Discrete equilibrium optimizer combined with simulated annealing for feature selection

Abstract

This paper proposes a binary adaptation of the recently proposed meta-heuristic, Equilibrium Optimizer (EO), called Discrete EO (DEO), to solve binary optimization problems. A U-shaped transfer function is used to map the continuous values of EO into the binary domain. To further improve the exploitation capability of DEO, Simulated Annealing (SA) is used as a local search procedure and the combination is named as DEOSA. The proposed DEOSA algorithm is applied to 18 well-known UCI datasets and compared with a wide range of algorithms. The results are statistically validated using Wilcoxon rank-sum test and Friedman test. In order to test the scalability and robustness of DEOSA, it is additionally tested over seven high-dimensional Microarray datasets and 25 binary Knapsack problems. The results evidently demonstrate the superiority and merits of DEOSA when solving binary optimization problems.