Eigen Crossover in Cooperative Model of Evolutionary Algorithms Applied to CEC 2022 Single Objective Numerical Optimisation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F22%3AA2302GV8" target="_blank" >RIV/61988987:17310/22:A2302GV8 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9870433" target="_blank" >https://ieeexplore.ieee.org/document/9870433</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CEC55065.2022.9870433" target="_blank" >10.1109/CEC55065.2022.9870433</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Eigen Crossover in Cooperative Model of Evolutionary Algorithms Applied to CEC 2022 Single Objective Numerical Optimisation

Popis výsledku v původním jazyce

In this paper, a cooperative model of four well-performing evolutionary algorithms enhanced by Eigen crossover is proposed and applied to a set of problems CEC 2022. The four adaptive algorithms employed in this model are - Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), Differential Evolution with Covariance Matrix Learning and Bimodal Distribution Parameter Setting (CoBiDE), an adaptive variant of jSO, and Differential Evolution With an Individual-Dependent Mechanism (IDE). For the higher efficiency of the cooperative model, a linear population-size reduction mechanism is employed. The model was introduced for CEC 2019. Here, Eigen crossover is applied for each cooperating algorithm. The provided results show that the proposed model of four Evolutionary Algorithms with Eigen crossover (EA4eig) is able to solve ten out of 24 optimisation problems. Moreover, comparing EA4eig with four state-of-the-art variants of adaptive Differential Evolution illustrates the superiority of the newly designed optimiser.

Název v anglickém jazyce

Eigen Crossover in Cooperative Model of Evolutionary Algorithms Applied to CEC 2022 Single Objective Numerical Optimisation

Popis výsledku anglicky

In this paper, a cooperative model of four well-performing evolutionary algorithms enhanced by Eigen crossover is proposed and applied to a set of problems CEC 2022. The four adaptive algorithms employed in this model are - Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), Differential Evolution with Covariance Matrix Learning and Bimodal Distribution Parameter Setting (CoBiDE), an adaptive variant of jSO, and Differential Evolution With an Individual-Dependent Mechanism (IDE). For the higher efficiency of the cooperative model, a linear population-size reduction mechanism is employed. The model was introduced for CEC 2019. Here, Eigen crossover is applied for each cooperating algorithm. The provided results show that the proposed model of four Evolutionary Algorithms with Eigen crossover (EA4eig) is able to solve ten out of 24 optimisation problems. Moreover, comparing EA4eig with four state-of-the-art variants of adaptive Differential Evolution illustrates the superiority of the newly designed optimiser.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

2022 IEEE Congress on Evolutionary Computation (CEC)

ISBN

978-1-6654-6708-7

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

1-8

Název nakladatele

IEEE

Místo vydání

Piscataway, NJ, USA

Místo konání akce

Padua, Italy

Datum konání akce

18. 7. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000859282000214