing, respectively, while the months 7–8 dataset was reserved for later-period validation. The results show that PSO-BP consistently outperformed conventional BP under both temporal holdout and later-period validation. SHAP analysis identified raw coal ash and collector dosage as the dominant factors for product-quality prediction, while collector dosage and frother dosage contributed most strongly to tailing heat of combustion. NSGA-II further revealed the trade-off among clean coal ash, clean coal sulfur, and tailing heat of combustion, and Entropy-TOPSIS converted the Pareto-optimal candidate set into a practically balanced operating recommendation. Sensitivity and robustness analyses indicated acceptable stability of both the optimization process and the final decision result. Overall, the proposed framework provides an interpretable prediction–optimization–decision workflow for coking coal flotation and offers a practical basis for future DCS-assisted intelligent regulation. Full article

The increasing penetration of photovoltaic (PV) systems in distribution networks has introduced new challenges in voltage regulation and energy loss mitigation, particularly under time-varying loading conditions. This paper presents a constrained multi-objective mathematical optimization framework for the optimal allocation and sizing of PV-STATCOM devices in radial distribution systems. The problem is formulated as a nonlinear optimization model that minimizes the daily energy losses over a 24 h operating horizon while satisfying network operational constraints, inverter capacity limits, and renewable penetration restrictions. To efficiently solve the resulting non-convex optimization problem, a metaheuristic algorithm based on the weighted mean of vectors (WMV) is employed. The WMV method integrates wavelet-based weighting mechanisms, mean-driven update rules, vector combination strategies, and a local refinement operator to balance global exploration and local exploitation within the feasible search domain. Constraint violations are handled through a penalty-based mathematical transformation of the objective function. The proposed framework is validated on the IEEE 33-bus and IEEE 69-bus distribution systems under realistic daily load variations. The numerical results demonstrate significant reductions in daily energy losses compared to differential evolution, particle swarm optimization, artificial rabbits optimization, and golden search optimization algorithms. Furthermore, convergence analysis confirms the robustness and computational efficiency of the WMV approach in solving large-scale constrained power system optimization problems. Full article

Air–ground collaborative systems leverage the complementary strengths of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) and hold significant potential for logistics in complex, unstructured environments. However, trajectory planning in infrastructure-free mountainous regions remains challenging owing to the need for continuous tight coupling, obstacle avoidance, and reliable communication-link maintenance. To address these challenges, this study proposes a cooperative trajectory planning framework that enforces strict inter-vehicle distance constraints to maintain communication connectivity. By formulating the coordination problem in terms of relative configurations between air and ground vehicles, the proposed framework exhibits translational invariance, reflecting an underlying symmetry with respect to global position shifts. This symmetry-aware formulation reduces reliance on absolute coordinates and promotes consistent cooperative behavior under environmental variability. The trajectory planning problem is mathematically formulated as a constrained multi-objective nonlinear programming (MONLP) model that balances energy consumption and trajectory smoothness. An adaptive inertia weight particle swarm optimization (AIWPSO) algorithm is developed to efficiently solve the resulting optimization problem. Simulation results demonstrate that the proposed approach generates smooth, collision-free trajectories while maintaining stable air–ground coordination, demonstrating improved feasibility and robustness over conventional planning methods in unstructured mountainous environments. Full article

This study investigates the dimensional synthesis and optimization of multi-link steering mechanisms—namely, the leading and mixed-leading double four-bar configurations—for front-wheel-drive vehicles. To overcome the accuracy limitations of conventional steering at large angles (up to 70°), a comparative metaheuristic approach is employed, utilizing two popular metaheuristic optimizations, Improved Particle Swarm Optimization (IPSO) and Differential Evolution with golden ratio (DE-gr), to optimize the geometric parameters of these complex eight-bar steering systems. Using a track-to-wheelbase ratio of 0.5, the optimization minimizes a mean-squared structural-error objective function integrated with Grashof mobility constraints. The optimized mechanisms are validated via ADAMS kinematic simulations and further analyzed in MATLAB R2021 regarding steering accuracy, transmission angles, and mechanical advantage. The results reveal a distinct performance trade-off: mixed-leading configurations achieve superior geometric precision and mass reduction due to shorter link lengths, with IPSO yielding the highest accuracy. Conversely, leading-type mechanisms provide a more linear and stable mechanical advantage, ensuring predictable force transmission. While DE-gr exhibits faster convergence across both variants, both algorithms effectively exploit the complex parameter space of multi-link systems. Ultimately, this metaheuristic optimization-based approach offers a superior and robust framework for the dimensional synthesis of high-performance multi-link steering mechanisms, surpassing the constraints of traditional gradient-based methods. Our findings recommend the mixed-leading configuration for precision-focused applications and the leading configuration for scenarios requiring consistent mechanical performance. Full article

Optimization of ridesharing systems poses challenges for the development of solvers due to a nonconvex discrete solution space and complex constraints. Over the past decade, many metaheuristic algorithms have been proposed to solve optimization problems in ridesharing systems. Performance, robustness and efficiency are three important issues in the development of metaheuristic algorithms for ridesharing systems. Cooperative coevolution is a potential approach to improving the performance, robustness, and efficiency of metaheuristic algorithms. However, studies on the application of cooperative coevolution to optimization problems in ridesharing systems remain limited, as most existing work focuses on problems with a continuous solution space. Metaheuristic algorithms can be combined with the cooperative coevolution approach to solve optimization problems. In this paper, we combine particle swarm optimization (PSO) and bare-bones particle swarm optimization (BBPSO) with cooperative coevolution to develop two metaheuristic algorithms for ridesharing systems: discrete cooperative coevolution-based particle swarm optimization (DCCPSO) and discrete cooperative coevolution-based bare-bones particle swarm optimization (DCCBBPSO). We conducted a comparative study to assess their effectiveness in terms of performance, robustness and efficiency based on the experimental results. The results indicate that the cooperative coevolution-based metaheuristic algorithms developed in this study outperform discrete PSO (DPSO), discrete BBPSO (DBBPSO), and many other existing metaheuristic algorithms for ridesharing systems in terms of performance and robustness. The results show that the DCCPSO algorithm and the DCCBBPSO algorithm outperform the other 16 metaheuristic algorithms in convergence speed (measured by the average number of generations to find the best solution) in most cases. However, the DCCPSO and the DCCBBPSO algorithms do not outperform all the other 16 metaheuristic algorithms in terms of runtime. This is due to the inherent complex structure of the CC approach. Full article

Personalized federated learning (PFL) faces significant challenges in resource-constrained edge environments, where strict communication budgets and severe system heterogeneity must be jointly addressed. Although one-shot federated learning reduces communication overhead, existing methods typically impose unified model architectures or rely on coarse manual selection strategies, limiting their adaptability to highly heterogeneous data distributions and restricting personalized representation capability. To overcome these limitations, we propose Personalized Federated Zero-shot Knowledge Distillation (pFedZKD), a data-free one-shot federated learning framework designed for structurally heterogeneous scenarios. The framework follows a decouple-and-reconstruct collaborative paradigm. On the client side (decoupling stage), we introduce Particle Swarm Optimization-based Federated Neural Architecture Search (PSO-FedNAS), a gradient-free neural architecture search method that enables each client to autonomously discover a customized convolutional architecture aligned with its local data distribution, eliminating the need for architectural consistency across clients. On the server side (reconstruction stage), to address parameter-space incompatibility caused by structural heterogeneity, we develop an architecture-agnostic multi-teacher zero-shot knowledge distillation mechanism (Multi-ZSKD). This method synthesizes pseudo-samples in latent space to extract semantic consensus from heterogeneous client models and transfers the aggregated knowledge to a unified global student model without accessing real data. The entire collaborative process is completed within a single communication round, substantially reducing communication cost while enhancing privacy preservation. Extensive experiments on MNIST, FashionMNIST, SVHN, and CIFAR-10 under heterogeneous data settings demonstrate that pFedZKD consistently achieves superior personalization accuracy, global generalization performance, and communication efficiency compared with state-of-the-art PFL methods. Full article

Path planning of mobile robots on grid maps is a complex optimization problem, and applying standard particle swarm optimization (PSO) to this task often leads to stagnation and premature convergence. To address these issues, a particle swarm optimizer enhanced by fluid mechanics and differential evolution (FMDEPSO) is proposed. The method integrates fluid-inspired neighborhood feedback with a differential evolution recombination mechanism to construct a semi-discrete population evolution framework. Specifically, FMDEPSO introduces a pressure repulsion term and a viscous diffusion term to mitigate early population collapse and suppress oscillations caused by abrupt velocity variations. Meanwhile, a gas–liquid phased adaptive scheduling strategy is adopted to dynamically adjust the learning factors, thereby balancing exploration and exploitation. In addition, the mutation–crossover–greedy selection operator of differential evolution (DE) is embedded into the update process to preserve population diversity and enhance the capability of escaping local optima. On the CEC2017 benchmark suite, FMDEPSO achieved the best mean results on 17, 19, and 17 functions under 30-, 50-, and 100-dimensional settings, respectively, compared with eight representative PSO variants. It maintained a top-three ranking on the majority of functions and obtained the overall best average rank according to the Friedman test. The Wilcoxon rank-sum test further confirmed its statistical advantage on most benchmark functions. In grid-based path-planning experiments on multi-scale environments ($20\times 20$, $40\times 40$, and $60\times 60$), FMDEPSO generates smooth and goal-directed feasible trajectories in successful runs and achieves the best overall performance among PSO-based methods while maintaining a favorable balance among path quality, success rate, and runtime across different complexity levels. Overall, the proposed method exhibits stable convergence behavior and competitive solution quality in both numerical benchmark optimization and mobile robot path-planning tasks. Full article

Wastewater heat recovery has emerged as a vital strategy for building energy conservation, due to its significant potential and the inherent thermal stability of sewage as a heat source. Enhancing synergy between such waste heat and other clean energy sources is a key research focus. This study developed a solar-assisted sewage-source coupled heating system for a Chinese university dormitory and established a multiobjective optimization framework integrating economic, environmental, and energy efficiency indicators via a combined weighting approach of the Analytic Hierarchy Process and Entropy Weight Method. Optimization was conducted using the Hooke–Jeeves algorithm, Particle Swarm Optimization algorithm, and the Hooke–Jeeves–Particle Swarm Optimization hybrid algorithm (shorten as HJ–PSO), with subsequent comparative performance analysis. The HJ–PSO hybrid performed best: 24% lower operating costs, a 4.8-year shorter dynamic payback period, 26.35% fewer carbon dioxide emissions, 38.65% lower overall energy consumption, and an 11.18% higher system coefficient of performance. Supported by relevant policies, the system is low-carbon and economically viable, enabling grid peak shaving. This research provides theoretical and engineering references for renewable energy heating systems. Full article

Unauthorized occupation of parking spaces designated for individuals with disabilities remains a persistent challenge in urban environments, limiting accessibility and inclusive mobility. This paper proposes an integrated UAV-assisted enforcement framework that combines drone-based imaging, onboard license plate recognition (LPR), IoT connectivity, and a staged optimization strategy for energy-aware surveillance. The framework employs a two-phase approach: first, it derives energy-efficient UAV activation patterns via sleep–active scheduling, followed by coverage maximization under energy constraints. The inherently multi-objective problem—balancing energy consumption, coverage, and redundancy—is addressed via a weighted-aggregation formulation, enabling efficient optimization with classical metaheuristic algorithms. Seven algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Ant Colony Optimization (ACO), Differential Evolution (DE), Artificial Bee Colony (ABC), and a Greedy baseline—are implemented in both conventional and staged variants to enable comprehensive evaluation. Experimental results demonstrate 32–45% reductions in energy consumption, over 95% coverage effectiveness, and 50–60% faster convergence compared to single-phase approaches, with all improvements statistically significant (p < 0.001). The proposed framework provides a scalable, practically deployable solution for intelligent enforcement of disability parking regulations while also enabling energy-efficient UAV coordination in smart urban monitoring systems. Full article

Underwater dynamic target detection, classification, localization, and tracking (DCLT) is central to maritime surveillance and monitoring and increasingly relies on distributed AUV-based robotic sonar networks operating in passive listening and, when required, cooperative multistatic modes. Achieving a robust performance in realistic oceans remains challenging, because sensor placement must adapt to time-varying acoustic conditions and target priors while preserving acoustic communication connectivity, and because frequent reconfiguration under dynamic currents makes classical large-scale planning computationally expensive. This paper presents an integrated deep reinforcement learning (DRL)-based framework for passive-stage sonar placement and dynamic reconfiguration in distributed AUV networks. First, we cast placement as a constructive finite-horizon Markov decision process (MDP) and train a Proximal Policy Optimization (PPO) agent to sequentially build a collision-free layout on a discretized surveillance grid. The terminal reward is formulated to jointly optimize the environment-aware detection performance, computed from BELLHOP-based transmission loss models, and global network connectivity, quantified using algebraic connectivity. Second, to enable time-critical reconfiguration, we estimate flow-aware motion costs for all AUV–destination pairs using a PPO with a Long Short-Term Memory (LSTM) trajectory policy trained for partial observability. The learned policy can be deployed onboard, allowing each AUV to refine its path online using locally sensed currents, improving robustness to ocean-model uncertainty. The resulting cost matrix is solved via an efficient zero-element assignment method to obtain the optimal one-to-one reassignment. In the reported simulation studies, the proposed Sequential PPO placement method achieves a final reward 16–21% higher than Particle Swarm Optimization (PSO) and 2–3.7% higher than the Genetic Algorithm (GA), while the proposed PPO + LSTM planner reduces average travel time by 30.44% compared with A*. The proposed closed-loop architecture supports frequent re-optimization, scalable fleet operation, and a seamless transition to communication-supported cooperative multistatic tracking after detection, enabling efficient, adaptive DCLT in dynamic marine environments. Full article

The connection of large-capacity loads at nodes in a radial distribution network can readily lead to severe voltage sag phenomena, thereby significantly deteriorating power supply quality. To ensure the safe operation of both voltage-sensitive equipment and the power grid, the deployment of Dynamic Voltage Restorers (DVR) and Static Var Compensators (SVC) is recognized as one of the most effective countermeasures for addressing voltage sag issues. Considering the inherent topological characteristics of the radial distribution network, a dimensional collaborative governance strategy is proposed, which takes longitudinal dimension collaborative governance as the primary approach and horizontal dimension collaborative governance as a supplementary measure. Based on sensitivity analysis, the concepts of horizontal sensitivity and longitudinal sensitivity are defined. Furthermore, considering the response time of governance equipment, the voltage sag governance process is divided into two distinct stages: in the first stage, governance is primarily reliant on DVR, and a longitudinal dimension collaborative optimization algorithm is proposed to solve the corresponding optimization model; in the second stage, governance mainly utilizes SVC, where a standard particle swarm optimization (PSO) algorithm is employed to solve its optimization model. A case study conducted on a 42-node radial distribution network validates that the proposed approach effectively governances the voltage sag problem in the distribution network. Through analysis, the number of nodes experiencing voltage sag was reduced from 29 to 0 in both the first and second governance stages. In the first stage, the total compensation voltage of the DVR is 0.581 p.u. With the coordinated participation of SVC in the second stage, the total DVR compensation voltage decreases to 0.100 p.u., corresponding to a significant reduction of 82.79%. Given the higher capital cost of DVR relative to SVC, this substantial decrease in required DVR capacity effectively lowers the overall governance cost. Full article