Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80084
Title: Discrete and continuous optimization based on hierarchical artificial bee colony optimizer
Authors: Ma, L
Hu, K
Zhu, Y
Niu B 
Chen, H
He, M
Issue Date: 2014
Publisher: Hindawi Publishing Corporation
Source: Journal of applied mathematics, 2014, v. 2014, 402616, p. 1-20 How to cite?
Journal: Journal of applied mathematics 
Abstract: This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC), to tackle complex high-dimensional problems. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operator is applied to enhance the global search ability between species. Experiments are conducted on a set of 20 continuous and discrete benchmark problems. The experimental results demonstrate remarkable performance of the HABC algorithm when compared with other six evolutionary algorithms.
URI: http://hdl.handle.net/10397/80084
ISSN: 1110-757X
EISSN: 1687-0042
DOI: 10.1155/2014/402616
Rights: Copyright © 2014 Lianbo Ma et al. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Ma, L., Hu, K., Zhu, Y., Niu, B., Chen, H., & He, M. (2014). Discrete and continuous optimization based on hierarchical artificial bee colony optimizer. Journal of Applied Mathematics, 2014, 402616, 1-20 is available at https://dx.doi.org/10.1155/2014/402616
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