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

Kód výsledku v 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>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2021 IEEE Congress on Evolutionary Computation (CEC)

ISBN

978-1-7281-8393-0

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

453-460

Název nakladatele

IEEE

Místo vydání

Piscataway, NJ, USA

Místo konání akce

Krakow, Poland

Datum konání akce

28. 6. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—