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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • 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