Binary Artificial Electric Field Algorithm

Artificial Electric Field Algorithm (AEFA) is one of the recent population-based optimization techniques, it is inspired by the electrostatic force theory. This article aims to design a novel binary version of the AEFA scheme to improve the performance of the original AEFA scheme in discrete space. The popular S-shaped and V-shaped functions are used to design the binary versions of the AEFA. The efficiency and the optimization ability of the proposed binary versions of the AEFA are studied theoretically as well as experimentally. An extensive experimental study is performed to understand the performance of the proposed schemes. A set of 24 benchmark problems are solved using binary versions of AEFA the experimental results are compared with nine state-of-the-art algorithms. The running time complexity and Wilcoxon's signed-rank statistical test are also conducted to judge the proposed algorithms. In addition to the experimental studies, theoretical analysis is also carried out which suggests the convergence scenario of the proposed schemes. These studies suggest that the designed binary versions of the AEFA are very efficient and competent in addressing difficult optimization problems.