  Swarm optimization algorithms are widely used for feature selection before
data mining and machine learning applications. The metaheuristic
nature-inspired feature selection approaches are used for single-objective
optimization tasks, though the major problem is their frequent premature
convergence, leading to weak contribution to data mining. In this paper, we
propose a novel feature selection algorithm named Reinforced Swarm Optimization
(RSO) leveraging some of the existing problems in feature selection. This
algorithm embeds the widely used Bee Swarm Optimization (BSO) algorithm along
with Reinforcement Learning (RL) to maximize the reward of a superior search
agent and punish the inferior ones. This hybrid optimization algorithm is more
adaptive and robust with a good balance between exploitation and exploration of
the search space. The proposed method is evaluated on 25 widely known UCI
datasets containing a perfect blend of balanced and imbalanced data. The
obtained results are compared with several other popular and recent feature
selection algorithms with similar classifier configurations. The experimental
outcome shows that our proposed model outperforms BSO in 22 out of 25 instances
(88%). Moreover, experimental results also show that RSO performs the best
among all the methods compared in this paper in 19 out of 25 cases (76%),
establishing the superiority of our proposed method.
