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

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...

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主要な著者:	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, Maged Nasser, Bander Ali Saleh Al-Rimy
フォーマット:	Artigo
言語:	Inglês
出版事項:	Nature Portfolio 2025-11-01
シリーズ:	Scientific Reports
主題:	Metaheuristic optimization Manta Ray foraging optimization Feature selection Lévy flight and chaotic maps Gene expression classification Multi-objective optimization
オンライン･アクセス:	https://doi.org/10.1038/s41598-025-25766-y
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