This study initiates the implementation of fractional-order (FO) fuzzy (F) PID (FOFPID) controller fine-tuned using a seagull optimization algorithm (SOA) for the study of load frequency control (LFC). Initially, the ...
详细信息
This study initiates the implementation of fractional-order (FO) fuzzy (F) PID (FOFPID) controller fine-tuned using a seagull optimization algorithm (SOA) for the study of load frequency control (LFC). Initially, the SOA-tuned FOFPID regulator is implemented on the widely utilized model of dual-area reheat-thermal system (DARTS), named test system-1 in this work for a perturbation of 10% step load (10% SLP) on area-1. Dynamical analysis of the DARTS system reveals the viability of the SOA-tuned FOFPID control scheme in regulating frequency deviations effectively compared to other control schemes covered in the literature. Later, the presented regulator is implemented on the multi-area diverse sources (MADS) system possessing realistic constraints in this study, termed test system-2. The sovereignty of the presented FOFPID controller is once again evidenced with controllers of PID/FOPID/FPID fine-tuned with the SOA approach. Moreover, the effect of considering practical realistic nonlinearity constraints such as communication time delays (CTDs) on MADS system performance is visualized and the necessity of its consideration is demonstrated. Furthermore, AC-DC lines are incorporated with the MADS system to enhance the performance under heavy-load disturbances and the robustness of the proposed regulatory mechanism is deliberated.
In the field of network intrusion detection based on a neural network with Gated Recurrent Units (GRU), the accuracy of classification decreases due to the improper setting of the parameters. Therefore an Improved Sea...
详细信息
ISBN:
(纸本)9781665426053
In the field of network intrusion detection based on a neural network with Gated Recurrent Units (GRU), the accuracy of classification decreases due to the improper setting of the parameters. Therefore an Improved seagull optimization algorithm (ISOA) to optimize GRU parameters has been proposed. The ISOA is proposed to address the shortcomings of the seagull optimization algorithm (SOA) of low convergence accuracy and slow convergence speed in dealing with global optimization problems. The algorithm uses an opposition-based learning method instead of a random method to initialize the population so that the population has a better diversity. To balance the overall and local search capability of the algorithm, in this paper, we design a special factor update formula that converges nonlinearly with the number of iterations, allowing the algorithm to better jump out of locally optimal solutions and speed up convergence. The improved algorithm is used to obtain better parameters and uses them to set the parameters of the GRU to build a better-performing classification model. Multiple UCI datasets are used and compared with other methods and finally validated using the NSL-KDD datasets for network intrusion detection. The experimental results show that compared to the performance of long short-term memory(LSTM) units, multilayer perceptron(MLP), and the traditional neural network with GRU, the classification accuracy of the method with ISOA for optimizing GRU is improved on each dataset, which helps to improve the classification performance in network intrusion detection.
Sign Language Recognition (SLR) covers the ability to translate Sign Language (SL) signals into written or spoken languages. This technique is useful for hearing-impaired people by offering them an effective method to...
详细信息
ISBN:
(纸本)9798331540661;9798331540678
Sign Language Recognition (SLR) covers the ability to translate Sign Language (SL) signals into written or spoken languages. This technique is useful for hearing-impaired people by offering them an effective method to communicate with persons having trouble in recognizing SLs. It can also be employed for generating automatic captions in real-time for live actions and videos. Various models of SLR include machine and deep learning, and computer vision techniques. A commonly employed technique involves using a camera to capture the body and hand movements of the signer, processing the video data to identify the signs. One of the high tasks of SLR includes the flexibility in SL over numerous individuals and cultures, the complex of definite signs, and the need for real-time process. This manuscript presents a SL Recognition Using an Improved seagull optimization algorithm with Deep Learning (SLR-ISOADL) methodology. The SLR-ISOADL approach aims to exploit a hyperparameter-tuned DL model to recognize and classify the SLs. In the SLR-ISOADL approach, a bilateral filtering (BF) approach can be applied to get rid of the noise. For learning and deriving intrinsic patterns, the SLR-ISOADL approach employs the AlexNet model. Besides, the ISOA can be applied for optimal hyperparameter election of the AlexNet model. Finally, the multilayer perceptron (MLP) technique can be exploited to detect and classify the SLs. The analytical experiment of the SLR-ISOADL technique is conducted on a benchmark dataset. The investigational analysis highlighted that the SLR-ISOADL technique gains enhanced detection outcomes in terms of distinct measures.
In this paper,an enhanced seagull optimization algorithm based on a double group evolutionary mechanism(DSOA) is proposed for photovoltaic(PV) cell parameter *** the DSOA,a double group search strategy and time-based ...
详细信息
ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
In this paper,an enhanced seagull optimization algorithm based on a double group evolutionary mechanism(DSOA) is proposed for photovoltaic(PV) cell parameter *** the DSOA,a double group search strategy and time-based mutation strategy are added to the population renewal mechanism of the conventional *** performance of DSOA on standard benchmark functions is surprising;it was good at accuracy and exploitation,also convergence *** with SOA and the other two classical intelligent optimizationalgorithms,the performance of DSOA is comprehensively *** is adopted to estimate PV cell *** simulation results demonstrate that estimated currents of circuit model based on DSOA have high similarity to the measured value of the photovoltaic cell.
Aiming at the defects of slow convergence and easy to fall into local optimum of the seagull optimization algorithm, this paper proposes an improved seagull optimization algorithm incorporating golden sine and chaotic...
详细信息
ISBN:
(数字)9781665458641
ISBN:
(纸本)9781665458641
Aiming at the defects of slow convergence and easy to fall into local optimum of the seagull optimization algorithm, this paper proposes an improved seagull optimization algorithm incorporating golden sine and chaotic perturbation of tent mapping. This algorithm enhances the global search ability through tent chaotic disturbance and Levy flight, accelerates the convergence through golden sine to improve the local search ability. In this paper, the original fixed convergence factor is transformed into a nonlinear decreasing convergence factor to improve the optimization efficiency. The performance is tested on the benchmark functions, and it is used to solving the multiprocessor task scheduling problem. Compared with other algorithms, experiments show that TGSOA has significant improvement over other algorithms in terms of convergence speed and robustness.
Aiming at the defects of seagull optimization algorithm (SOA) in solving optimization problems, such as local optimization, slow convergence speed and low optimization accuracy, a seagull optimization algorithm (SCSOA...
详细信息
ISBN:
(纸本)9781665478960
Aiming at the defects of seagull optimization algorithm (SOA) in solving optimization problems, such as local optimization, slow convergence speed and low optimization accuracy, a seagull optimization algorithm (SCSOA) based on integration of improved Sobol sequence and Cauchy variation is proposed. First, initialize the population using the Sobol sequence to make the seagulls more evenly distributed in the initial solution space;Secondly, use a nonlinear function to replace the original convergence factor to guide the seagull to always maintain a larger body degree of freedom during the search process, enhance the global search ability, and avoid falling into the local optimum;Then, the Cauchy variation strategy is introduced, so that the individual can better find the optimal solution and enhance the ability of the algorithm to jump out of the local optimum;Finally, use the benchmark function to test the improved algorithm, and compare it with the original algorithm and the experimental results of other algorithms. The results show that SCSOA performs better in convergence speed and optimization accuracy, and the global optimization capability is also improved.
In the stage of emergency supplies reserve, in order to ensure the smooth implementation of the emergency supplies work plan, the problem of vehicle routing for emergency vehicles is studied. Firstly, a vehicle routin...
详细信息
ISBN:
(纸本)9798400709784
In the stage of emergency supplies reserve, in order to ensure the smooth implementation of the emergency supplies work plan, the problem of vehicle routing for emergency vehicles is studied. Firstly, a vehicle routing model for emergency vehicles with the lowest total cost including carbon emission cost is constructed. Secondly, a strategy is introduced to control parameter A by incorporating a nonlinear decreasing inverse S-shaped function into the attack behavior part of the basic seagull optimization algorithm, and the seagull optimization algorithm is improved. Finally, based on the classic Solomon test data, random data is selected to verify the algorithm and model, and compared with the basic seagull optimization algorithm. The experimental results show that the improved seagull optimization algorithm has better convergence than the basic seagull optimization algorithm, indicating that the model and algorithm have good applicability and practical value.
Accurate load forecasting is conducive to the formulation of the power generation plan, lays the foundation for the formulation of quotation, and provides the basis for the power management system and distribution man...
详细信息
Accurate load forecasting is conducive to the formulation of the power generation plan, lays the foundation for the formulation of quotation, and provides the basis for the power management system and distribution management system. This study aims to propose a high precision load forecasting method. The power load forecasting model, based on the Improved seagull optimization algorithm, which optimizes SVM (ISOA-SVM), is constructed. First, aiming at the problem that the random selection of internal parameters of SVM will affect its performance, the Improved seagull optimization algorithm (ISOA) is used to optimize its parameters. Second, to solve the slow convergence speed of the seagull optimization algorithm (SOA), three strategies are proposed to improve the optimization performance and convergence accuracy of SOA, and an ISOA algorithm with better optimization performance and higher convergence accuracy is proposed. Finally, the load forecasting model based on ISOA-SVM is established by using the Mean Square Error (MSE) as the objective function. Through the example analysis, the prediction performance of the ISOA-SVM is better than the comparison models and has good prediction accuracy and effectiveness. The more accurate load forecasting can provide guidance for power generation and power consumption planning of the power system.
Estimating parameters and establishing high-accuracy and high-reliability models of photovoltaic (PV) modules by using the actual current-voltage data is important to simulate, model, and optimize the PV systems. Seve...
详细信息
Estimating parameters and establishing high-accuracy and high-reliability models of photovoltaic (PV) modules by using the actual current-voltage data is important to simulate, model, and optimize the PV systems. Several meta-heuristic optimization techniques have been developed to estimate the parameters of the solar PV models. However, it is still a challenging task to accurately, reliably, and quickly estimate the unknown parameters of PV models. This paper proposes a novel hybrid seagull optimization algorithm (HSOA) for estimating the unknown parameters of PV models effectively and accurately. In proposed HSOA, the personal historical best information is embedded into position search equation to improve the solution precision. A novel nonlinear escaping energy factor based on cosine function is presented for balancing global exploration and local exploitation. The differential mutation strategy is introduced to escape from the local optima. We firstly select twelve classical benchmark test functions to investigate the feasibility of HSOA, and experimental results show that HSOA is superior to most compared methods. Then, HSOA is used for solving parameters estimation problem of three benchmark solar PV models. The comparison results demonstrate that HSOA is superior to BOA, GWO, WOA, HHO, SOA, EEGWO, and ISCA on solution quality, convergence and reliability.(c) 2022 Elsevier Ltd. All rights reserved.
The post-epidemic era has led to the accumulation of cargo, which has brought greater pressure to container ports. Since traditional methods cannot simultaneously consider the effect of tidal, uncertain, and environme...
详细信息
The post-epidemic era has led to the accumulation of cargo, which has brought greater pressure to container ports. Since traditional methods cannot simultaneously consider the effect of tidal, uncertain, and environmental factors on the allocation plan. To relieve this pressure, firstly, considering tidal factors, formulating time window rules, thinking out uncertain factors, and determining constraints from three perspectives of vessel berthing process, quay crane and container truck operation, a new berth-quay crane-truck joint scheduling model is constructed by minimizing three aspects of vessels turnaround time, the carbon emissions of quay cranes and trucks, namely TEU-BQCT model. Then, aiming at obtaining a relatively high-quality solution, combining chaotic mapping and quantum entanglement, a new chaotic quantum adaptive seagull optimization algorithm is proposed, namely CQASOA, exclusive coding rules suitable for the TEU-BQCT model is formulated, a feasible integer algorithm is developed, the external penalty function is constructed to limit constraints, and a novel joint scheduling solution method of berth-quay crane-truck is proposed, namely TEU-BQCT_CQASOA. Subsequently, two ports of different scales in South China are used to test the constructed solution method feasibility. The simulation results indicate that the constructed TEU-BQCT model can obtain a more suitable scheduling scheme. The proposed CQASOA has better performance than other comparison algorithms selected in this paper, which can obtain a better solution when solving the TEU-BQCT model.
暂无评论