A hybridizing-enhanced differential evolution for optimization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F23%3A50020496" target="_blank" >RIV/62690094:18470/23:50020496 - isvavai.cz</a>
Result on the web
<a href="https://peerj.com/articles/cs-1420/" target="_blank" >https://peerj.com/articles/cs-1420/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.7717/peerj-cs.1420" target="_blank" >10.7717/peerj-cs.1420</a>
Alternative languages
Result language
angličtina
Original language name
A hybridizing-enhanced differential evolution for optimization
Original language description
Differential evolution (DE) belongs to the most usable optimization algorithms, presented in many improved and modern versions in recent years. Generally, the low convergence rate is the main drawback of the DE algorithm. In this article, the gray wolf optimizer (GWO) is used to accelerate the convergence rate and the final optimal results of the DE algorithm. The new resulting algorithm is called Hunting Differential Evolution (HDE). The proposed HDE algorithm deploys the convergence speed of the GWO algorithm as well as the appropriate searching capability of the DE algorithm. Furthermore, by adjusting the crossover rate and mutation probability parameters, this algorithm can be adjusted to pay closer attention to the strengths of each of these two algorithms. The HDE/current-to-rand/ 1 performed the best on CEC-2019 functions compared to the other eight variants of HDE. HDE/current-to-best/1 is also chosen as having superior performance to other proposed HDE compared to seven improved algorithms on CEC-2014 functions, outperforming them in 15 test functions. Furthermore, jHDE performs well by improving in 17 functions, compared with jDE on these functions. The simulations indicate that the proposed HDE algorithm can provide reliable outcomes in finding the optimal solutions with a rapid convergence rate and avoiding the local minimum compared to the original DE algorithm.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
PeerJ Computer Science
ISSN
2376-5992
e-ISSN
2376-5992
Volume of the periodical
9
Issue of the periodical within the volume
June 2023
Country of publishing house
GB - UNITED KINGDOM
Number of pages
36
Pages from-to
"Article number: e1420"
UT code for WoS article
001009615200001
EID of the result in the Scopus database
2-s2.0-85162139756