Improved Equilibrium Optimizer using Density-based Population and Entropy Operator for Feature Selection

As the number of features in practical applications grows, the feature selection process becomes increasingly important. Since the feature selection problem is NP-hard, no exact algorithm can determine the optimal subset over a reasonable period of time. However, traditional feature selection methods are time-consuming and tend to get stuck in local optima. Unlike traditional search techniques, metaheuristics are more effective at exploring and exploiting the search domain because they use several operators. Besides these behaviors of metaheuristics, we present an improved Equilibrium Optimizer algorithm using the density of population and entropy operator, which has proven a good exploration ability to provide a promising candidate solution. After that, a new feature selection model is developed based on the improved Equilibrium Optimizer algorithm. K-nearest neighbors is used as an evaluator for the new solutions. In order to test the performance of the proposed algorithm, simulation experiments are conducted on a set of 14 standard test functions containing both unimodal and multimodal functions. To evaluate the effectiveness of the proposed algorithm, 15 UCI benchmark datasets and five metaheuristics, GA, CS, GSA, RDA, and BBA  are applied. The experimental results revealed the effectiveness of our approach in terms of accuracy performance for the feature selection process.