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

Jarrah, Muath Ibrahim and Mohamad Jaya, Abdul Syukor and Mohd Abid, Mohd Asyadi Azam and Alqattan, Zakaria N. and Azam, Mohd Asyadi and Abdullah, Rosni and Jarrah, Hazim and Abu‑Khadrah, Ahmed Ismail (2020) A novel explanatory hybrid artificial bee colony algorithm for numerical function optimization. Journal of Supercomputing, 76 (12). pp. 9330-9354. ISSN 1573-0484

Text 2020 A NOVEL EXPLANATORY HYBRID ARTIFICIAL BEE COLONY ALGORITHM FOR NUMERICAL FUNCTION OPTIMIZATION.PDF Restricted to Registered users only Download (1MB)

Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Metaheuristic Optimization Algorithms, Swarm Intelligence Algorithms, Artifcial Bee Colony Algorithm, Β-Hill Climbing, Numerical Benchmark Function Optimization
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	13 May 2022 10:52
Last Modified:	13 May 2022 10:52
URI:	http://eprints.utem.edu.my/id/eprint/24751
Statistic Details:	View Download Statistic

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