Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization

Xu, Minghai; Cao, Li; Lu, Dongwan; Hu, Zhongyi; Yue, Yinggao

doi:10.3390/biomimetics8020235

Cited by 17 publications

(7 citation statements)

References 158 publications

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“…Xu et al [45] provide a thorough study that expands the application of bio-inspired algorithms in image processing. The review covers a range of swarm intelligence optimization techniques, such as Ant Colony Optimization (ACO) and WOA.…”

Section: Engagement Of Aco and Woa In Sld Identificationmentioning

confidence: 99%

Ant Colony and Whale Optimization Algorithms Aided by Neural Networks for Optimum Skin Lesion Diagnosis: A Thorough Review

Mukhlif,

Ramaha,

Hameed

et al. 2024

Mathematics

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The adoption of deep learning (DL) and machine learning (ML) has surged in recent years because of their imperative practicalities in different disciplines. Among these feasible workabilities are the noteworthy contributions of ML and DL, especially ant colony optimization (ACO) and whale optimization algorithm (WOA) ameliorated with neural networks (NNs) to identify specific categories of skin lesion disorders (SLD) precisely, supporting even high-experienced healthcare providers (HCPs) in performing flexible medical diagnoses, since historical patient databases would not necessarily help diagnose other patient situations. Unfortunately, there is a shortage of rich investigations respecting the contributory influences of ACO and WOA in the SLD classification, owing to the recent adoption of ML and DL in the medical field. Accordingly, a comprehensive review is conducted to shed light on relevant ACO and WOA functionalities for enhanced SLD identification. It is hoped, relying on the overview findings, that clinical practitioners and low-experienced or talented HCPs could benefit in categorizing the most proper therapeutical procedures for their patients by referring to a collection of abundant practicalities of those two models in the medical context, particularly (a) time, cost, and effort savings, and (b) upgraded accuracy, reliability, and performance compared with manual medical inspection mechanisms that repeatedly fail to correctly diagnose all patients.

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Section: Engagement Of Aco and Woa In Sld Identificationmentioning

confidence: 99%

Ant Colony and Whale Optimization Algorithms Aided by Neural Networks for Optimum Skin Lesion Diagnosis: A Thorough Review

Mukhlif,

Ramaha,

Hameed

et al. 2024

Mathematics

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“…Therefore, the input and output model of the kernel extreme learning machine is as shown in Formula (5).…”

Section: Kernel Extreme Learning Machinementioning

confidence: 99%

“…In engineering applications, learning for data classification is very desirable. Traditional learning techniques, such as artificial neural networks (ANN) and support vector machines (SVM), appear helpless in the face of the twin needs of quicker training speed and greater learning accuracy [ 4 , 5 ]. Huang et al’s extreme learning machine (ELM) is a single-layer feedforward network learning technique (single-hidden layer feedforward neural networks, SLFNs) that does not need changing the hidden layer neuron network settings.…”

Section: Introductionmentioning

confidence: 99%

Towards an Optimal KELM Using the PSO-BOA Optimization Strategy with Applications in Data Classification

et al. 2023

Self Cite

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The features of the kernel extreme learning machine—efficient processing, improved performance, and less human parameter setting—have allowed it to be effectively used to batch multi-label classification tasks. These classic classification algorithms must at present contend with accuracy and space–time issues as a result of the vast and quick, multi-label, and concept drift features of the developing data streams in the practical application sector. The KELM training procedure still has a difficulty in that it has to be repeated numerous times independently in order to maximize the model’s generalization performance or the number of nodes in the hidden layer. In this paper, a kernel extreme learning machine multi-label data classification method based on the butterfly algorithm optimized by particle swarm optimization is proposed. The proposed algorithm, which fully accounts for the optimization of the model generalization ability and the number of hidden layer nodes, can train multiple KELM hidden layer networks at once while maintaining the algorithm’s current time complexity and avoiding a significant number of repeated calculations. The simulation results demonstrate that, in comparison to the PSO-KELM, BBA-KELM, and BOA-KELM algorithms, the PSOBOA-KELM algorithm proposed in this paper can more effectively search the kernel extreme learning machine parameters and more effectively balance the global and local performance, resulting in a KELM prediction model with a higher prediction accuracy.

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“…As a kind of bio-inspired heuristic algorithm, Swarm intelligence algorithms (SIA) are bio-inspired heuristic algorithms that simulate the evolutionary patterns, behavioral characteristics, or thinking modes of insects, birds, or other populations 23 . Among many commonly used SIAs, the Sparrow Search Algorithm (SSA) 24 is known for its high search accuracy, fast convergence speed, good stability, and robustness.…”

Section: 、 Introductionmentioning

confidence: 99%

Salinity prediction in Qiantang Estuary based on Improved LSTM model

Zheng,

Sun,

Jiao

et al. 2024

Preprint

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Investigating saltwater intrusion is vital for optimal use of estuarine water resources. Presently, diverse data-driven models, mainly neural network models, have been employed to predict tidal estuarine salinity. However, the high nonlinearity, randomness, and instability of salinity sequences pose challenges for accurate estuarine salinity forecasting. In this paper, a multi-factor salinity prediction model using an enhanced Long Short-Term Memory (LSTM) network was proposed, based on measured data from Cangqian and Qibao stations in the Qiantang Estuary during 2011-2012. To improve prediction accuracy, input variables of the model were determined through Grey Relational Analysis (GRA) combined with estuarine dynamic analysis, and hyperparameters were optimized using a multi-strategy Improved Sparrow Search Algorithm (ISSA). Additionally, the model was applied to forecast salinity under different runoff conditions, analyzing the sensitivity of salinity to upstream discharge. Experimental result shows that compared to other models (BP, GRU, LSTM, SSA-LSTM), the new proposed ISSA-LSTM model has smaller errors and higher prediction accuracy, with NSE improving over 5% and other metrics (MAP, MAPE, RMSE) improving over 10%. Thus, the model provides a practical solution for the rapid and precise prediction of estuarine salinity.

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Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization

Cited by 17 publications

References 158 publications

Ant Colony and Whale Optimization Algorithms Aided by Neural Networks for Optimum Skin Lesion Diagnosis: A Thorough Review

Ant Colony and Whale Optimization Algorithms Aided by Neural Networks for Optimum Skin Lesion Diagnosis: A Thorough Review

Towards an Optimal KELM Using the PSO-BOA Optimization Strategy with Applications in Data Classification

Salinity prediction in Qiantang Estuary based on Improved LSTM model

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