Evidence map›Paper›PMID 41290769›Full record

ArticleScientific reports2025

Chaotic Lévy and adaptive restart enhance the Manta Ray foraging optimizer for gene feature selection.

Shamsuddeen Adamu, Hitham Alhussian, Said Jadid Abdulkadir, Ayed Alwadain, Sallam O F Khairy, Hussaini Mamman, Ismail Said Almuniri, Al Waleed Sulaiman Al Abri, Zaid Fawaz Jarallah, Hamood Saif Hamood Al Fahdi and 2 more

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Shamsuddeen AdamuDepartment of Computing, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia. shamsu200@yahoo.com.
Hitham AlhussianDepartment of Computing, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia.
Said Jadid AbdulkadirDepartment of Computing, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia.
Ayed AlwadainComputer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia.
Sallam O F KhairyDepartment of Information Systems, College of Economics, Management and Information Systems, University of Nizwa, Nizwa, Oman.
Hussaini MammanDepartment of Computing, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia.
Ismail Said AlmuniriDepartment of Information Systems, College of Economics, Management and Information Systems, University of Nizwa, Nizwa, Oman.
Al Waleed Sulaiman Al AbriDepartment of Information Systems, College of Economics, Management and Information Systems, University of Nizwa, Nizwa, Oman.
Zaid Fawaz JarallahIAIICT, Ahmadu Bello University, Zaria, Nigeria.
Hamood Saif Hamood Al FahdiComputer Science Department, Aljabel Alakhder Sultanate of Oman, Muscat, Oman.
Maged NasserDepartment of Computing, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia.
Bander Ali Saleh Al-RimySchool of Computing, University of Portsmouth, Portsmouth, UK, England.

Funding

Ongoing Research Funding program, King Saud University, Riyadh, Saudi Arabia. ORF-2025-309Yayasan UTP 015PBC-028
6 · The paper itself

Abstract

Swarm-based optimization algorithms often face challenges in maintaining an effective exploration-exploitation balance in high-dimensional search spaces. Manta Ray Foraging Optimization (MRFO), while competitive, is hindered by static parameter settings and premature convergence. This study introduces CLA-MRFO, an adaptive variant incorporating chaotic Lévy flight modulation, phase-aware memory, and an entropy-informed restart strategy to enhance search dynamics. On the CEC'17 benchmark suite, CLA-MRFO achieved the lowest mean error on 23 of 29 functions, with an average performance gain of 31.7% over the next best algorithm; statistical validation via the Friedman test confirmed the significance of these results ([Formula: see text]). To examine practical utility, CLA-MRFO was applied to a high-dimensional leukemia gene selection task, where it identified ultra-compact subsets (≤5% of original features) of biologically coherent genes with established roles in leukemia pathogenesis. These subsets enabled a mean F

Indexed as

AlgorithmsComputational BiologyLeukemiaHumansBioinformaticsFeature selectionGene expression classificationHybrid optimization algorithmsLévy flight and chaotic mapsManta Ray foraging optimizationMetaheuristic optimizationMulti-objective optimization

Identifiers

PMID41290769
PMCPMC12647646

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