Comparison of Nature-Inspired Population-Based Algorithms on Continuous Optimisation Problems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F19%3AA2001T2N" target="_blank" >RIV/61988987:17310/19:A2001T2N - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2210650218301536" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2210650218301536</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.swevo.2019.01.006" target="_blank" >10.1016/j.swevo.2019.01.006</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of Nature-Inspired Population-Based Algorithms on Continuous Optimisation Problems

Popis výsledku v původním jazyce

Nine nature-inspired algorithms are compared with four advanced adaptive differential evolution (DE) variants, the classic DE and the blind randomsearch on two benchmark sets. One of the benchmark sets is the CEC 2011 collection of 22 real-world optimization problems, the latter is the suite of30 artificial optimization problems defined for the competition of the algorithms within CEC 2014. The results of the experiments demonstrate the superiority of the adaptive DE variants both on the real-world problems and the artificial CEC 2014 test suite at all the levels of dimension (10, 30, and50). Some of the nature-inspired algorithms perform even worse than the blind random search. The efficiency of the classic DE is comparable with the better performing nature-inspired methods. The results entitle to form a recommendation for practitioners: Do not propose a new original algorithm but select from the optimization algorithms supported by thorough researchand good ranking at international competitions of optimization algorithms.

Název v anglickém jazyce

Comparison of Nature-Inspired Population-Based Algorithms on Continuous Optimisation Problems

Popis výsledku anglicky

Nine nature-inspired algorithms are compared with four advanced adaptive differential evolution (DE) variants, the classic DE and the blind randomsearch on two benchmark sets. One of the benchmark sets is the CEC 2011 collection of 22 real-world optimization problems, the latter is the suite of30 artificial optimization problems defined for the competition of the algorithms within CEC 2014. The results of the experiments demonstrate the superiority of the adaptive DE variants both on the real-world problems and the artificial CEC 2014 test suite at all the levels of dimension (10, 30, and50). Some of the nature-inspired algorithms perform even worse than the blind random search. The efficiency of the classic DE is comparable with the better performing nature-inspired methods. The results entitle to form a recommendation for practitioners: Do not propose a new original algorithm but select from the optimization algorithms supported by thorough researchand good ranking at international competitions of optimization algorithms.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

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

50

Číslo periodika v rámci svazku

NOV2019

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

10

Strana od-do

—

Kód UT WoS článku

000497252300028

EID výsledku v databázi Scopus

2-s2.0-85061284824