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Statements

Subject Item
n2:RIV%2F61989100%3A27240%2F13%3A86092931%21RIV15-MSM-27240___
rdf:type
skos:Concept n8:Vysledek
dcterms:description
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 optimum solution 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 box constraints and 7 shifted (non-traditional) functions taken from literature. Comparison of numerical results with traditional DE and opposition based DE (ODE) show that the proposed schemes considered by us for generating the random numbers significantly improves the performance of DE in terms of convergence rate and average CPU time. 2012 Elsevier Inc. All rights reserved. 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 optimum solution 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 box constraints and 7 shifted (non-traditional) functions taken from literature. Comparison of numerical results with traditional DE and opposition based DE (ODE) show that the proposed schemes considered by us for generating the random numbers significantly improves the performance of DE in terms of convergence rate and average CPU time. 2012 Elsevier Inc. All rights reserved.
dcterms:title
Unconventional initialization methods for differential evolution Unconventional initialization methods for differential evolution
skos:prefLabel
Unconventional initialization methods for differential evolution Unconventional initialization methods for differential evolution
skos:notation
RIV/61989100:27240/13:86092931!RIV15-MSM-27240___
n3:aktivita
n9:S
n3:aktivity
S
n3:cisloPeriodika
9
n3:dodaniDat
n6:2015
n3:domaciTvurceVysledku
Abraham Padath, Ajith
n3:druhVysledku
n11:J
n3:duvernostUdaju
n17:S
n3:entitaPredkladatele
n16:predkladatel
n3:idSjednocenehoVysledku
112559
n3:idVysledku
RIV/61989100:27240/13:86092931
n3:jazykVysledku
n14:eng
n3:klicovaSlova
Stochastic optimization; Random numbers; Quadratic interpolation; Nonlinear simplex method; Initial population; Differential evolution
n3:klicoveSlovo
n7:Quadratic%20interpolation n7:Random%20numbers n7:Stochastic%20optimization n7:Differential%20evolution n7:Initial%20population n7:Nonlinear%20simplex%20method
n3:kodStatuVydavatele
US - Spojené státy americké
n3:kontrolniKodProRIV
[9437C449BC33]
n3:nazevZdroje
APPLIED MATHEMATICS AND COMPUTATION
n3:obor
n5:IN
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
3
n3:rokUplatneniVysledku
n6:2013
n3:svazekPeriodika
219
n3:tvurceVysledku
Abraham Padath, Ajith Ali, M. Pant, M.
n3:wos
000312366700030
s:issn
0096-3003
s:numberOfPages
21
n12:doi
10.1016/j.amc.2012.10.053
n15:organizacniJednotka
27240