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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

20201 - Electrical and electronic engineering

Project

<a href="/en/project/TE02000077" target="_blank" >TE02000077: Smart Regions - Buildings and Settlements Information Modelling, Technology and Infrastructure for Sustainable Development</a><br>

Continuities

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

Publication year

2018

Confidentiality

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

Article name in the collection

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

ISBN

9783319977737

ISSN

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e-ISSN

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Number of pages

14

Pages from-to

15-28

Publisher name

Springer

Place of publication

Basel

Event location

Lisabon

Event date

Sep 17, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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