Metaheuristic swarm-based intelligent algorithms are extensively employed for engineering optimization tasks due to their efficacy in addressing nonlinear and high-dimensional challenges. This study presents an improv...
详细信息
Metaheuristic swarm-based intelligent algorithms are extensively employed for engineering optimization tasks due to their efficacy in addressing nonlinear and high-dimensional challenges. This study presents an improved snake optimization algorithm (SOEA) to overcome the limitations of the standard snake optimization algorithm (SOA), such as slow convergence, subpar optimization accuracy, and vulnerability to local optima. The integration of elite opposition-based learning strategy enables the adjustment of snake population positions, thereby enhancing the algorithm's global search capacity and iteration speed. Moreover, the incorporation of the adaptive threshold method enhances its local search performance and convergence speed. Experimental results demonstrate the superior performance of the proposed SOEA algorithm in achieving global optimization and accelerating convergence speed. The SOEA algorithm achieves a remarkable 34% reduction in the average number of iterations required compared to the SOA algorithm, and it also exhibits a lower mean squared error. Finally, the effectiveness of the proposed algorithm is validated through its successful application to solving the multi-UAV path planning problem.
The snake optimization algorithm (SO) is an efficient meta-heuristic algorithm. However, it still has insufficient convergence speed and accuracy when tackling complex problems. To address these shortcomings, this pap...
详细信息
The snake optimization algorithm (SO) is an efficient meta-heuristic algorithm. However, it still has insufficient convergence speed and accuracy when tackling complex problems. To address these shortcomings, this paper proposes an improved snake optimization algorithm based on Hybrid Strategy (HSO). During the population initialization phase, the study employs a good point set initialization method, resulting in a more uniform distribution of the initial population. Second, a nonlinear balance factor is introduced to better balance exploration and exploitation. Furthermore, the differential evolution strategy and L & eacute;vy flight strategy are introduced to enhance the algorithm's capability to escape local optima. To evaluate the effectiveness of the proposed strategies, this study conducted an ablation comparison experiment based on the CEC2022 benchmark functions and compared the HSO algorithm with several meta-heuristic algorithms. The results of the experiment were then statistically analyzed using the Friedman test and Wilcoxon signed-rank test. Finally, three engineering design problems were employed to assess the application value of HSO in practical problems. The findings demonstrate that HSO achieves significant improvements in optimization capability compared to SO, and outperforms the comparison algorithms.
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 pow...
详细信息
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.
Malware is a malicious software intended to cause damage to computer systems. In recent times, significant proliferation of malware utilized for illegal and malicious goals has been recorded. Several machine and deep ...
详细信息
Malware is a malicious software intended to cause damage to computer systems. In recent times, significant proliferation of malware utilized for illegal and malicious goals has been recorded. Several machine and deep learning methods are widely used for the detection and classification of malwares. Image-based malware detection includes the usage of machine learning and computer vision models for analyzing the visual representation of malware, including binary images or screenshots, for the purpose of detecting malicious behaviors. This techniques provides the potential to identify previously hidden or polymorphic malware variants based on the visual features, which provide a further layer of defense against emerging cyber-attacks. This study introduces a new snake optimization algorithm with Deep Convolutional Neural Network for Image-Based Malware Classification technique. The primary intention of the proposed technique is to apply a hyperparameter-tuned deep learning method for identifying and classifying malware images. Primarily, the ShuffleNet method is mainly used to derivate the feature vectors. Besides, the snake optimization algorithm can be deployed to boost the choice of hyperparameters for the ShuffleNet algorithm. For the recognition and classification of malware images, attention-based bi-directional long short-term memory model. The simulation evaluation of the proposed algorithm has been examined using the Malimg malware dataset. The experimental values inferred that the proposed methodology achieves promising performance with a maximum accuracy of 98.42% compared to existing models.
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 i...
详细信息
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. Further
To address the drawbacks of the traditional snakeoptimization method, such as a random population initialization, slow convergence speed, and low accuracy, an adaptive t-distribution mixed mutation snakeoptimization...
详细信息
To address the drawbacks of the traditional snakeoptimization method, such as a random population initialization, slow convergence speed, and low accuracy, an adaptive t-distribution mixed mutation snakeoptimization strategy is proposed. Initially, Tent-based chaotic mapping and the quasi-reverse learning approach are utilized to enhance the quality of the initial solution and the population initialization process of the original method. During the evolution stage, a novel adaptive t-distribution mixed mutation foraging strategy is introduced to substitute the original foraging stage method. This strategy perturbs and mutates at the optimal solution position to generate new solutions, thereby improving the algorithm's ability to escape local optima. The mating mode in the evolution stage is replaced with an opposite-sex attraction mechanism, providing the algorithm with more opportunities for global exploration and exploitation. The improved snakeoptimization method accelerates convergence and improves accuracy while balancing the algorithm's local and global exploitation capabilities. The experimental results demonstrate that the improved method outperforms other optimization methods, including the standard snakeoptimization technique, in terms of solution robustness and accuracy. Additionally, each improvement technique complements and amplifies the effects of the others.
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 optimizatio...
详细信息
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 snakeoptimization (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.
A gas outburst prediction model based on multiple strategy fusion and improved snake optimization algorithm (MFISO) and temporal convolutional network (TCN) is proposed to address the problems of low accuracy of deep ...
详细信息
A gas outburst prediction model based on multiple strategy fusion and improved snake optimization algorithm (MFISO) and temporal convolutional network (TCN) is proposed to address the problems of low accuracy of deep learning prediction models for gas outburst in underground mines. By adopting the phase space reconstruction method, the time series of multiple complex influencing factors related to gas outburst were reconstructed and used as model inputs. Sine chaos mapping, spiral search strategy and snake dynamic adaptive weight are introduced to improve the snake optimization algorithm (SO), which enhances the local optimal escape capability and global search capability of the algorithm. The tangent-based rectified linear unit (ThLU) was used to improve the rectified linear unit (ReLU) of the standard TCN to strengthen the generalization capability of the model. The MFISO algorithm was used to optimize the relevant hyperparameters of the improved TCN model to optimize the accuracy of gas outburst prediction. The TCN, GRU, LSTM, SO-TCN, WOA-TCN, and PSO-TCN prediction models were selected to compare the prediction performance with the MFISO-TCN gas outburst prediction model, and the results showed that the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) of the MFISO-TCN model were 3.11%, 0.47% and 3.31% are lower than those of other models, which verifies that the method of this paper effectively intensifies the performance of gas outburst prediction model in underground mines.
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 com...
详细信息
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 the paradigm of mobile edge computing (MEC), providing low-latency and high-reliability services for users is garnering increasing attention. Appropriate edge-server placement is the crucial first step to realizing...
详细信息
In the paradigm of mobile edge computing (MEC), providing low-latency and high-reliability services for users is garnering increasing attention. Appropriate edge-server placement is the crucial first step to realizing such services, as it can meet computation requirements and enhance resource utilization. This study delves into efficient and intelligent dynamic edge-server placement by taking into account time-varying network scenarios and deployment costs. Firstly, edge servers are classified into static and dynamic ones. Subsequently, an improved snake optimization algorithm is proposed to determine the number and placement locations of dynamic servers while adhering to delay requirements. Finally, a minimum placement-cost algorithm is put forward to further reduce the service cost. Experimental results demonstrate that compared to classic algorithms, the proposed algorithms can achieve a reduction in latency of 5% to 12%. And compared to the state-of-the-art methods, they can reduce service costs by 20% to 43%. This research offers an effective solution for dynamic edge-server placement and holds great theoretical and practical significance.
暂无评论