Self-organizing migrating algorithm using covariance matrix adaptation evolution strategy for dynamic constrained optimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10247739" target="_blank" >RIV/61989100:27240/21:10247739 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/21:10247739

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2210650221000973" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2210650221000973</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.swevo.2021.100936" target="_blank" >10.1016/j.swevo.2021.100936</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Self-organizing migrating algorithm using covariance matrix adaptation evolution strategy for dynamic constrained optimization

Popis výsledku v původním jazyce

The dynamic constrained optimization problems can be a challenge for the optimization algorithms. They must tackle global optimum detection, as well as the change of the environment. Recently, a novel test suite for dynamic constrained optimization was introduced. Furthermore, three well-performed evolutionary algorithms were compared based on it. The experimental results show that each algorithm performed best for a different type of optimization problem. The objective of our work was to develop an algorithm reflecting requirements arising from the novel test suite and regarding the results provided by the tested algorithms. In this work, we present a novel evolutionary algorithm for dynamic constrained optimization. The algorithm hybridizes the self-organizing migrating algorithm and the covariance matrix adaptation evolution strategy with constraints handling approach. To avoid premature convergence, the best solutions representing feasible regions do not affect the rest of the population. Two clustering methods, exclusion radius, and quantum particles are used to preserve population diversity. The performance is evaluated on the recently published test suite and compared to the three state-of-the-art algorithms. The presented algorithm outperformed these algorithms in most test cases, which indicates the efficiency of the utilized mechanisms. (C) 2021 The Author(s)

Název v anglickém jazyce

Self-organizing migrating algorithm using covariance matrix adaptation evolution strategy for dynamic constrained optimization

Popis výsledku anglicky

The dynamic constrained optimization problems can be a challenge for the optimization algorithms. They must tackle global optimum detection, as well as the change of the environment. Recently, a novel test suite for dynamic constrained optimization was introduced. Furthermore, three well-performed evolutionary algorithms were compared based on it. The experimental results show that each algorithm performed best for a different type of optimization problem. The objective of our work was to develop an algorithm reflecting requirements arising from the novel test suite and regarding the results provided by the tested algorithms. In this work, we present a novel evolutionary algorithm for dynamic constrained optimization. The algorithm hybridizes the self-organizing migrating algorithm and the covariance matrix adaptation evolution strategy with constraints handling approach. To avoid premature convergence, the best solutions representing feasible regions do not affect the rest of the population. Two clustering methods, exclusion radius, and quantum particles are used to preserve population diversity. The performance is evaluated on the recently published test suite and compared to the three state-of-the-art algorithms. The presented algorithm outperformed these algorithms in most test cases, which indicates the efficiency of the utilized mechanisms. (C) 2021 The Author(s)

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

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 periodika

Swarm and Evolutionary Computation

ISSN

2210-6502

e-ISSN

—

Svazek periodika

65

Číslo periodika v rámci svazku

65

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

18

Strana od-do

—

Kód UT WoS článku

000680430000017

EID výsledku v databázi Scopus

2-s2.0-85108854822