Alternatives to Evolutionary Optimization Algorithms in the Context of Traditional Stochastic Optimization Methods in Smart Area Technical Equipment Applications

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F18%3A00323244" target="_blank" >RIV/68407700:21110/18:00323244 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-97773-7_2" target="_blank" >http://dx.doi.org/10.1007/978-3-319-97773-7_2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-97773-7_2" target="_blank" >10.1007/978-3-319-97773-7_2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Alternatives to Evolutionary Optimization Algorithms in the Context of Traditional Stochastic Optimization Methods in Smart Area Technical Equipment Applications

Popis výsledku v původním jazyce

The use of evolutionary computational techniques has become widespread in many technical disciplines including, but not limited, neural networks and evolutionary algorithms. From these techniques, in the field of global optimization, mainly the evolutionary optimization algorithms are used, especially one of their types – genetic algorithms. From the mathematical point of view, the evolutionary and genetic algorithms are just another representatives of stochastic optimization algorithms. The aim of our research was to describe the basic properties of stochastic algorithms including genetic algorithms, to select suitable candidates from the class of traditional stochastic algorithms and to compare their behaviour with the genetic algorithms. In this paper, we are going to address so-called technical optimization, where we do not know the optimized function directly, but we are able to get the value of an optimized function at any point (for example by measuring a certain quantity). The stochastic optimization algorithms provide the advantage of efficient working even with such functions. An important criterion for optimization is also the ability to parallelize a task. The optimization algorithms can be implemented as a parallel system – we calculate the value of a purpose function at several points at the same time. The paper will also describe the specific described implementation and testing of selected algorithms on analytical functions as well as functions mediated by artificial neural networks, which have been learned on practice data. Furthermore, the algorithm implementation for different environments and their routine user-friendly practical applications are described.

Název v anglickém jazyce

Alternatives to Evolutionary Optimization Algorithms in the Context of Traditional Stochastic Optimization Methods in Smart Area Technical Equipment Applications

Popis výsledku anglicky

The use of evolutionary computational techniques has become widespread in many technical disciplines including, but not limited, neural networks and evolutionary algorithms. From these techniques, in the field of global optimization, mainly the evolutionary optimization algorithms are used, especially one of their types – genetic algorithms. From the mathematical point of view, the evolutionary and genetic algorithms are just another representatives of stochastic optimization algorithms. The aim of our research was to describe the basic properties of stochastic algorithms including genetic algorithms, to select suitable candidates from the class of traditional stochastic algorithms and to compare their behaviour with the genetic algorithms. In this paper, we are going to address so-called technical optimization, where we do not know the optimized function directly, but we are able to get the value of an optimized function at any point (for example by measuring a certain quantity). The stochastic optimization algorithms provide the advantage of efficient working even with such functions. An important criterion for optimization is also the ability to parallelize a task. The optimization algorithms can be implemented as a parallel system – we calculate the value of a purpose function at several points at the same time. The paper will also describe the specific described implementation and testing of selected algorithms on analytical functions as well as functions mediated by artificial neural networks, which have been learned on practice data. Furthermore, the algorithm implementation for different environments and their routine user-friendly practical applications are described.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/TE02000077" target="_blank" >TE02000077: Inteligentní Regiony - Informační modelování budov a sídel, technologie a infrastruktura pro udržitelný rozvoj</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization

ISBN

9783319977737

ISSN

—

e-ISSN

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Počet stran výsledku

14

Strana od-do

15-28

Název nakladatele

Springer

Místo vydání

Basel

Místo konání akce

Lisabon

Datum konání akce

17. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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