Dimension-Wise Particle Swarm Optimization: Evaluation and Comparative Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F21%3A50018182" target="_blank" >RIV/62690094:18470/21:50018182 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2076-3417/11/13/6201" target="_blank" >https://www.mdpi.com/2076-3417/11/13/6201</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app11136201" target="_blank" >10.3390/app11136201</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Dimension-Wise Particle Swarm Optimization: Evaluation and Comparative Analysis

Popis výsledku v původním jazyce

This article evaluates a recently introduced algorithm that adjusts each dimension in particle swarm optimization semi-independently and compares it with the traditional particle swarm optimization. In addition, the comparison is extended to differential evolution and genetic algorithm. This presented comparative study provides a clear exposition of the effects introduced by the proposed algorithm. Performance of all evaluated optimizers is evaluated based on how well they perform in finding the global minima of 24 multi-dimensional benchmark functions, each having 7, 14, or 21 dimensions. Each algorithm is put through a session of self-tuning with 100 iterations to ensure convergence of their respective optimization parameters. The results confirm that the new variant is a significant improvement over the traditional algorithm. It also obtained notably better results than differential evolution when applied to problems with high-dimensional spaces relative to the number of available particles.

Název v anglickém jazyce

Dimension-Wise Particle Swarm Optimization: Evaluation and Comparative Analysis

Popis výsledku anglicky

This article evaluates a recently introduced algorithm that adjusts each dimension in particle swarm optimization semi-independently and compares it with the traditional particle swarm optimization. In addition, the comparison is extended to differential evolution and genetic algorithm. This presented comparative study provides a clear exposition of the effects introduced by the proposed algorithm. Performance of all evaluated optimizers is evaluated based on how well they perform in finding the global minima of 24 multi-dimensional benchmark functions, each having 7, 14, or 21 dimensions. Each algorithm is put through a session of self-tuning with 100 iterations to ensure convergence of their respective optimization parameters. The results confirm that the new variant is a significant improvement over the traditional algorithm. It also obtained notably better results than differential evolution when applied to problems with high-dimensional spaces relative to the number of available particles.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

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

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

APPLIED SCIENCES-BASEL

ISSN

2076-3417

e-ISSN

—

Svazek periodika

11

Číslo periodika v rámci svazku

13

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

31

Strana od-do

"Article Number: 6201"

Kód UT WoS článku

000672290200001

EID výsledku v databázi Scopus

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