Authors:
Mohamed Wajdi Ouertani
1
;
Raja Oueslati
1
and
Ghaith Manita
1
;
2
Affiliations:
1
Laboratory MARS, LR17ES05, ISITCom, Sousse University, Sousse, Tunisia
;
2
ESEN, Manouba University, Manouba, Tunisia
Keyword(s):
Feature Selection, Optimization, Elk Herd Optimiser, Distance Balance Mechanism.
Abstract:
This research paper introduces an enhanced version of the Binary Elk Herd Optimizer (BEHO), integrated with a Fitness Distance Balance (FDB) mechanism called FDB-BEHO, tailored for high-dimensional optimization tasks. This study evaluates the performance of FDB-BEHO across multiple gene expression datasets, focusing on feature selection in bioinformatics—a domain characterized by complex, high-dimensional data. The FDB mechanism is designed to prevent premature convergence by maintaining an optimal balance between exploration and exploitation, utilizing a diversity measure that adjusts dynamically based on the fitness-distance correlation among solutions. Comparative analyses demonstrate that FDB-BEHO surpasses traditional meta-heuristic algorithms in fitness values and classification accuracy and reduces the number of selected features, thereby enhancing model simplicity and interpretability. These results validate the effectiveness of FDB-BEHO in navigating complex solution spaces
efficiently and underscore its potential applicability in other domains requiring robust feature selection capabilities. The study’s findings suggest that incorporating diversity-enhancing mechanisms like FDB can significantly improve the performance of binary optimization algorithms, offering promising directions for future research in optimization technology.
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