Self-organizing migrating algorithm with clustering-aided migration

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F20%3A63526752" target="_blank" >RIV/70883521:28140/20:63526752 - isvavai.cz</a>

Výsledek na webu

<a href="https://dl.acm.org/doi/10.1145/3377929.3398129" target="_blank" >https://dl.acm.org/doi/10.1145/3377929.3398129</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3377929.3398129" target="_blank" >10.1145/3377929.3398129</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Self-organizing migrating algorithm with clustering-aided migration

Popis výsledku v původním jazyce

This paper proposes a novel migration strategy for Self-organizing Migrating Algorithm (SOMA), which combines advantages of the explorative All-To-Random migration with new exploitation focused All-To-Cluster-Leaders strategy. The main goal of this novel innovation to SOMA is to deliver competitive results, not only on the latest CEC 2020 benchmark set on a single objective bound-constrained numerical optimization. The proposed algorithm variant was titled SOMA-CL, and it has manifested notable potential in such demanding challenges. The results of the proposed algorithm were compared and tested for statistical significance against two other SOMA variants. © 2020 ACM.

Název v anglickém jazyce

Self-organizing migrating algorithm with clustering-aided migration

Popis výsledku anglicky

This paper proposes a novel migration strategy for Self-organizing Migrating Algorithm (SOMA), which combines advantages of the explorative All-To-Random migration with new exploitation focused All-To-Cluster-Leaders strategy. The main goal of this novel innovation to SOMA is to deliver competitive results, not only on the latest CEC 2020 benchmark set on a single objective bound-constrained numerical optimization. The proposed algorithm variant was titled SOMA-CL, and it has manifested notable potential in such demanding challenges. The results of the proposed algorithm were compared and tested for statistical significance against two other SOMA variants. © 2020 ACM.

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

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

ISBN

978-145037127-8

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

1441-1447

Název nakladatele

Association for Computing Machinery

Místo vydání

New York

Místo konání akce

Cancun

Datum konání akce

8. 7. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—