Solving Multi Objective Stochastic Programming Problems Using Differential Evolution

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Solving Multi Objective Stochastic Programming Problems Using Differential Evolution

Popis výsledku v původním jazyce

Stochastic (or probabilistic) programming is an optimization technique in which the constraints and/or the objective function of an optimization problem contains random variables. The mathematical models of these problems may follow any particular probability distribution for model coefficients. The objective here is to determine the proper values for model parameters influenced by random events. In this study, Differential Evolution (DE) and its two recent variants LDE1 and LDE2 are presented for solving multi objective linear stochastic programming (MOSLP) problems, having several conflicting objectives. The numerical results obtained by DE and its variants are compared with the available results from where it is observed that the DE and its variantssignificantly improve the quality of solution of the given considered problem in comparison with the quoted results in the literature.

Název v anglickém jazyce

Solving Multi Objective Stochastic Programming Problems Using Differential Evolution

Popis výsledku anglicky

Stochastic (or probabilistic) programming is an optimization technique in which the constraints and/or the objective function of an optimization problem contains random variables. The mathematical models of these problems may follow any particular probability distribution for model coefficients. The objective here is to determine the proper values for model parameters influenced by random events. In this study, Differential Evolution (DE) and its two recent variants LDE1 and LDE2 are presented for solving multi objective linear stochastic programming (MOSLP) problems, having several conflicting objectives. The numerical results obtained by DE and its variants are compared with the available results from where it is observed that the DE and its variantssignificantly improve the quality of solution of the given considered problem in comparison with the quoted results in the literature.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2010

Kód důvěrnosti údajů

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

Název statě ve sborníku

SWARM, EVOLUTIONARY, AND MEMETIC COMPUTING

ISBN

978-3-642-17562-6

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

8

Strana od-do

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Název nakladatele

Springer Heidelberg

Místo vydání

Berlín

Místo konání akce

Indie

Datum konání akce

16. 12. 2010

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000286284400007