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The precision of short-term photovoltaic power forecasts is of utmost importance for the planning and operation of the electrical grid system. To enhance the precision of short-term output power prediction in photovoltaic systems, this paper proposes a method integrating K-means clustering: an improved snake optimization algorithm with a convolutional neural network–bidirectional long short-term memory network to predict short-term photovoltaic power. Firstly, K-means clustering is utilized to categorize weather scenarios into three categories: sunny, cloudy, and rainy. The Pearson correlation coefficient method is then utilized to determine the inputs of the model. Secondly, the snake optimization algorithm is improved by introducing Tent chaotic mapping, lens imaging backward learning, and an optimal individual adaptive perturbation strategy to enhance its optimization ability. Then, the multi-strategy improved snake optimization algorithm is employed to optimize the parameters of the convolutional neural network–bidirectional long short-term memory network model, thereby augmenting the predictive precision of the model. Finally, the model established in this paper is utilized to forecast photovoltaic power in diverse weather scenarios. The simulation findings indicate that the regression coefficients of this method can reach 0.99216, 0.95772, and 0.93163 on sunny, cloudy, and rainy days, which has better prediction precision and adaptability under various weather conditions.

In engineering applications, many complex problems can be formulated as mathematical optimization challenges, and efficiently solving these problems is critical. Metaheuristic algorithms have proven highly effective in addressing a wide range of engineering issues. The Snake Optimization Algorithm (SO) is a novel metaheuristic method with widespread use. However, SO has limitations, including reduced search efficiency in later stages and a tendency to get trapped in local optima, preventing full exploration of the solution space. To overcome these, this paper introduces the Multi-strategy Improved Snake Optimization Algorithm (ISO), which integrates six key strategies. First, the Sobol sequence is used for population initialization, ensuring uniform distribution and enhancing global exploration. Second, the RIME algorithm accelerates convergence and improves exploitation. Lens reverse learning further promotes exploration, avoiding local optima. Levy flight facilitates large random steps, balancing exploration and refinement. Adaptive step-size adjustment dynamically tunes the step size based on fitness, optimizing exploration-exploitation. Lastly, the Brownian random walk introduces local perturbations to fine-tune solutions. These strategies collectively improve convergence speed, stability, and optimization capability, ensuring an effective balance between exploration and exploitation. The ISO population distribution was evaluated using three uniformity algorithms: Average Nearest Neighbor Distance, Star Discrepancy, and Sum of Squared Deviations (SSD). ISO demonstrated improvements of 63.08%, 26.09%, and 8.88%, respectively, over SO. Its exploration-exploitation balance and convergence were analyzed on the 30-dimensional CEC-2017 benchmark functions. Additionally, ISO was tested on 23 classic benchmark functions, CEC-2011, and CEC-2017 benchmark functions. Results showed ISO’s superior performance in convergence speed, stability, and global optimization. Furthermore, ISO was successfully applied in four engineering domains: UAV path planning, robot path planning, wireless sensor network node deployment, and pressure vessel design. In all cases, ISO outperformed SO with rapid convergence and strong robustness, achieving performance improvements of 5.69%, 34.61%, 20.73%, and 7.8%, respectively, underscoring its superior efficacy in practical applications.

With the continued rapid growth of urban areas, problems such as traffic congestion and environmental pollution have become increasingly common. Alleviating these problems involves addressing signal timing optimization and control, which are critical components of urban traffic management. In this paper, a VISSIM simulation-based traffic signal timing optimization model is proposed with the aim of addressing these urban traffic congestion issues. The proposed model uses the YOLO-X model to obtain road information from video surveillance data and predicts future traffic flow using the long short-term memory (LSTM) model. The model was optimized using the snake optimization (SO) algorithm. The effectiveness of the model was verified by applying this method through an empirical example, which shows that the model can provide an improved signal timing scheme compared to the fixed timing scheme, with a decrease of 23.34% in the current period. This study provides a feasible approach for the research of signal timing optimization processes.

In a complex and dynamic battlefield environment, enabling autonomous underwater vehicles (AUVs) to reach dynamic targets in the shortest possible time using global autonomous planning is a key issue affecting the completion of search tasks. In this study, ahierarchicalAUV task planning method that uses a combination of hierarchical programming and a snake optimization algorithm is proposed for two typical cases where the platform can provide initial target information. This method decomposes the search task problem into a three-level programming problem, with the outer task planning goal of achieving the shortest encounter time between AUV and dynamic targets; the goal of task planning in the middle layer is to achieve the shortest actual navigation time for AUVs under different operating conditions; and the internal task planning is responsible for considering the comprehensive trajectory optimization under navigation constraints such as threat zone, path length, and path smoothness. The snake optimization algorithm was used for solving each layer of task planning. The feasibility of the proposed method was verified through simulation experiments of AUV search tasks under two types of initial target information conditions. The simulation results show that this method can achieve task planning for AUV searching for dynamic targets under various constraint conditions, optimize the encounter time between AUV and dynamic targets, and have strong engineering practical value. It has certain reference significance for task planning problems similar to underwater unmanned equipment.

In response to the challenge of rapid unmanned aerial vehicles (UAV) path planning for bridge construction in complex terrain, this paper presents an enhanced snake optimization (CSGLSO) UAV three-dimensional path planning algorithm. Initially, this study enhances the stochasticity strategy for generating initial populations within the Snake Optimization (SO) algorithm employing the Piecewise Chaotic Mapping technique, thereby obliterating transient periodic traits and fostering equilibrium in the solution space of the SO algorithm’s progenies. Subsequently, integrating the Subtraction-Average-Based Optimizer algorithm mitigates the issue of convergence speed within the SO algorithm confronting high-dimensional complex functions. Ultimately, employing adaptive t-distribution and lens imaging reverse learning facilitates the evasion of local optima within the current position by the SO algorithm, thus augmenting its exploratory prowess. To ascertain the efficacy of the enhanced algorithm, 14 standard test function convergence comparison experiments were conducted, as well as three-dimensional path planning simulation experiments under multi-scenario conditions of bridge construction by UAV. Experimental findings reveal that relative to SO, Hybrid Snake Optimizer Algorithm, Improved Salp Swarm Algorithm, and Exploratory Cuckoo Search, CSGLSO manifests shorter and more streamlined trajectories, accelerated convergence rates, and elevated optimization precision. Thereby, UAVs are empowered to execute path-planning endeavors expeditiously and precisely within intricate environments.

In Unmanned Aerial Vehicle (UAV) networks, multi-class aerial image classification (AIC) is crucial in various applications, from environmental monitoring to infrastructure inspection. Deep Learning (DL), a powerful tool in artificial intelligence (AI), proves significant in this context, enabling the model to analyze and classify complex aerial images effectually. By utilizing advanced neural network architectures, such as convolutional neural networks (CNN), DL models outperform at identifying complex features and patterns within the aerial imagery. These models can extract spectral and spatial information from the captured data, classifying diverse terrains, structures, and objects precisely. Furthermore, the integration of Snake Optimization algorithms assists in fine-tuning the classification process, improving accuracy. As UAV networks continue to expand, DL-powered multi-class AIC significantly enhances the performance of surveillance, reconnaissance, and remote sensing tasks, contributing to the advancement of autonomous aerial systems. This study proposes a Snake Optimization Algorithm with Deep Learning for Multi-Class Aerial Image Classification (SOADL-MCAIC) methodology on UAV Networks. The main purpose of SOADL-MCAIC methodology is to recognize the presence of multiple classes of aerial images on the UAV networks. To accomplish this, the SOADL-MCAIC technique utilizes Gaussian filtering (GF) for pre-processing. In addition, the SOADL-MCAIC technique employs the Efficient DenseNet model to learn difficult and intrinsic features in the image. The SOA-based hyperparameter tuning process is used to enhance the performance of the Efficient DenseNet technique. Finally, the kernel extreme learning machine (KELM)-based classification algorithm is implemented to identify and classify the presence of various classes in aerial images. The simulation outcomes of the SOADL-MCAIC method are examined under the UCM land use dataset. The experimental analysis of the SOADL-MCAIC method portrayed a superior accuracy value of 99.75% over existing models.

Inertia is the measure of a power system’s ability to resist power interference. The accurate estimation and prediction of inertia are crucial for the safe operation of the power system. To obtain the accurate power system inertia provided by generators, this paper proposes an estimation method considering the influence of frequency and voltage characteristics on the power deficit during transients. Specifically, the traditional swing equations-based inertia estimation model is improved by embedding linearized frequency and voltage factors. On this basis, the snake optimization algorithm is utilized to identify the power system inertia constant due to its strong global search ability and fast convergence speed. Finally, the proposed inertia estimation method is validated in four test systems, and the results show the effectiveness of the proposed method.

To address the issue that traditional snake optimization (SO) algorithms tend to become trapped in local optima when identifying aerodynamic parameters of high-spin projectiles – where complex flight dynamics and measurement noise further complicate the process – this paper proposes an enhanced snake optimization algorithm integrated with genetic algorithm (GA) mechanisms. Specifically, the improved algorithm incorporates GA-based selection and crossover operations into the SO framework, aiming to strengthen global search capability by simulating not only snakes’ natural foraging and combat behaviors but also the evolutionary characteristics of genetic algorithms. For handling noisy trajectory data, Kalman filtering is applied to denoise measured information, laying a reliable foundation for subsequent parameter identification. The method utilizes segmented trajectory data of high-spin projectiles across different speed stages for analysis. Comparative experiments with the traditional SO algorithm and other optimized variants demonstrate that the proposed approach reduces identification errors by 49%, significantly outperforming conventional methods in accuracy. Further validation with full trajectory measured data shows that when the identified aerodynamic parameters are substituted into ballistic equations, the deviation between calculated and actual impact point coordinates is minimal, confirming their effectiveness. Notably, the improved algorithm does not rely on precise initial parameter settings, enhancing its adaptability in practical scenarios. In summary, it provides a robust solution for accurately identifying projectile aerodynamic parameters and holds promises for engineering applications.

Quality of Service (QoS) routing protocol is a hot topic in the research field of wireless sensor networks (WSNs). However, the task of identifying an optimal path that simultaneously meets multiple QoS constraints is acknowledged as an NP-hard problem, with its complexity intensifying in proportion to the network’s nodal count. Therefore, a novel heuristic multi-objective trust routing method, the Levy Chaos Adaptive Snake Optimization-based Multi-Trust Routing Method (LCASO-MTRM), is proposed, aiming to enhance link bandwidth while simultaneously reducing latency, packet loss, and energy consumption. The proposed method incorporates innovative chaos and adaptive operators within the LCASO framework. The chaos operator enhances population diversity, expands the solution space, and accelerates the search process. Meanwhile, the adaptive operator improves convergence, enhances robustness, and effectively prevents stagnation. Additionally, this paper introduces a novel multi-objective QoS routing model that integrates a link trust mechanism, allowing for a more accurate assessment of link trust levels and a precise reflection of the current link status. The effectiveness of LCASO-MTRM is demonstrated through simulation comparisons with the Improved Particle Swarm Optimization (IPSO), Improved Artificial Bee Colony Algorithm (IABC), and Cloned Whale Optimization Algorithm (CWOA). Simulation results demonstrate that LCASO-MTRM significantly reduces energy consumption by 49.53%, latency by 22.56%, and packet loss by 40.21%, while increasing bandwidth by 6.13%, outperforming the other algorithms.

As extreme weather events become more frequent, the icing of transmission lines in winter has become more common, causing significant economic losses to power systems and drawing increasing attention. However, owing to the complexity of the conductor icing process, establishing high-precision ice thickness prediction models is vital for ensuring the safe and stable operation of power grids. Therefore, this paper proposes a hybrid model combining an improved snake optimization (ISO) algorithm, deep extreme learning machine (DELM), and hybrid kernel extreme learning machine (HKELM). Firstly, based on the analysis of the factors that influence the icing, the temperature, the humidity, the wind velocity, the wind direction, and the precipitation are selected as the weather parameters for the prediction model of the transmission line icing. Secondly, the HKELM is introduced into the regression layer of DELM to obtain the deep hybrid kernel extreme learning machine (DHKELM) model for ice thickness prediction. The SO algorithm is then augmented by incorporating the Latin hypercube sampling technique, t-distribution mutation strategy, and Cauchy mutation, enhancing its convergence. Finally, the ISO-DHKELM model is applied to the icing data of transmission lines in Sichuan Province for experiments. The simulation results indicate that this model not only performs well, but also enhances the accuracy of ice thickness predictions.