Chaotic Levy 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 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

SUBMITTER: Adamu S 

PROVIDER: S-EPMC12647646 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature