ArticleScientific reports2024
CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 47 citations in OpenAlex.
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- Federated multi-label text feature selection via manifold-aware sparse modeling and cooperative grey wolf optimization.Scientific reports · 2026Article
- Chaotic enhanced leader slime mold algorithm for dome structures with frequency constraints.Scientific reports · 2025Article
- Q-Learning-Driven Butterfly Optimization Algorithm for Green Vehicle Routing Problem Considering Customer Preference.Biomimetics (Basel, Switzerland) · 2025Article
- Multi-Strategy Enhanced Parrot Optimizer: Global Optimization and Feature Selection.Biomimetics (Basel, Switzerland) · 2024Article
- Enhanced artificial hummingbird algorithm with chaotic traversal flight.Scientific reports · 2024Article
- Research on hybrid strategy Particle Swarm Optimization algorithm and its applications.Scientific reports · 2024Article
- IRIME: Mitigating exploitation-exploration imbalance in RIME optimization for feature selection.iScience · 2024Article
- A multi-strategy improved rime optimization algorithm for three-dimensional USV path planning and global optimization.Scientific reports · 2024Article
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Authors and funding
3 authors at 2 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9-100%), sensitivity (77.6-100%), precision (91.8-96.08%), specificity (91.6-100%) and F-Score (90-100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC'2022 benchmarks functions.
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