Unconventional initialization methods for differential evolution

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F13%3A86092931" target="_blank" >RIV/61989100:27240/13:86092931 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.amc.2012.10.053" target="_blank" >http://dx.doi.org/10.1016/j.amc.2012.10.053</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.amc.2012.10.053" target="_blank" >10.1016/j.amc.2012.10.053</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Unconventional initialization methods for differential evolution

Popis výsledku v původním jazyce

The crucial role played by the initial population in a population-based heuristic optimization cannot be neglected. It not only affects the search for several iterations but often also has an influence on the final solution. If the initial population itself has some knowledge about the potential regions of the search domain then it is quite likely to accelerate the rate of convergence of the optimization algorithm. In the present study we propose two schemes for generating the initial population of differential evolution (DE) algorithm. These schemes are based on quadratic interpolation (QI) and nonlinear simplex method (NSM) in conjugation with computer generated random numbers. The idea is to construct a population that is biased towards the optimumsolution right from the very beginning of the algorithm. The corresponding algorithms named as QIDE (using quadratic interpolation) and NSDE (using non linear simplex method), are tested on a set of 20 traditional benchmark problems with

Název v anglickém jazyce

Unconventional initialization methods for differential evolution

Popis výsledku anglicky

The crucial role played by the initial population in a population-based heuristic optimization cannot be neglected. It not only affects the search for several iterations but often also has an influence on the final solution. If the initial population itself has some knowledge about the potential regions of the search domain then it is quite likely to accelerate the rate of convergence of the optimization algorithm. In the present study we propose two schemes for generating the initial population of differential evolution (DE) algorithm. These schemes are based on quadratic interpolation (QI) and nonlinear simplex method (NSM) in conjugation with computer generated random numbers. The idea is to construct a population that is biased towards the optimumsolution right from the very beginning of the algorithm. The corresponding algorithms named as QIDE (using quadratic interpolation) and NSDE (using non linear simplex method), are tested on a set of 20 traditional benchmark problems with

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

APPLIED MATHEMATICS AND COMPUTATION

ISSN

0096-3003

e-ISSN

—

Svazek periodika

219

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

21

Strana od-do

4474-4494

Kód UT WoS článku

000312366700030

EID výsledku v databázi Scopus

—