Differential Evolution with Distance-based Mutation-selection Applied to CEC 2021 Single Objective Numerical Optimisation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F21%3AA2202A06" target="_blank" >RIV/61988987:17310/21:A2202A06 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Differential Evolution with Distance-based Mutation-selection Applied to CEC 2021 Single Objective Numerical Optimisation
Original language description
A Differential Evolution (DE) algorithm with distance-based mutation-selection, population size reduction, and an optional external archive (DEDMNA) is proposed and tested on the CEC 2021 benchmark suite. The three well-known mutation variants are chosen in combination with one crossover for this model. The distances of three newly generated positions are computed to select the most proper position to evaluate. In the proposed algorithm, an efficient linear population-size reduction mechanism is applied. Moreover, an archive is employed to store older effective solutions. The provided results show that the proposed variant of DEDMNA is able to solve 64 out of 160 optimisation problems. Moreover, DEDMNA outperforms the efficient adaptive j2020 variant in 102 problems, and it is worse only in 15 problems out of 160. From the comparison of DEDMNA with five state-of-the-art DE algorithms, the superiority of DEDMNA is obvious.
Czech name
—
Czech description
—

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2021 IEEE Congress on Evolutionary Computation (CEC)
ISBN
978-1-7281-8393-0
ISSN
—
e-ISSN
—
Number of pages
8
Pages from-to
453-460
Publisher name
IEEE
Place of publication
Piscataway, NJ, USA
Event location
Krakow, Poland
Event date
Jun 28, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

Similar results(10)