UM
Cross entropy method based hybridization of dynamic group optimization algorithm
Tang R.2; Fong S.2; Dey N.1; Wong R.K.3; Mohammed S.4
2017-10-01
Source PublicationEntropy
ISSN10994300
Volume19Issue:10
AbstractRecently, a new algorithm named dynamic group optimization (DGO) has been proposed, which lends itself strongly to exploration and exploitation. Although DGO has demonstrated its efficacy in comparison to other classical optimization algorithms, DGO has two computational drawbacks. The first one is related to the two mutation operators of DGO, where they may decrease the diversity of the population, limiting the search ability. The second one is the homogeneity of the updated population information which is selected only from the companions in the same group. It may result in premature convergence and deteriorate the mutation operators. In order to deal with these two problems in this paper, a new hybridized algorithm is proposed, which combines the dynamic group optimization algorithm with the cross entropy method. The cross entropy method takes advantage of sampling the problem space by generating candidate solutions using the distribution, then it updates the distribution based on the better candidate solution discovered. The cross entropy operator does not only enlarge the promising search area, but it also guarantees that the new solution is taken from all the surrounding useful information into consideration. The proposed algorithm is tested on 23 up-to-date benchmark functions; the experimental results verify that the proposed algorithm over the other contemporary population-based swarming algorithms is more effective and efficient.
KeywordDynamic group optimization algorithm Entropy-based Meta-heuristics
DOI10.3390/e19100533
URLView the original
Language英語
Fulltext Access
Citation statistics
Cited Times [WOS]:7   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Techno India College of Technology
2.Universidade de Macau
3.University of New South Wales (UNSW) Australia
4.Lakehead University
Recommended Citation
GB/T 7714
Tang R.,Fong S.,Dey N.,et al. Cross entropy method based hybridization of dynamic group optimization algorithm[J]. Entropy,2017,19(10).
APA Tang R.,Fong S.,Dey N.,Wong R.K.,&Mohammed S..(2017).Cross entropy method based hybridization of dynamic group optimization algorithm.Entropy,19(10).
MLA Tang R.,et al."Cross entropy method based hybridization of dynamic group optimization algorithm".Entropy 19.10(2017).
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Tang R.]'s Articles
[Fong S.]'s Articles
[Dey N.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Tang R.]'s Articles
[Fong S.]'s Articles
[Dey N.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Tang R.]'s Articles
[Fong S.]'s Articles
[Dey N.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.