Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation

Liu, Qingxin; Li, Ni; Jia, Heming; Qi, Qi; Abualigah, Laith

doi:10.3390/math10071014

Cited by 60 publications

(13 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The free travel stage includes two strategies namely SFO strategy and Experience attack strategy. In SFO strategy, the remora population is attached to swordfish and makes travel [50][51][52]. This behavior is mathematically modelled as presented in equation (7).…”

Section: Preliminaries Of Sae and Romentioning

confidence: 99%

Local Entropy Based Remora Optimization and Sparse Autoencoders for Cancer Diagnosis Through Microarray Gene Expression Analysis

Bharanidharan,

Chakravarthy,

Venkatesan

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Gene expression analysis can be used as a tool to detect cancer and the type of cancer in its early stages. However, the computational complexity for gene expression analysis is quite high due to the large amount of numerical data. Hence, dimensionality reduction techniques are employed to reduce the computational complexity, which in turns reduces the accuracy of cancer diagnosis. This research work focuses on the development of computerized tools with reduced computational complexity and increased accuracy to identify cancer cells in five different regions, namely breast, liver, lung, prostate, and renal. Sparse autoencoder is implemented to reduce feature dimensions, and Remora Optimization is used to increase the performance of cancer diagnosis system through transformation of features. The local entropybased fitness function is utilized to evaluate the appropriateness of population in Remora Optimization algorithm, and it is considered as the important reason for performance improvement in the proposed cancer diagnosis system. 1027 patients are taken into consideration in this analysis of the microarray datasets, which were gathered from the Curated Microarray Database. Six distinct metrics are used to examine the classification performance, and four different supervised classifiers are investigated. When the suggested sparse autoencoder and local entropy-based Remora Optimization are applied, the average balanced accuracy of the examined classifiers is 93.4%. The average dimensions are decreased from 36045 to 80 dimensions using the suggested method. In the absence of the suggested methodology, the vanilla classifiers yield an average balanced accuracy of 82.7%.

show abstract

Section: Preliminaries Of Sae and Romentioning

confidence: 99%

Local Entropy Based Remora Optimization and Sparse Autoencoders for Cancer Diagnosis Through Microarray Gene Expression Analysis

Bharanidharan,

Chakravarthy,

Venkatesan

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The main idea of lens opposition-based learning comes from the principle of convex lens imaging. The search range is expanded by generating a reverse position based on the current coordinates [39], which can be seen in Figure 5. In two-dimensional coordinates, the search range of the x-axis is (a, b) and the y-axis represents a convex lens.…”

Section: Lens Opposition-based Learningmentioning

confidence: 99%

“…current coordinates [39], which can be seen in Figure 5. In two-dimensional coordinates, the search range of the x-axis is (a, b) and the y-axis represents a convex lens.…”

Section: Lens Opposition-based Learningmentioning

confidence: 99%

Modified Sand Cat Swarm Optimization Algorithm for Solving Constrained Engineering Optimization Problems

Rao

Wen

et al. 2022

Mathematics

Self Cite

View full text Add to dashboard Cite

The sand cat swarm optimization algorithm (SCSO) is a recently proposed metaheuristic optimization algorithm. It stimulates the hunting behavior of the sand cat, which attacks or searches for prey according to the sound frequency; each sand cat aims to catch better prey. Therefore, the sand cat will search for a better location to catch better prey. In the SCSO algorithm, each sand cat will gradually approach its prey, which makes the algorithm a strong exploitation ability. However, in the later stage of the SCSO algorithm, each sand cat is prone to fall into the local optimum, making it unable to find a better position. In order to improve the mobility of the sand cat and the exploration ability of the algorithm. In this paper, a modified sand cat swarm optimization (MSCSO) algorithm is proposed. The MSCSO algorithm adds a wandering strategy. When attacking or searching for prey, the sand cat will walk to find a better position. The MSCSO algorithm with a wandering strategy enhances the mobility of the sand cat and makes the algorithm have stronger global exploration ability. After that, the lens opposition-based learning strategy is added to enhance the global property of the algorithm so that the algorithm can converge faster. To evaluate the optimization effect of the MSCSO algorithm, we used 23 standard benchmark functions and CEC2014 benchmark functions to evaluate the optimization performance of the MSCSO algorithm. In the experiment, we analyzed the data statistics, convergence curve, Wilcoxon rank sum test, and box graph. Experiments show that the MSCSO algorithm with a walking strategy and a lens position-based learning strategy had a stronger exploration ability. Finally, the MSCSO algorithm was used to test seven engineering problems, which also verified the engineering practicability of the proposed algorithm.

show abstract

“…Opposition-based learning (OBL) is a new computational intelligence scheme [30]. In the past few years, OBL has been successfully applied to various population-based evolutionary algorithms [31][32][33][34][35]. Random opposition-based learning increases the random value of [0, 1] on the basis of opposition-based learning, so as to obtain the random solution within the range of inverse solution.…”

Section: Random Opposition-based Learningmentioning

confidence: 99%

A Modified Group Teaching Optimization Algorithm for Solving Constrained Engineering Optimization Problems

Rao

Jia

et al. 2022

Mathematics

Self Cite

View full text Add to dashboard Cite

The group teaching optimization algorithm (GTOA) is a meta heuristic optimization algorithm simulating the group teaching mechanism. The inspiration of GTOA comes from the group teaching mechanism. Each student will learn the knowledge obtained in the teacher phase, but each student’s autonomy is weak. This paper considers that each student has different learning motivations. Elite students have strong self-learning ability, while ordinary students have general self-learning motivation. To solve this problem, this paper proposes a learning motivation strategy and adds random opposition-based learning and restart strategy to enhance the global performance of the optimization algorithm (MGTOA). In order to verify the optimization effect of MGTOA, 23 standard benchmark functions and 30 test functions of IEEE Evolutionary Computation 2014 (CEC2014) are adopted to verify the performance of the proposed MGTOA. In addition, MGTOA is also applied to six engineering problems for practical testing and achieved good results.

show abstract

Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation

Cited by 60 publications

References 74 publications

Local Entropy Based Remora Optimization and Sparse Autoencoders for Cancer Diagnosis Through Microarray Gene Expression Analysis

Local Entropy Based Remora Optimization and Sparse Autoencoders for Cancer Diagnosis Through Microarray Gene Expression Analysis

Modified Sand Cat Swarm Optimization Algorithm for Solving Constrained Engineering Optimization Problems

A Modified Group Teaching Optimization Algorithm for Solving Constrained Engineering Optimization Problems

Contact Info

Product

Resources

About