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A great quantity of data is being created these days, which is kept in massive datasets with different irrelevant attributes that are unrelated to the goal notion. Feature selection deals with the selection of most pertinent features that also aid to increase the classification accuracy. The topic of feature selection is viewed as a multi-objective optimization problem with two goals: improving classification accuracy and reducing the number of features used. Drone Squadron Optimization (DSO) is one of the most recent artifactinspired optimization algorithm; having two key components: semi-autonomous drones that hover over a terrain and a command center that manages the drones. In this paper, two binary variants of the DSO are proposed to deal with the feature selection problem. The proposed binary algorithms are applied on 21 different benchmark datasets against five state-of-the-art algorithms, i.e., Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Flower Pollination Algorithm (FPA), Genetic Algorithm (GA) and Ant Lion Optimization (ALO). Different assessment indicators are used to assess the diversification and intensification of the optimization algorithms. When compared to current state-of-the-art wrapper-based algorithms, the suggested binary techniques are more efficient in scanning the dimension space and picking the most useful characteristics for categorization tasks, resulting in the lowest classification error rate.

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