A Modified Reptile Search Algorithm for Numerical Optimization Problems

Yuan, Qihang; Zhang, Yongde; Dai, Xuesong; Zhang, Shu

doi:10.1155/2022/9752003

Cited by 10 publications

(2 citation statements)

References 56 publications

(144 reference statements)

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“…The Algorithm exhibits versatility as it can address multiple issues, including unconstrained and constrained problems, single-objective and multi-objective problems, and difficulties involving continuous and discrete variables. Many fields, including finance, image, and signal processing [13], engineering [14], renewable energy [15], machine learning, and many others, have adopted it due to its exceptional efficiency and robustness. This is because it outperforms other popular optimization methods, has a sufficiently fast execution time, and has a good quality convergence rate.…”

Section: Introductionmentioning

confidence: 99%

Reptile Search Algorithm for Association Rule Mining

Boukhalat,

Heraguemi,

Benouis

et al. 2024

IJCDS

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Association rule mining (ARM) is a very popular, engaging, and active research area in data mining. It seeks to find valuable connections between different attributes in a defined dataset. ARM, which describes it as an NP-complete problem, creates a fertile field for optimization applications. The Reptile Search Algorithm (RSA) is an innovative evolutionary algorithm. It yanks stimulation from the encircling and hunting conducts of crocodiles. It is a well-known optimization technique for solving NP-complete issues. Since its introduction by Abualigah et al. in 2022, the approach has attracted considerable attention from researchers and has extensively been used to address diverse optimization issues in several disciplines. This is due to its satisfactory execution speed, efficient convergence rate, and superior effectiveness compared to other widely recognized optimization methods. This paper suggests a new version of the reptile search algorithm for resolving the association rules mining challenge. Our proposal inherits the trade-off between local and global search optimization issues demonstrated by the Reptile search algorithm. To illustrate the power of our proposal, a sequence of experiments is taken out on a varied, well-known, employing multiple comparison criteria. The results show an evident dominance of the proposed approach in the front of the famous association rules mining algorithms as well as Bees Swarm Optimization (BSO), Bat Algorithm (BA), Whale Optimization Algorithm (WOA), and others regarding CPU time, fitness criteria, and the quality of generated rules.

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Section: Introductionmentioning

confidence: 99%

Reptile Search Algorithm for Association Rule Mining

Boukhalat,

Heraguemi,

Benouis

et al. 2024

IJCDS

View full text Add to dashboard Cite

show abstract

“…It is also a scalable, easy-to-use, and sound algorithm, making it suitable for various real-world problems. However, like other metaheuristic algorithms, RSA's performance may also be affected by the problem's size and complexity, leading to premature convergence, due to a lack of balance between exploration and exploitation capabilities [26]. To overcome these limitations, the problem-specific knowledge embedded in the search space should be considered, and the optimization structure of RSA should be appropriately adjusted.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Feature Selection Optimization for COVID-19 Microarray Data

Krishanthi,

Jayetileke,

et al. 2023

COVID

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The utilization of gene selection techniques is crucial when dealing with extensive datasets containing limited cases and numerous genes, as they enhance the learning processes and improve overall outcomes. In this research, we introduce a hybrid method that combines the binary reptile search algorithm (BRSA) with the LASSO regression method to effectively filter and reduce the dimensionality of a gene expression dataset. Our primary objective was to pinpoint genes associated with COVID-19 by examining the GSE149273 dataset, which focuses on respiratory viral (RV) infections in individuals with asthma. This dataset suggested a potential increase in ACE2 expression, a critical receptor for the SARS-CoV-2 virus, along with the activation of cytokine pathways linked to COVID-19. Our proposed BRSA method successfully identified six significant genes, including ACE2, IFIT5, and TRIM14, that are closely related to COVID-19, achieving an impressive maximum classification accuracy of 87.22%. By conducting a comparative analysis against four existing binary feature selection algorithms, we demonstrated the effectiveness of our hybrid approach in reducing the dimensionality of features, while maintaining a high classification accuracy. As a result, our hybrid approach shows great promise for identifying COVID-19-related genes and could be an invaluable tool for other studies dealing with very large gene expression datasets.

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A novel reinforcement learning-based reptile search algorithm for solving optimization problems

Ghetas,

Issa

2023

Neural Comput & Applic

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This work proposes a novel reptile search algorithm (RSA) to solve optimization problems called reinforcement reptile search algorithm (RLRSA). The basic RSA performs exploitation through highly walking in the first half of searching process while the exploration phase is executed through the hunting phase in the second half. Therefore, the algorithm is not able to balance exploration and exploitation and this behavior results in trapping in local optima. A novel learning method based on reinforcement learning and Q-learning model is proposed to balance the exploitation and exploration phases when the solution starts deteriorating. Furthermore, the random opposite-based learning (ROBL) is introduced to increase the diversity of the population and so enhance the obtained solutions. Twenty-three typical benchmark functions, including unimodal, multimodal and fixed-dimension multimodal functions, were employed to assess the performance of RLRSA. According to the findings, the RLRSA method surpasses the standard RSA approach in the majority of benchmark functions evaluated, specifically in 12 out of 13 unimodal functions, 9 out of 13 multimodal functions, and 8 out of 10 fixed multimodal functions. Furthermore, the RLRSA is applied to vessel solve pressure and tension/compression spring design problems. The results show that RLRSA significantly found the solution with minimum cost. The experimental results reveal the superiority of the RLRSA compared to RSA and other optimization methods in the literature.

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A Modified Reptile Search Algorithm for Numerical Optimization Problems

Cited by 10 publications

References 56 publications

Reptile Search Algorithm for Association Rule Mining

Reptile Search Algorithm for Association Rule Mining

Enhancing Feature Selection Optimization for COVID-19 Microarray Data

A novel reinforcement learning-based reptile search algorithm for solving optimization problems

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