Self-organizing migrating algorithm: review, improvements and comparison

Skanderová, Lenka

doi:10.1007/s10462-022-10167-8

Cited by 8 publications

(3 citation statements)

References 122 publications

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“…The first is to improve the algorithm itself by suggesting better control parameters such as SOMA with clustering-aided migration [24] and leader selection in SOMA [25], and reorganizing the mechanism the algorithm operates such as self-adapting SOMA [26] and the ensemble of strategies and perturbation parameter in SOMA [27]. The second is to combine SOMA with other algorithms such as GA and PSO to form hybridization versions like [28] and [29], please refer to [30] for more details.…”

Section: A the Soma Algorithmmentioning

confidence: 99%

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Swarm and Evolutionary Algorithms in Image Compression by F-Transform

Linh

Diep

2023

IEEE Access

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This article investigates the application of swarm and evolutionary algorithms, namely the SOMA, DE, and GA, for optimizing the F-transform-based image compression. To do this, we introduce the cost function, evaluating the approximation of decompressed image to the original image, concerning the parameters that control the approximation quality of the F-transform. This function is then minimized by the selected algorithms to find optimal settings for image compression and decompression. We design experiments to compare the performance of the original F-transform method and the methods optimized by SOMA, DE, and GA on a dataset of 10 pictures. In all considered cases, the results obtained with the optimized method completely surpass those obtained by the original one. We also apply a statistical test (called Wilcoxon signed-rank test) for ranking the performance of selected algorithms in this issue. The results show that the SOMA and DE perform well in cases where the compressed image sizes are small. However, the GA algorithm shows outperformance in comparison with the others in more complicated cases where the compressed image size is bigger. The outperformance of the GA is in terms of decompression quality and computation time. Finally, we provide a visual comparison between the original F-transformbased method and the method optimized by the GA, tested on a 128 × 128 picture. The decompressed image by the latter is much sharper and more detailed than that obtained by the former.

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Section: A the Soma Algorithmmentioning

confidence: 99%

“…The development and success of the above algorithms are summarized in publications [30], [32], and [43]. In-depth readers can refer to them for more details.…”

Section: The De Algorithmmentioning

confidence: 99%

Swarm and Evolutionary Algorithms in Image Compression by F-Transform

Linh

Diep

2023

IEEE Access

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“…In addition, a sequential likelihood ratio test method was also used to validate the efficacy and results of the suggested system. For SCADA security, some of the recent meta-heuristic optimization techniques are developed in the existing works, which includes hybrid PSO [29], Whale Optimization (WO) [30], Self-Organizing Migration (SOM) [31], Differential Evolution (DE) [32], and Gradient Descent [33]. Sheng et al [34] employed a new cyber-physical model for identifying intrusions from the smart grid SCADA networks.…”

Section: Related Workmentioning

confidence: 99%

A security model for smart grid SCADA systems using stochastic neural network

Rabie

Shitharth

Alghazzawi

et al. 2023

IET Generation Trans & Dist

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Detection of cyber‐threats in the smart grid Supervisory Control and Data Acquisition (SCADA) is still remains one of the complex and essential processes need to be highly concentrated in present times. Typically, the SCADA is more prone to the security issues due to their environmental problems and vulnerabilities. Therefore, the proposed work intends to design a new detection approach by integrating the optimization and classification models for smart grid SCADA security. In this framework, the min‐max normalization is performed at first for noise removal and attributes arrangement. Here, the correlation estimation mechanism is mainly deployed to reduce the dimensionality of features by choosing the relevant features used for attack prediction. Moreover, the optimal features are selected by using the optimal solution provided by the Holistic Harris Hawks Optimization (H3O). Finally, the Perceptron Stochastic Neural Network (PSNN) is utilized to categorize the normal and attacking data flow in the network with minimal processing time and complexity. By using the combination of proposed H3O‐PSNN technique, the detection accuracy is improved up to 99% for all datasets used in this study, and also other measures such as precision to 99.2%, recall to 99%, f1‐score to 99.2% increased, when compared to the standard techniques.

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Review and empirical analysis of sparrow search algorithm

Yue

Cao

et al. 2023

Artif Intell Rev

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Self-organizing migrating algorithm: review, improvements and comparison

Cited by 8 publications

References 122 publications

Swarm and Evolutionary Algorithms in Image Compression by F-Transform

Swarm and Evolutionary Algorithms in Image Compression by F-Transform

A security model for smart grid SCADA systems using stochastic neural network

Review and empirical analysis of sparrow search algorithm

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