A self-adaptive spherical search algorithm for real-world constrained optimization problems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10247258" target="_blank" >RIV/61989100:27240/20:10247258 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/20:10247258

Výsledek na webu

<a href="https://dl.acm.org/doi/pdf/10.1145/3377929.3398186" target="_blank" >https://dl.acm.org/doi/pdf/10.1145/3377929.3398186</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3377929.3398186" target="_blank" >10.1145/3377929.3398186</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A self-adaptive spherical search algorithm for real-world constrained optimization problems

Popis výsledku v původním jazyce

Determination of the global optimum of complex non-convex optimization problems of the real-world applications has remained a challenging task. Many researchers have been developing various types of effective direct search-based methods to tackle these problems. In this paper, we introduce a new variant of the recently developed Spherical Search (SS) algorithm, which contains a powerful and effective self-adaptation structure to enhance the performance. To analyze the performance, proposed algorithm is tested on the 57 test problems collected from different real-world applications. The obtained results statistically confirm the efficacy and efficiency of the proposed algorithm. (C) 2020 Owner/Author.

Název v anglickém jazyce

A self-adaptive spherical search algorithm for real-world constrained optimization problems

Popis výsledku anglicky

Determination of the global optimum of complex non-convex optimization problems of the real-world applications has remained a challenging task. Many researchers have been developing various types of effective direct search-based methods to tackle these problems. In this paper, we introduce a new variant of the recently developed Spherical Search (SS) algorithm, which contains a powerful and effective self-adaptation structure to enhance the performance. To analyze the performance, proposed algorithm is tested on the 57 test problems collected from different real-world applications. The obtained results statistically confirm the efficacy and efficiency of the proposed algorithm. (C) 2020 Owner/Author.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

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 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference

ISBN

978-1-4503-7128-5

ISSN

—

e-ISSN

—

Počet stran výsledku

2

Strana od-do

13-14

Název nakladatele

Association for Computing Machinery

Místo vydání

New York

Místo konání akce

Cancún

Datum konání akce

8. 7. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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