Dwarf Mongoose Optimization Algorithm

Agushaka, Jeffrey O.; Ezugwu, Absalom E.; Abualigah, Laith

doi:10.1016/j.cma.2022.114570

Cited by 465 publications

(69 citation statements)

References 50 publications

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“…In addition, the ADMM algorithm used in this paper cannot guarantee to find the global optimum of the model. Therefor, another successive research is to combine some other algorithms, such as nature-inspired heuristic algorithms 51 – 54 , arithmetic optimization algorithms 55 .…”

Section: Discussionmentioning

confidence: 99%

A nonconvex $$\hbox{TV}_q-l_1$$ regularization model and the ADMM based algorithm

Fang

Tang

et al. 2022

Sci Rep

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The total variation (TV) regularization with $$l_1$$ l 1 fidelity is a popular method to restore the image contaminated by salt and pepper noise, but it often suffers from limited performance in edge-preserving. To solve this problem, we propose a nonconvex $$\hbox{TV}_q-l_1$$ TV q - l 1 regularization model in this paper, which utilizes a nonconvex $$l_q$$ l q -norm $$(0<q<1)$$ ( 0 < q < 1 ) defined in total variation (TV) domain (called $$\hbox{TV}_q$$ TV q regularizer) to regularize the restoration, and uses $$l_1$$ l 1 fidelity to measure the noise. Compared to the traditional TV model, the proposed model can more effectively preserve edges and contours since it provides a more sparse representation of the restoration in TV domain. An alternating direction method of multipliers (ADMM) combining with majorization-minimization (MM) scheme and proximity operator is introduced to numerically solve the proposed model. In particular, a sufficient condition for the convergence of the proposed algorithm is provided. Numerical results validate the proposed model and algorithm, which can effectively remove salt and pepper noise while preserving image edges and contours. In addition, compared with several state-of-the-art variational regularization models, the proposed model shows the best performance in terms of peak signal to noise ratio (PSNR) and mean structural similarity index (MSSIM). We can obtain about 0.5 dB PSNR and 0.06 MSSIM improvements against all compared models.

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Section: Discussionmentioning

confidence: 99%

A nonconvex $$\hbox{TV}_q-l_1$$ regularization model and the ADMM based algorithm

Fang

Tang

et al. 2022

Sci Rep

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“…These test functions are categorized into three main categories, unimodal, multimodal, and multimodal, with fixed dimension functions. They are always used in the domain of machine learning and optimization algorithms [30,31].…”

Section: Experimental Settingsmentioning

confidence: 99%

Hybrid Reptile Search Algorithm and Remora Optimization Algorithm for Optimization Tasks and Data Clustering

Almotairi

Abualigah

2022

Symmetry

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Data clustering is a complex data mining problem that clusters a massive amount of data objects into a predefined number of clusters; in other words, it finds symmetric and asymmetric objects. Various optimization methods have been used to solve different machine learning problems. They usually suffer from local optimal problems and unbalance between the search mechanisms. This paper proposes a novel hybrid optimization method for solving various optimization problems. The proposed method is called HRSA, which combines the original Reptile Search Algorithm (RSA) and Remora Optimization Algorithm (ROA) and handles these mechanisms’ search processes by a novel transition method. The proposed HRSA method aims to avoid the main weaknesses raised by the original methods and find better solutions. The proposed HRSA is tested on solving various complicated optimization problems—twenty-three benchmark test functions and eight data clustering problems. The obtained results illustrate that the proposed HRSA method performs significantly better than the original and comparative state-of-the-art methods. The proposed method overwhelmed all the comparative methods according to the mathematical problems. It obtained promising results in solving the clustering problems. Thus, HRSA has a remarkable efficacy when employed for various clustering problems.

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“…There are several ways to validate classification results. The hold-out method simply divides the data set into two different sets of training and testing; the leave-one-out method produces sets of N-sized samples randomly using sampling with substitution, as opposed to previous approaches that used samples without substitution [82,83].…”

Section: Classificationmentioning

confidence: 99%

Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches

Gharaibeh

Alzu’bi

Abdullah

et al. 2022

BDCC

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Plenty of disease types exist in world communities that can be explained by humans’ lifestyles or the economic, social, genetic, and other factors of the country of residence. Recently, most research has focused on studying common diseases in the population to reduce death risks, take the best procedure for treatment, and enhance the healthcare level of the communities. Kidney Disease is one of the common diseases that have affected our societies. Sectionicularly Kidney Tumors (KT) are the 10th most prevalent tumor for men and women worldwide. Overall, the lifetime likelihood of developing a kidney tumor for males is about 1 in 466 (2.02 percent) and it is around 1 in 80 (1.03 percent) for females. Still, more research is needed on new diagnostic, early, and innovative methods regarding finding an appropriate treatment method for KT. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of machine learning can save diagnosis time, improve test accuracy, and reduce costs. Previous studies have shown that deep learning can play a role in dealing with complex tasks, diagnosis and segmentation, and classification of Kidney Tumors, one of the most malignant tumors. The goals of this review article on deep learning in radiology imaging are to summarize what has already been accomplished, determine the techniques used by the researchers in previous years in diagnosing Kidney Tumors through medical imaging, and identify some promising future avenues, whether in terms of applications or technological developments, as well as identifying common problems, describing ways to expand the data set, summarizing the knowledge and best practices, and determining remaining challenges and future directions.

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Dwarf Mongoose Optimization Algorithm

Cited by 465 publications

References 50 publications

A nonconvex $$\hbox{TV}_q-l_1$$ regularization model and the ADMM based algorithm

A nonconvex $$\hbox{TV}_q-l_1$$ regularization model and the ADMM based algorithm

Hybrid Reptile Search Algorithm and Remora Optimization Algorithm for Optimization Tasks and Data Clustering

Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches

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