A novel explanatory hybrid artificial bee colony algorithm for numerical function optimization
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A novel explanatory hybrid artificial bee colony algorithm for numerical function optimization Muath Ibrahim Jarrah, et al. [full author details at the end of the article]
© Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Metaheuristic optimization algorithms · Swarm intelligence algorithms · Artificial bee colony algorithm · β-Hill climbing · Numerical benchmark function optimization List of symbols AF Acceleration factor N Population size
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νij Neighbor solution MCN Maximum cycle number HC Hill climbing CS Cuckoo search BA Bat algorithm fmin Minimum frequency bw Bandwidth parameter λ Wavelength MR Modification rate 𝛷ij Solution D Solution dimension GA Genetic algorithm DE Differential evolution HS Harmony search PSO Particle swarm optimization NI Number of iterations in βHC A0 Loudness ri0 Pulse emission rate
1 Introduction In general, for any optimization problem, it is very difficult if not impossible to explore a huge number of solutions in the solution space to find the optimal solution. This is due to a limitation of computational tasks, such as time and
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