A novel explanatory hybrid artificial bee colony algorithm for numerical function optimization

• Main Authors: Jarrah, Muath Ibrahim, Mohamad Jaya, Abdul Syukor, Mohd Abid, Mohd Asyadi Azam, Alqattan, Zakaria N., Azam, Mohd Asyadi, Abdullah, Rosni, Jarrah, Hazim, Abu‑Khadrah, Ahmed Ismail
• Format: Article
• Language: English
• Published: Springer Nature 2020
• Online Access: http://eprints.utem.edu.my/id/eprint/24751/2/2020%20A%20NOVEL%20EXPLANATORY%20HYBRID%20ARTIFICIAL%20BEE%20COLONY%20ALGORITHM%20FOR%20NUMERICAL%20FUNCTION%20OPTIMIZATION.PDF; http://eprints.utem.edu.my/id/eprint/24751/; https://link.springer.com/article/10.1007/s11227-019-03083-2
• Institution: Universiti Teknikal Malaysia Melaka

Over the past few decades, there has been a surge of interest of using swarm intelligence (SI) in computer-aided optimization. SI algorithms have demonstrated their efcacy in solving various types of real-world optimization problems. However, it is impossible to fnd an optimization algorithm that can obtain the global optimum for every optimization problem. Therefore, researchers extensively try to improve methods of solving complex optimization problems. Many SI search algorithms are widely applied to solve such problems. ABC is one of the most popular algorithms in solving diferent kinds of optimization problems. However, it has a weak local search performance where the equation of solution search in ABC performs good exploration, but poor exploitation. Besides, it has a fast convergence and can therefore be trapped in the local optima for some complex multimodal problems. In order to address such issues, this paper proposes a novel hybrid ABC with outstanding local search algorithm called β-hill climbing (βHC) and denoted by ABC–βHC. The aim is to improve the exploitation mechanism of the standard ABC. The proposed algorithm was experimentally tested with parameters tuning process and validated using selected benchmark functions with diferent characteristics, and it was also evaluated and compared with well-known state-of-the-art algorithms. The evaluation process was investigated using diferent common measurement metrics. The result showed that the proposed ABC–βHC had faster convergence in most benchmark functions and outperformed eight algorithms including the original ABC in terms of all the selected measurement metrics. For more validation, Wilcoxon’s rank sum statistical test was conducted, and the p values were found to be mostly less than 0.05, which demonstrates that the superiority of the proposed ABC–βHC is statistically signifcant.