An efficient and robust artificial bee colony algorithm for numerical optimization

Xiang, Wan-li

doi:10.1016/j.cor.2012.12.006

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An efficient and robust artificial bee colony algorithm for numerical optimization

2013-05-01

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Xiang, Wan-li *

An, Mei-qing

DOI：10.1016/j.cor.2012.12.006

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Abstract

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Artificial bee colony (ABC) algorithm has already shown more effective than other population-based algorithms. However, ABC is good at exploration but poor at exploitation, which results in an issue on convergence performance in some cases. To improve the convergence performance of ABC, an efficient and robust artificial bee colony (ERABC) algorithm is proposed. In ERABC, a combinatorial solution search equation is introduced to accelerate the search process. And in order to avoid being trapped in local minima, chaotic search technique is employed on scout bee phase. Meanwhile, to reach a kind of sustainable evolutionary ability, reverse selection based on roulette wheel is applied to keep the population diversity. In addition, to enhance the global convergence, chaotic initialization is used to produce initial population. Finally, experimental results tested on 23 benchmark functions show that ERABC has a very good performance when compared with two ABC-based algorithms. (C) 2012 Elsevier Ltd. All rights reserved.

Keywords:

Artificial bee colony algorithm

Initialization based on chaos

Reverse selection based on roulette wheel

Solution search equation

Chaotic search

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