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

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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 efficacy in solving various types of real-world optimization problems. However, it is impossible to find 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 different 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 different characteristics, and it was also evaluated and compared with well-known state-of-the-art algorithms. The evaluation process was investigated using different 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 significant.

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Abbreviations

AF:

Acceleration factor

N :

Population size

ν ij :

Neighbor solution

MCN:

Maximum cycle number

HC:

Hill climbing

CS:

Cuckoo search

BA:

Bat algorithm

f min :

Minimum frequency

bw:

Bandwidth parameter

λ :

Wavelength

MR:

Modification rate

\(\varPhi_{ij}\) :

Solution

D :

Solution dimension

GA:

Genetic algorithm

DE:

Differential evolution

HS:

Harmony search

PSO:

Particle swarm optimization

NI:

Number of iterations in βHC

A 0 :

Loudness

\(r_{i}^{0}\) :

Pulse emission rate

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Authors and Affiliations

  1. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

    Muath Ibrahim Jarrah & A. S. M. Jaya

  2. National Advance IPv6 Center, School of Computer Sciences, Universiti Sains Malaysia, 11800, Gelugor, Penang, Malaysia

    Zakaria N. Alqattan & Rosni Abdullah

  3. Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

    Mohd Asyadi Azam

  4. Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland, New Zealand

    Hazim Jarrah

  5. College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia

    Ahmed Ismail Abu-Khadrah

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  1. Muath Ibrahim Jarrah
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Correspondence to Muath Ibrahim Jarrah.

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Jarrah, M.I., Jaya, A.S.M., Alqattan, Z.N. et al. A novel explanatory hybrid artificial bee colony algorithm for numerical function optimization. J Supercomput 76, 9330–9354 (2020). https://doi.org/10.1007/s11227-019-03083-2

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