Hybrid guided artificial bee colony algorithm for numerical function optimization

Abstract

Many different earning algorithms used for getting high performance in mathematics and statistical tasks. Recently, an Artificial Bee Colony (ABC) developed by Karaboga is a nature inspired algorithm, which has been shown excellent performance with some standard algorithms. The hybridization and improvement strategy made ABC more attractive to researchers. The two famous improved algorithms are: Guided Artificial Bee Colony (GABC) and Gbest Guided Artificial Bee Colony (GGABC), are used the foraging behaviour of the gbest and guided honey bees for solving optimization tasks. In this paper, GABC and GGABC methods are hybrid and so-called Hybrid Guided Artificial Bee Colony (HGABC) algorithm for strong discovery and utilization processes. The experiment results tested with sets of numerical benchmark functions show that the proposed HGABC algorithm outperforms ABC, PSO, GABC and GGABC algorithms in most of the experiments.