Novelty Search in Particle Swarm Optimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F21%3A63544778" target="_blank" >RIV/70883521:28140/21:63544778 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9660131" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9660131</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SSCI50451.2021.9660131" target="_blank" >10.1109/SSCI50451.2021.9660131</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Novelty Search in Particle Swarm Optimization

Popis výsledku v původním jazyce

This paper presents a novel approach to implementing the Novelty search technique (introduced by Kenneth O. Stanley) into the Particle Swarm optimization algorithm (PSO). PSO is well-known for its impaired ability to operate in multidimensional spaces due to its inclination towards premature convergence and possible stagnation. This presented research aims to try various implementations of Novelty Search that could remove this inability and enhance the PSO algorithm. In total, we present five different modifications. The CEC 2020 single objective bound-constrained optimization benchmark testbed was used to evaluate the different Novelty Search-based modifications of the algorithm. All results were compared and tested for statistical significance against the original variant of PSO using the Friedman rank test. This work aims to increase understanding of implementing new approaches for population dynamics control, which are not driven purely by a gradient, and inspire other researchers working with different evolutionary computation methods.

Název v anglickém jazyce

Novelty Search in Particle Swarm Optimization

Popis výsledku anglicky

This paper presents a novel approach to implementing the Novelty search technique (introduced by Kenneth O. Stanley) into the Particle Swarm optimization algorithm (PSO). PSO is well-known for its impaired ability to operate in multidimensional spaces due to its inclination towards premature convergence and possible stagnation. This presented research aims to try various implementations of Novelty Search that could remove this inability and enhance the PSO algorithm. In total, we present five different modifications. The CEC 2020 single objective bound-constrained optimization benchmark testbed was used to evaluate the different Novelty Search-based modifications of the algorithm. All results were compared and tested for statistical significance against the original variant of PSO using the Friedman rank test. This work aims to increase understanding of implementing new approaches for population dynamics control, which are not driven purely by a gradient, and inspire other researchers working with different evolutionary computation methods.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

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 Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings

ISBN

978-172819048-8

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

—

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

Piscataway, New Jersey

Místo konání akce

Orlando

Datum konání akce

5. 12. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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