Seagull Optimization Algorithm Research Articles

DFIG in Wind Energy Applications with High Order Sliding Mode Observer-based Fault-Tolerant Control Scheme using Sea Gull Optimization

This paper describes a new method for maximizing power extraction from a wind energy conversion system (WECS) by using a doubly fed induction generator (DFIG) that operates below nominal wind speed. To maximize the collected power of a wind turbine (WTG) exposed to actuator failure, a fault-tolerant high-order sliding mode observer (HOSMO) and Seagull Optimization Algorithm with a model predictive controller (MPC) technique is proposed. Evaluate both the real state and the sensor error simultaneously using a higher-order sliding-mode observer. Active fault tolerant controllers are designed to regulate wind turbine rotor speed and power in the presence of actuator defects and uncertainty. With the growing interest in employing wind turbines (WTGs) as the primary generators of electrical energy, fault tolerance has been seen as essential to improving efficiency and reliability. This research focuses on optimal fault-tolerant pitch control, which is used to modify the pitch angle of wind turbine blades in the event of sensor, actuator, and system failures. A Seagull Optimization Algorithm (SOA) is proposed to tune controller parameters to improve the performance of WT. The proposed method has achieved 92% of power tracking performance when compared to existing method.

Antenna modeling based on meta-heuristic intelligent algorithms and neural networks

As wireless communication technology continues to advance, the antenna, as an essential front-end device in radio communication system, is surrounded by more and more complex electromagnetic wave environments with increasing variety, resulting in greater demand for antennas and higher design requirements. While the traditional antenna design methods suffered from the disadvantage of low design efficiency, a powerful tool for accelerating antenna design is the modelling of antennas with neural networks. Aiming to enhance the modeling accuracy of neural network, multiple novel meta-heuristic swarm intelligent algorithms are introduced and part of them are modified for the purpose of applying to optimizing network’s weights and biases so as to raise the antenna model`s prediction precision on the basis of neural network. Specifically, the intelligent algorithms and their improvement directions include the strategy of optimizing weights and biases for neural networks with seagull optimization algorithm, optimizing the weights and biases of neural network with the improved butterfly algorithm fused with reverse learning, and the artificial rabbit algorithm optimizing the neural network weights and biases. In addition, two intelligent optimization algorithms that are already more mature: the particle swarm algorithm and the genetic algorithm are added to compare with the above three algorithms. The accuracy of neural network prediction before and after the optimisation of neural network by seagull algorithm, the butterfly algorithm incorporating reverse learning, the artificial rabbit algorithm, the particle swarm algorithm, and the genetic algorithm are got through the results respectively. The results of the experiments displayed that the neural network optimized of the improved butterfly algorithm incorporating reverse learning has a prediction accuracy of 99.69 % with stable results, the optimised neural network prediction accuracy of the seagull algorithm reaches 99.51%, and the optimised neural network prediction accuracy of the artificial rabbit algorithm is 99.49 %. The remaining two traditional algorithms optimized neural network accuracy is 83.1 % and 99.43 % respectively. Therefore, the improved butterfly algorithm incorporating reverse learning is the most effective among these three new algorithms applied to the field of antenna prediction. Moreover, the running time of the network optimized by different algorithms is quite distinct, among which the neural network optimized by the improved butterfly algorithm incorporating reverse learning takes the shortest time, which increases the prediction efficiency of the network by more than 70%. In summary, the application of the fused reverse learning improved butterfly algorithm in optimizing neural network predictions yields the shortest processing time and highest accuracy. This not only enables faster and more precise antenna design but also holds greater significance for the field of antenna design and analysis.

A Crude Oil Spot Price Forecasting Method Incorporating Quadratic Decomposition and Residual Forecasting

The world economy is affected by fluctuations in the price of crude oil, making precise and effective forecasting of crude oil prices essential. In this study, we propose a combined forecasting scheme, which combines a quadratic decomposition and optimized support vector regression (SVR). In the decomposition part, the original crude oil price series are first decomposed using empirical modal decomposition (CEEMDAN), and then the residuals of the first decomposition (RES) are decomposed using variational modal decomposition (VMD). Additionally, this work proposes to optimize the support vector regression model (SVR) by the seagull optimization algorithm (SOA). Ultimately, the empirical investigation created the feature-variable system and predicted the filtered features. By computing evaluation indices like MAE, MSE, R2, and MAPE and validating using Brent and WTI crude oil spot, the prediction errors of the CEEMDAN -RES.-VMD -SOA-SVR combination prediction model presented in this paper are assessed and compared with those of the other twelve comparative models. The empirical evidence shows that the combination model being proposed in this paper outperforms the other related comparative models and improves the accuracy of the crude oil price forecasting model.

Performance analysis of three-phase solar PV, BESS, and Wind integrated UPQC for power quality improvement

UPQC (Unified Power Quality Conditioner) is indeed a Versatile power conditioner which could be utilized to adjust for a variety of power supply voltage disturbances, rectify voltage fluctuation, and minimize harmonic load current in accessing the power source. To ensure a continuous power supply. We are making use of UPQC technology in combination with shunt and series AFC (Active filter compensators) to counteract power quality problems resulting from grid harmonics and nonlinear loads. Therefore, the focus of this research is to analyze the UPQC's system performance under swell and sag with the variation of solar irradiation and temperature. To achieve these two distinct cases of PV system like temperature-25 °C, irradiance-700 W/m2, and temperature-45 °C, irradiance-800 W/m2, are considered respectively. The implementation of the FOPI controller purposes to bolster power quality of 3-phase HESS (hybrid energy storage system), which includes an integrated UPQC for outstanding performance. In order to manage harmonics resulting from non-linear loads and reduce grid power quality issues, the UPQC model integrates both shunt and series AFC. In the case of extended voltage interruptions, the integrated UPQC with BESS, wind and PV can effectively resolve power quality concerns. The UPQC shunt compensator efficiently harnesses power from HES (hybrid energy systems), whereas the series compensator acts as a guardian, protecting the load from power quality issues originating in the grid. In order to regulate the voltage of the DC connection at a desired level, the aim of this article is to develop a FOPI controller with isodamping properties. In particular, a novel hybrid algorithm called the Enhanced Seagull with Rooster Update (ES-RU) Algorithm, which combines the ideas of Chicken Swarm Optimization (CSO) and Seagull Optimization algorithm (SOA), optimally tunes the gain of the FOPI controller. For optimal performance, the FOPI controller's gain parameters (Kp, Ki, λ) are finely tuned using an Enhanced Seagull with Rooster Update (ES-RU) Algorithm. As a consequence, the performance of the adopted and extant models is examined during sag and swell conditions with respect to source voltage and load voltage and compared with other conventional models like PI, FF, CS and SG, respectively for both cases like temperature-25 °C, irradiance 700 W/m2, and temperature-45°C, irradiance-800 W/m2, correspondingly. The overshot and total harmonic distortion (THD) of the proposed ES-RU is superior to the existing models like PI, FF, CS and SG.

Fuzzy logic Based Seagull Optimization Algorithm for Efficiency and Security in Wireless Sensor Networks

Wireless sensor networks (WSN) find applications in diverse fields such as environmental monitoring, healthcare, and industrial control systems. The pivotal components of these networks are the sensor nodes, which, unfortunately, consume a substantial amount of energy when transmitting information directly to the base station (BS). To mitigate energy consumption associated with direct transmission, this paper proposes a two-phase approach utilizing hybrid clustering and routing algorithms. The proposed approach incorporates fuzzy and seagull techniques for clustering and adopts optimal CH (cluster head) selection, CBRP (Cluster-Based Routing Protocol), and AES (Advanced Encryption Standard) for secure routing. The system employs rule-based fuzzy logic to correlate input values in both clustering and routing algorithms. Decision-making is based on factors such as the residual energy of sensor nodes, distance from the BS, and the number of nodes within the communication range. Input variables' crisp values are transformed into diverse fuzzy values, and the fuzzy output values are converted back to crisp values using the centroid defuzzification method. Selection of cluster heads and routers is determined by the output values, with sensor nodes being allocated to respective cluster heads based on their load-handling capacity. The routing path is then generated considering the capacity of routers. Simulations are conducted to evaluate energy consumption, active sensor nodes per round, and the sustainability period of the network. This proposed hybrid clustering and routing system aim to enhance the overall efficiency of wireless sensor networks by optimizing energy consumption and ensuring secure data transmission. The optimization model identifies the most suitable nodes in the routing cycle, starting with chosen cluster heads. The overarching goal is to enhance network indicators, including network lifespan, power consumption per node, and packet delivery percentage. The proposed solution achieved a network lifetime of 100 hours and a data delivery rate of 98%. additionally, it consumed the least amount of energy, measuring at 95,000 joules.

Short-term wind power output prediction using hybrid-enhanced seagull optimization algorithm and support vector machine: A high-precision method

ABSTRACT Affected by the uncertainty of external environmental factors, wind power generation has significant characteristics of randomness and non-stationarity. Accurately predicting wind power is a necessary technical means to improve the efficiency of wind energy utilization and reduce wind abandonment. Integrating large-scale wind power into the grid has put higher requirements for wind power prediction accuracy. Hence, this study proposes a combined prediction model based on wavelet denoising (WD), hybrid enhanced seagull optimization algorithm (HESOA) and support vector machine (SVM) to predict short-term wind power. The WD technology obtains effective signals from original wind power data, providing a reasonable basis for prediction research. The HESOA is proposed by introducing the Tent mapping, individual self-learning factor and Gaussian population strategy into the original seagull optimization algorithm to optimize SVM’s prediction ability. The case study results reflect that the prediction method is feasible and universal in suppressing wind power generation volatility in spring and winter. The RMSE of short-term wind power prediction is controlled below 65 kW, and the value of R2 is above 98%. This study improves the accuracy of wind power prediction and helps to strengthen the renewable energy consumption level and reliability of wind power grid-connected generation systems.

Hybrid Strategies Based Seagull Optimization Algorithm for Solving Engineering Design Problems

The seagull optimization algorithm (SOA) is a meta-heuristic algorithm proposed in 2019. It has the advantages of structural simplicity, few parameters and easy implementation. However, it also has some defects including the three main drawbacks of slow convergence speed, simple search method and poor ability of balancing global exploration and local exploitation. Besides, most of the improved SOA algorithms in the literature have not considered the drawbacks of the SOA comprehensively enough. This paper proposes a hybrid strategies based algorithm (ISOA) to overcome the three main drawbacks of the SOA. Firstly, a hyperbolic tangent function is used to adjust the spiral radius. The spiral radius can change dynamically with the iteration of the algorithm, so that the algorithm can converge quickly. Secondly, an adaptive weight factor improves the position updating method by adjusting the proportion of the best individual to balance the global and local search abilities. Finally, to overcome the single search mode, an improved chaotic local search strategy is introduced for secondary search. A comprehensive comparison between the ISOA and other related algorithms is presented, considering twelve test functions and four engineering design problems. The comparison results indicate that the ISOA has an outstanding performance and a significant advantage in solving engineering problems, especially with an average improvement of 14.67% in solving welded beam design problem.

ELA-RCP: An energy-efficient and load balanced algorithm for reliable controller placement in software-defined networks

Software-defined networking is a modern and popular network technology that utilizes controllers as network operating systems to manage the network and allow user applications to interact with network hardware. Determining the optimal number of controllers and the optimal place to install them are some of the multi-controller model challenges that are known as the Controller Placement Problem (CPP). CPP is an optimization problem that tries to minimize delay and achieve a balanced network. CPP becomes more important when we intend to increase reliability and reduce energy consumption. This paper proposes an Energy-efficient and Load-balanced optimization Algorithm for Reliable Controller Placement (ELA-RCP) in Software-Defined Networks (SDN). The proposed ELA-RCP is a three-step solution to CPPs. First, it determines the optimal number of controllers using the proposed cellular learning automata, taking into account network traffic status, scale, and budget. Second, an innovative meta-heuristic algorithm is proposed to identify proper controller locations based on factors like path reliability, controller processing capacity, propagation delay, and energy consumption. In the last step, load balancing and the reliability of the controllers are improved using queuing theory and a proposed greedy algorithm. ELA-RCP is evaluated with real-world topologies. The simulation demonstrates up to 25% improvement in energy consumption, up to 108% improvement in the network survivability, and up to 68% improvement in mean average delay rise compared to particle swarm optimization, firefly optimization algorithm, ant lion optimization, and seagull optimization algorithm.

A novel hybrid swarm intelligence algorithm for solving TSP and desired-path-based online obstacle avoidance strategy for AUV

Aiming at the problem that Ant Colony Optimization (ACO) is subject primarily to the parameters, we propose a hybrid algorithm SOA-ACO-2Opt to optimize the ACO parameter combination through Seagull Optimization Algorithm (SOA) to strengthen ACO’s search capability. To obtain a uniform initial distribution of the ACO parameter combination, we incorporated the Kent Chaos Map (KCM) to randomly initialize the seagull’s position, reducing the tendency of SOA to fall into the local optimum. To avoid slow calculation speed and premature convergence of ACO, we improved the adaptive multi-population mechanism to reduce repeated redundant calculations and used the ϵ−greedy and default strategy, respectively, to update the ants’ position. 2Opt is applied to find shorter paths in each iteration. In addition, when AUV navigates on the planned path, it may encounter obstacles. Therefore, this paper proposes an autonomous obstacle avoidance algorithm based on forward-looking sonar to ensure safety during tasks. SOA-ACO-2Opt is verified against twelve different problems extracted from TSPLIB and compared with some state-of-the-art algorithms. Furthermore, sea trials were carried out for several representative marine engineering applications of TSP and obstacle avoidance. Experimental results show that this work can significantly improve AUV’s work efficiency and intelligence and protect the AUV’s safety.

Jellyfish Search Optimized Gated Recurrent Unit Based FOPID/PID for Enhanced Automatic Voltage Regulator

The most essential mechanism for regulation in power systems is the Automatic Voltage Regulator (AVR) device, which maintains generator terminal voltage within predefined parameters. This research paper introduces an innovative approach to enhance AVR system performance through the ideal setting for a fractional-order PID (FOPID) controller that leverages Bidirectional Gate Recurrent Unit (BiGRU)-based deep learning techniques in conjunction with the Jellyfish Search Optimization (JSO) algorithm. To enhance flexibility and precision in control, the conventional proportional integral derivative (PID) controller is replaced with a FOPID controller, whose parameters are optimized by means of the JSO algorithm. The effectiveness of the proposed Jellyfish Search Optimization-based most advanced controllers (JSO-FOPID/PID) is compared with the existing FOPID controller implemented using the Seagull Optimization Algorithm (SOA-FOPID). Comprehensive analyses, including transient time, robustness, stability, and convergence analysis, are conducted subject to applying both stable and alternating load situations to validate the proposed approach. Experimental validation is performed using the MATLAB R2022a application with FOMCON tools and a laboratory prototype for testing. The simulations and experiments demonstrate that the projected AVR consistently delivers optimum dynamic responses and enhances overall stability when compared to existing methods, underscoring its efficacy in voltage regulation and power system control.

UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF.

Secondary salinization is a crucial constraint on agricultural progress in arid regions. The specific mulching irrigation technique not only exacerbates secondary salinization but also complicates field-scale soil salinity monitoring. UAV hyperspectral remote sensing offers a monitoring method that is high-precision, high-efficiency, and short-cycle. In this study, UAV hyperspectral images were used to derive one-dimensional, textural, and three-dimensional feature variables using Competitive adaptive reweighted sampling (CARS), Gray-Level Co-occurrence Matrix (GLCM), Boruta Feature Selection (Boruta), and Brightness-Color-Index (BCI) with Fractional-order differentiation (FOD) processing. Additionally, three modeling strategies were developed (Strategy 1 involves constructing the model solely with the 20 single-band variable inputs screened by the CARS algorithm. In Strategy 2, 25 texture features augment Strategy 1, resulting in 45 feature variables for model construction. Strategy 3, building upon Strategy 2, incorporates six triple-band indices, totaling 51 variables used in the model's construction) and integrated with the Seagull Optimization Algorithm for Random Forest (SOA-RF) models to predict soil electrical conductivity (EC) and delineate spatial distribution. The results demonstrated that fractional order differentiation highlights spectral features in noisy spectra, and different orders of differentiation reveal different hidden information. The correlation between soil EC and spectra varies with the order. 1.9th order differentiation is proved to be the best order for constructing one-dimensional indices; although the addition of texture features slightly improves the accuracy of the model, the integration of the three-waveband indices significantly improves the accuracy of the estimation, with an R2 of 0.9476. In contrast to the conventional RF model, the SOA-RF algorithm optimizes its parameters thereby significantly improving the accuracy and model stability. The optimal soil salinity prediction model proposed in this study can accurately, non-invasively and rapidly identify excessive salt accumulation in drip irrigation under membrane. It is of great significance to improve the growing conditions of cotton, increase the cotton yield, and promote the sustainable development of Xinjiang's agricultural economy, and also provides a reference for the prevention and control of regional soil salinization.

Smart predictive viscosity mixing of CO2–N2 using optimized dendritic neural networks to implicate for carbon capture utilization and storage

Crucial for carbon capture, utilization, and storage (CCUS) initiatives and diverse industries, heat transfer underscores the need for a precise assessment of carbon dioxide (CO2) and nitrogen (N2) viscosities in gaseous blends across various temperatures. This research pioneers an intelligent model by enhancing the dendritic neural regression (DNR) framework, employing the Seagull Optimization Algorithm with Marine Predator Algorithm (SOAMPA) for optimal predictions. Leveraging recent advancements in metaheuristic optimization techniques, the study reveals the superior performance of the novel SOAMPA approach in predictive accuracy, marking a significant breakthrough in predicting CO2-N2 mixture viscosities with implications for advancing CCUS projects and diverse industries. The optimized DNR model, empowered by the modified SOAMPA optimization technique, contributes to estimating the viscosity of N2-CO2 mixture gases. Utilizing inputs like pressure, temperature, mole fraction of N2, and model fraction of CO2, the models are trained and tested on a dataset comprising over 3030 data samples from public literature. Key contributions encompass proposing an optimized DNR approach, introducing the modified SOAMPA technique, and demonstrating its superiority over established optimization methods in conjunction with the traditional DNR model for predicting viscosity based on real experimental datasets.

A novel Elman neural network based on Gaussian kernel and improved SOA and its applications

To address challenges encountered in traditional Elman neural networks (ENNs), such as low convergence accuracy, difficulties in hyperparameter selection, and issues with gradient disappearance, a new ENN based on the Gaussian kernel and an improved seagull optimization algorithm (SOA) named GSENN is proposed. First, the principle of the ENN is introduced, and the effects of the network structure, parameters, and gradient descent algorithm on its output are analyzed. Building upon the analysis results, an input layer for the ENN using the Gaussian kernel is designed. The outstanding local feature extraction capability of the Gaussian kernel is used to enhance convergence accuracy, and the predicted trends are more in line with real data. In addition, we propose an SOA incorporating a nonlinear factor to optimize the weights and thresholds of the ENN. This approach aims to resolve the challenges in hyperparameter selection and problems of gradient vanishing in the ENN. Seven commonly used neural network structures and the latest improved techniques were chosen for comparative experiments across different datasets. The experimental results show that GSENN achieves a mean relative error rate of 4.286 %. The MSE results indicate a 45.600 % improvement over the original ENN and a 37.142 % improvement over BP neural networks. The experimental results demonstrate that GSENN exhibits efficient forecasting capability and offers reliable and effective forecasting for the relevant departments.

Seagull Optimization Algorithm with Share Creation with an Image Encryption Scheme for Secure Vehicular Ad Hoc Networks

Α Vehicular Ad hoc Network (VANET) allows transmission, amid moving or stationary vehicles via wireless technology. Amongst several problems, safe transmission is the most important one in smart VANETs in 5G networks. Smart vehicles require integration with advanced road systems encompassing smart payment and traffic control systems. Numerous security mechanisms are used in VANETs to ensure safe communication. One such mechanism is cryptographic digital signatures based on encryption. This study introduces the new seagull optimization algorithm involving share creation with an image encryption scheme (SGOA-SCIES) for secure VANET transmissions. The goal of the SGOA-SCIES technique is to create a considerable number of shares and encrypt them to accomplish security. In the SGOA-SCIES technique, a Multiple Share Creation (MSC) scheme is employed to generate numerous share sets. For the share encryption process, the SGOA-SCIES technique engages the Fractional-Order Chaotic System (FOCS) approach to encrypt the generated shares. The optimal keys of the FOCS method can be chosen by the SGOA usage, which ameliorates the security level. The performance evaluation of the SGOA-SCIES method is examined on benchmark data. The simulations demonstrate the enhanced SGOA-SCIES methodology outcome and compare it with the ones of other existing systems and under the implementation of various measures.

Seagull optimization algorithm for node localization in wireless sensor networks

Seagull optimization algorithm for node localization in wireless sensor networks

Short-term wind speed forecasts through hybrid model based on improved variational mode decomposition

ABSTRACT Scientific and accurate wind predictions are the basis for exploiting and utilizing wind energy. Combining the VMD and KELM, this research proposes a new hybrid model to capture the pattern of wind speed change, the parameters of which are optimized by the Spotted Hyena Optimizer(SHO) and Seagull Optimization Algorithm(SOA), respectively. The VMD is optimized by the SHO to achieve the purpose of adaptive decomposition of historical wind speed time series. After that, the parameters of the KELM are optimized by the SOA. Then, the decomposed wind speed series is input into the optimized KELM. The output prediction Intrinsic Mode Functions(IMFs) is added up to obtain the short-term wind speed prediction results. The measured wind speeds for four seasons were selected as the case study for the proposed model. The prediction results are compared with the measured wind speed series to verify the accuracy and reliability of the model. Besides, the prediction accuracy and stability of the proposed model are better than traditional prediction models, with stronger performance and lower computational cost. The results of case studies indicate that the proposed model can satisfy the need for actual applications.

Application of modified seagull optimization algorithm with archives in urban water distribution networks: Dealing with the consequences of sudden pollution load

This study focuses on the optimization of consequence management actions in the urban water distribution network. The EPANET simulation model is employed in combination with the multi-objective modified seagull optimization algorithm (MOMSOA) based on archives for a more efficient optimization process. Two objective functions are developed: minimizing reactive activities (cost reduction) and minimizing consumed pollution mass. The utilization of shut-off valves and hydrants for isolating the network and discharging pollution is explored. Without consequence management, 84.5 kg of pollution is consumed. With 18 reactive activities, pollution consumption was reduced to 59.8 kg. Also, to compare the proposed method with other algorithms, the interaction curve between reactive activities and the amount of pollutant mass consumed was obtained using other methods, including MOSOA, NSGA-II, MOPSO, and MOSMA. According to the obtained curve, the proposed method performed better in reducing the mass of consumed pollution. Extracting optimal activities using MOMSOA and a maximum of 18 activities takes about 80 min. The MOMSOA with archive technique significantly shortens this time for real-time consequence management. The proposed approach demonstrates that increasing the archive population decreases the extraction time of interaction curves between objectives by up to 60 %. A small archive capacity slightly increases the time required to extract optimal activities due to searching for similar solutions. However, utilizing the archive capacity enables real-time optimization and consequence management in the network.

Soil Moisture Prediction Model Based on Improved GRU Recurrent Neural Network

Soil moisture plays a crucial role in land water and energy cycles, and has a certain impact on weather and climate change. In agricultural production, crop moisture status can be determined based on soil moisture, and timely and effective irrigation strategies can be formulated to ensure grain yield while saving water resources, maximizing the value of agricultural water resource utilization, and achieving sustainable development. Therefore, the accuracy of soil moisture prediction has important research value for many fields such as agriculture and climate. In this paper, the super parameters of GRU Recurrent neural network are optimized by intelligent seagull optimization algorithm using a small number of influencing factors, namely, atmospheric temperature, atmospheric humidity, rainfall and soil moisture data, and a soil moisture prediction model is established. The model was used to predict soil moisture for the next 12 hours, 24 hours, 36 hours, and 48 hours, respectively. The final experiment showed that the model in this paper had better predictive effect on soil moisture, with the best predictive evaluation index data being MAPE (12h) = 4.4120%, R2 (12h) = 0.94605, and RMSE (12h) = 1.9998; By comparing the prediction results of multiple time steps vertically, it was found that the prediction accuracy of the model in this paper decreased more smoothly, meeting the requirements of soil moisture prediction.

Research on the purification mechanism of heavy metal pollution by biochar composites driven by degree learning

This paper proposes an innovative approach by integrating deep learning technology, specifically employing the GRU recurrent neural network model based on the Seagull optimization algorithm, to enhance the accuracy of predicting biochar performance. The Seagull optimization algorithm, inspired by seagull predatory behavior, is adept at efficiently identifying optimal model parameters, thereby improving the model’s generalization ability and robustness. The GRU recurrent neural network, designed for sequence data processing, proves to be instrumental in capturing dynamic and nonlinear interactions between biochar and heavy metals. This, in turn, contributes to heightened prediction accuracy and model interpretability. The article unfolds in a structured manner, beginning with an introduction to the biochar preparation method and its characteristics. It then delves into an analysis of the sources and hazards of heavy metal pollution. Following this, the paper explains the principles and advantages of deep learning technology, providing a comprehensive foundation for the subsequent discussion. The construction and verification process of the proposed model is then detailed, concluding with the presentation of experimental results and in-depth analysis. In essence, this research introduces a pioneering idea and methodology for optimizing biochar design and effectively controlling heavy metal pollution, presenting a fresh perspective on addressing these environmental challenges.

Construction and Implementation of Innovation and Entrepreneurship Education System in Higher Vocational Colleges in the New Era Based on VAR Modeling

Abstract This study is dedicated to exploring the construction and implementation of innovation and entrepreneurship education system in higher vocational colleges. By analyzing the relationship between education investment and industrial employment, it further enhances the innovation and entrepreneurship ability of students in higher vocational colleges. It promotes the synergistic development of education and economy. First, the relationship between higher education investment and industrial employment is analyzed by VAR model, which reveals the influence of education on the structural adjustment of industrial jobs. Second, the evaluation indexes and quality clustering of innovation and entrepreneurship education are refined through fuzzy cluster analysis and K-means clustering method based on seagull optimization algorithm. The research results show that the improved innovation and entrepreneurship education system can significantly improve students’ innovation and entrepreneurship satisfaction and self-evaluation, in which the indicators of entrepreneurial spirit and psychological quality are considerably enhanced. In addition, students’ innovation and entrepreneurship ability showed significant differences in multiple dimensions. This study confirms the effectiveness of the construction of innovation and entrepreneurship education system in higher vocational colleges, which is substantial in improving students’ innovation and entrepreneurship ability.