Mathematical programming and meta-heuristics are two types of optimization methods. Meta-heuristic algorithms can identify optimal/near-optimal solutions by mimicking natural behaviours or occurrences and provide benefits such as simplicity of execution, a few parameters, avoidance of local optimization, and flexibility. Many meta-heuristic algorithms have been introduced to solve optimization issues, each of which has advantages and disadvantages. Studies and research on presented meta-heuristic algorithms in prestigious journals showed they had good performance in solving hybrid, improved and mutated problems. This paper reviews the sparrow search algorithm (SSA), one of the new and robust algorithms for solving optimization problems. This paper covers all the SSA literature on variants, improvement, hybridization, and optimization. According to studies, the use of SSA in the mentioned areas has been equal to 32%, 36%, 4%, and 28%, respectively. The highest percentage belongs to Improved, which has been analyzed by three subsections: Meat-Heuristics, artificial neural networks, and Deep Learning. 

https://link.springer.com/content/pdf/10.1007/s11831-022-09804-w.pdf

Advances in Sparrow Search Algorithm: A Comprehensive Survey

Time-series production forecasting method based on the integration of Bidirectional Gated Recurrent Unit (Bi-GRU) network and Sparrow Search Algorithm (SSA)

Review and empirical analysis of sparrow search algorithm

As a recent swarm intelligence optimization algorithm, sparrow search algorithm (SSA) is widely adopted in many real-world problems. However, the solutions to the limitations of SSA (such as low accuracy of convergence and tendency of trapping into local optimum) are still not available. To address these issues, we propose an enhanced multi-strategies sparrow search algorithm (EMSSA) based on three strategies specifically addressing the limitations of SSA: 1) in the uniformity-diversification orientation strategy, we propose an adaptive-tent chaos theory to allow more diversity and greater randomness in the initial population; 2) in the hazard-aware transfer strategy, we construct a weighted sine and cosine algorithm based on the growth function to avoid trapping into the state of local optima stagnation; 3) in the dynamic evolutionary strategy, we design the similar perturbation function and introduce the triangle similarity theory to improve the exploration capability. The performance of EMSSA in solving the continuous optimization problems about the 23 benchmark functions, CEC2014, and CEC2017 problems is much improved than that of SSA and other state-of-the-art algorithms. Furthermore, the results of the density peak clustering optimization show that the EMSSA outperforms SSA. 

Enhancing sparrow search algorithm via multi-strategies for continuous optimization problems

Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019–2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA. 

/pdf/nature-inspired-optimization-algorithms-and-their-uxb2g0vl.pdf

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

Precise and timely detection of brain tumor area has a very high effect on the selection of medical care, its success rate and following the disease process during treatment. Existing algorithms for brain tumor diagnosis have problems in terms of better performance on various brain images with different qualities, low sensitivity of the results to the parameters introduced in the algorithm and also reliable diagnosis of tumors in the early stages of formation. A computer aided system is proposed in this research for automatic brain tumors diagnosis. The method includes four main parts: pre-processing and segmentation techniques, features extraction and final categorization. Gray-level co-occurrence matrix (GLCM) and Discrete Wavelet Transform (DWT) were applied for characteristic extraction of the MR images which are then injected to an optimized convolutional neural network (CNN) for the final diagnosis. The CNN is optimized by a new design of Sparrow Search Algorithm classification (ESSA). Finally, a comparison of the results of the method with three state of the art technique on the Whole Brain Atlas (WBA) database to show its higher efficiency.

An optimal brain tumor detection by convolutional neural network and Enhanced Sparrow Search Algorithm.

In recent years, the diagnosis of brain tumors with the help of magnetic resonance imaging (MRI) methods has received significant attention. MRI techniques with substantial capabilities of displaying the internal structures of the human body have become one of the most widely used methods in this field. In the present study, a tumor is segmented after effectively preprocessing MRI images. Then, the main features are mined using a combination of the gray‐level cooccurrence matrix and discrete wavelet transform. Finally, the mined features are fed into an optimized convolutional neural network (CNN)‐based classification using a new improved metaheuristic technique, called balanced sparrow search algorithm (BSSA) for the final diagnosis to improve the efficiency of the CNN concerning consistency and accuracy. To verify the efficacy of the recommended algorithm, it is implemented on the whole brain atlas (WBA) database, and the results are compared with certain new and well‐known methods. A comparative result also has been performed to the study, and the results show that the highest accuracy achieved by the recommended BSSA‐CNN system is 93.65%. In addition, it is demonstrated that the specificity of 65.07% in the presented method yields results that are significantly better than those of the competing methods.

Optimal brain tumor diagnosis based on deep learning and balanced sparrow search algorithm

Purpose To observe and analyze the clinical and multi-mode imaging features of eyes with PHOMS, and to introduce two cases of PHOMS which underwent multi-mode imaging. Methods Retrospective clinical observational study. A total of 26 patients (37 eyes) with hyperreflective structures surrounded by hyporeflective edges around the optic discs who were examined and diagnosed at Shandong Eye Hospital between January 2019 and June 2021 were included in the study. Among these patients, 12 were male and 14 were female. Fifteen were monocular. The average age was 39 years. All patients underwent the following examinations: Best-corrected visual acuity (BCVA), intraocular pressure examinations, slit-lamp anterior segment examinations, indirect ophthalmoscopy, visual field examinations, fundus color photography, fundus autofluorescence (FAF), optical coherence tomography (OCT), and optical coherence tomography angiography (OCTA). Some of the patients were examined with fundus fluorescein angiography (FFA). Clinical data and imaging characteristics from the OCT, OCTA, and FFA were analyzed retrospectively. Results We found the hyperreflective structures surrounded by hyporeflective edges around the optic discs in 37 eyes. EDI-OCT results revealed hyperreflective structures surrounded by hyporeflective edges around the optic discs in all eyes. Typical hyperreflexia lesions occurred around the optic disc, located subretinally and above Bruch's membrane. OCTA revealed that the highly reflective perioptic material also had vascular structures. Conclusion EDI-OCT of PHOMS showed hyperreflective structures surrounded by hyporeflective edges around all of the optic discs. Infra-red photography showed temporal hyperreflexia. These characteristics can be seen in a variety of diseases and may be a relatively common feature revealed by EDI-OCT scanning. These characteristics may also be seen in elderly patients as well as children. PHOMS may be found in optic disc drusen (ODD), tilted disc syndrome (TDS), optic neuritis, ischemic optic neuropathy, and in white dot syndromes. Few patients may be developed into macular neovascularization (MNV). In order to improve the accuracy and robustness of the conclusions and provide better clinical guidance, we need to conduct more comprehensive research in the subsequent clinical work.

/pdf/clinical-and-multi-mode-imaging-features-of-eyes-with-19wfdl9v.pdf

Clinical and Multi-Mode Imaging Features of Eyes With Peripapillary Hyperreflective Ovoid Mass-Like Structures

Imaging findings of sex cord tumor with annular tubules: a case description

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An optimal brain tumor detection by convolutional neural network and Enhanced Sparrow Search Algorithm.

Optimal brain tumor diagnosis based on deep learning and balanced sparrow search algorithm

Clinical and Multi-Mode Imaging Features of Eyes With Peripapillary Hyperreflective Ovoid Mass-Like Structures

Imaging findings of sex cord tumor with annular tubules: a case description