Feature selection via Lèvy Antlion optimization

In this paper, a modification of the newly proposed antlion optimization (ALO) is introduced and applied to feature selection relied on the Lèvy flights. ALO method is one of the encouraging swarm intelligence algorithms which make use of random walking to perform the exploration and exploitation operations. Random walks based on uniform distribution is responsible for premature convergence and stagnation. A Lèvy flight random walk is suggested as a permutation for performing a local search. Lèvy random walking grants the optimization ability to generate several solutions that are apart from existing solutions and furthermore enables it to escape from local minima and much efficient in examining large search area. The proposed Lèvy antlion optimization (LALO) algorithm is applied in a wrapper-based mode to select optimal feature combination that maximizing classification accuracy while minimizing the number of selected features. LALO algorithm is applied on 21 different benchmark datasets against genetic algorithm (GA), particle swarm optimization (PSO), and the native ALO methods. Different initialization methods and several evaluation criteria are employed to assess algorithm diversification and intensification of the optimization algorithms. The experimental results demonstrate the significant improvement in the proposed LALO over the native ALO and many well-known methods used in feature selection.