Binary Peacock Algorithm: A Novel Metaheuristic Approach for Feature Selection

Abstract

Binary metaheuristic algorithms prove to be invaluable for solving binary optimization problems. This paper proposes a binary variant of the peacock algorithm (PA) for feature selection. PA, a recent metaheuristic algorithm, is built upon lekking and mating behaviors of peacocks and peahens. While designing the binary variant, two major shortcomings of PA (lek formation and offspring generation) were identified and addressed. Eight binary variants of PA are also proposed and compared over mean fitness to identify the best variant, called binary peacock algorithm (bPA). To validate bPA’s performance experiments are conducted using 34 benchmark datasets and results are compared with eight well-known binary metaheuristic algorithms. The results show that bPA classifies 30 datasets with highest accuracy and extracts minimum features in 32 datasets, achieving up to 99.80% reduction in the feature subset size in the dataset with maximum features. bPA attained rank 1 in Friedman rank test over all parameters.