A simplex differential evolution algorithm: Development and applications

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F12%3A86092945" target="_blank" >RIV/61989100:27240/12:86092945 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1177/0142331211403032" target="_blank" >http://dx.doi.org/10.1177/0142331211403032</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1177/0142331211403032" target="_blank" >10.1177/0142331211403032</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A simplex differential evolution algorithm: Development and applications

Popis výsledku v původním jazyce

Population-based heuristic optimization methods like differential evolution (DE) depend largely on the generation of the initial population. The initial population not only affects the search for several iterations but often also has an influence on thefinal solution. The conventional method for generating the initial population is the use of computer-generated pseudo-random numbers, which may not be very effective. In the present study, we have investigated the potential of generating the initial population by integrating the non-linear simplex method of Nelder and Mead with pseudo-random numbers in a DE algorithm. The resulting algorithm named the non-linear simplex DE is tested on a set of 20 benchmark problems with box constraints and two real life problems. Numerical results show that the proposed scheme for generating the random numbers significantly improves the performance of DE in terms of fitness function value, convergence rate and average CPU time. The Author(s) 2011.

Název v anglickém jazyce

A simplex differential evolution algorithm: Development and applications

Popis výsledku anglicky

Population-based heuristic optimization methods like differential evolution (DE) depend largely on the generation of the initial population. The initial population not only affects the search for several iterations but often also has an influence on thefinal solution. The conventional method for generating the initial population is the use of computer-generated pseudo-random numbers, which may not be very effective. In the present study, we have investigated the potential of generating the initial population by integrating the non-linear simplex method of Nelder and Mead with pseudo-random numbers in a DE algorithm. The resulting algorithm named the non-linear simplex DE is tested on a set of 20 benchmark problems with box constraints and two real life problems. Numerical results show that the proposed scheme for generating the random numbers significantly improves the performance of DE in terms of fitness function value, convergence rate and average CPU time. The Author(s) 2011.

Druh

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

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

Transactions of the Institute of Measurement and Control

ISSN

0142-3312

e-ISSN

—

Svazek periodika

34

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

14

Strana od-do

691-704

Kód UT WoS článku

000306556100004

EID výsledku v databázi Scopus

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