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<p>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 (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(p &lt; 0.01\)</EquationSource> </InlineEquation>). To examine practical utility, CLA-MRFO was applied to a high-dimensional leukemia gene selection task, where it identified ultra-compact subsets (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\le\)</EquationSource> </InlineEquation>5% of original features) of biologically coherent genes with established roles in leukemia pathogenesis. These subsets enabled a mean F<sub>1</sub>-score of <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(0.953 \pm 0.012\)</EquationSource> </InlineEquation> under a stringent 5-fold nested cross-validation across six classification models. While highly effective in a binary classification setting, the method’s performance in a multi-class diagnostic context revealed constraints in generalizability, indicating that the identified biomarkers are highly context-dependent. Overall, CLA-MRFO exhibited consistent behavior (&lt;5% variance across runs) and provides an adaptable framework for high-dimensional optimization tasks with applications extending to bioinformatics and related domains.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月25日

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  • 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

摘要

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 ( \(p < 0.01\) ). To examine practical utility, CLA-MRFO was applied to a high-dimensional leukemia gene selection task, where it identified ultra-compact subsets ( \(\le\) 5% of original features) of biologically coherent genes with established roles in leukemia pathogenesis. These subsets enabled a mean F1-score of \(0.953 \pm 0.012\) under a stringent 5-fold nested cross-validation across six classification models. While highly effective in a binary classification setting, the method’s performance in a multi-class diagnostic context revealed constraints in generalizability, indicating that the identified biomarkers are highly context-dependent. Overall, CLA-MRFO exhibited consistent behavior (<5% variance across runs) and provides an adaptable framework for high-dimensional optimization tasks with applications extending to bioinformatics and related domains.