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An improved artificial bee colony algorithm based on mean best-guided approach for continuous optimization problems and real brain MRI images segmentation

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dc.contributor.authorAlrosan, Ayaten_US
dc.contributor.authorAlomoush, Waleeden_US
dc.contributor.authorNorwawi, Noritaen_US
dc.contributor.authorAlswaitti, Mohammeden_US
dc.contributor.authorMakhadmeh, Sharif Naseren_US
dc.date.accessioned2024-05-29T03:25:46Z
dc.date.available2024-05-29T03:25:46Z
dc.date.issued2020
dc.description.abstractThe artificial bee colony (ABC) algorithm is a relatively new algorithm inspired by nature and has been shown to be efficient in contrast to other optimization algorithms. Nonetheless, ABC has some similar drawbacks to the optimization algorithms in terms of the unbalanced search behavior. The original ABC algorithm shows strong exploration capability with ineffective exploitation due to the unbalanced search model. In this paper, a new ABC algorithm called MeanABC is introduced to achieve the search behavior balance via a modified search equation based on the information of the mean of the previous best solutions. To evaluate the performance of the proposed algorithm, experiments were divided into two parts: First, the proposed algorithm was tested on a comprehensive set of 14 benchmark functions. The results show that the proposed MeanABC enhances the performance of the original ABC in terms of faster global convergence speed, solution quality, and better robustness when compared to other ABC variants. Secondly, the proposed algorithm was applied as a hybrid with the FCM algorithm as a segmentation technique to a set of 20 volumes of real brain MRI images with 20 images for each volume. All of these images have several characteristics, levels of difficulty, and cover different domains. The obtained results are promising, especially when the performance of the proposed algorithm was compared to other state-of-the-art segmentation techniques.en_US
dc.identifier.citationCite this article Alrosan, A., Alomoush, W., Norwawi, N. et al. An improved artificial bee colony algorithm based on mean best-guided approach for continuous optimization problems and real brain MRI images segmentation. Neural Comput & Applic 33, 1671–1697 (2021). https://doi.org/10.1007/s00521-020-05118-9en_US
dc.identifier.doihttps://doi.org/10.1007/s00521-020-05118-9
dc.identifier.issn0941-0643
dc.identifier.scopusWOS:000541031000004
dc.identifier.scopus2-s2.0-85086597481
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85086597481&doi=10.1007%2fs00521-020-05118-9&partnerID=40&md5=337b1830ea9029decbe3bb9e1934a484
dc.identifier.urihttps://link.springer.com/article/10.1007/s00521-020-05118-9#citeas
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/12024
dc.languageEnglish
dc.language.isoen_USen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.sourceScopus
dc.subjectOptimization algorithmsen_US
dc.subjectArtificial bee colonyen_US
dc.subjectContinuous optimization problemsen_US
dc.subjectSearch behavioren_US
dc.subjectMRI imagesen_US
dc.titleAn improved artificial bee colony algorithm based on mean best-guided approach for continuous optimization problems and real brain MRI images segmentationen_US
dc.typeArticleen_US
dspace.entity.typePublication

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