Atom Search Optimization with Simulated Annealing -- a Hybrid Metaheuristic Approach for Feature Selection
This is an incremental improvement for researchers in optimization and feature selection, potentially enhancing model efficiency in domains like digit recognition and emotion recognition.
The authors tackled feature selection by proposing a hybrid metaheuristic combining Atom Search Optimization and Simulated Annealing, achieving higher classification accuracy and fewer selected features compared to recent wrapper-based methods on 25 datasets.
'Hybrid meta-heuristics' is one of the most interesting recent trends in the field of optimization and feature selection (FS). In this paper, we have proposed a binary variant of Atom Search Optimization (ASO) and its hybrid with Simulated Annealing called ASO-SA techniques for FS. In order to map the real values used by ASO to the binary domain of FS, we have used two different transfer functions: S-shaped and V-shaped. We have hybridized this technique with a local search technique called, SA We have applied the proposed feature selection methods on 25 datasets from 4 different categories: UCI, Handwritten digit recognition, Text, non-text separation, and Facial emotion recognition. We have used 3 different classifiers (K-Nearest Neighbor, Multi-Layer Perceptron and Random Forest) for evaluating the strength of the selected featured by the binary ASO, ASO-SA and compared the results with some recent wrapper-based algorithms. The experimental results confirm the superiority of the proposed method both in terms of classification accuracy and number of selected features.