Modified Genetic Algorithm with Sorting Process for Wireless Sensor Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10246984" target="_blank" >RIV/61989100:27240/20:10246984 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-981-15-0324-5_33" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-981-15-0324-5_33</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-15-0324-5_33" target="_blank" >10.1007/978-981-15-0324-5_33</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Modified Genetic Algorithm with Sorting Process for Wireless Sensor Network

Popis výsledku v původním jazyce

The genetic algorithm is widely used in optimization problems, in which, a population of candidate solutions is mutated and altered toward better solutions. Usually, genetic algorithm works in optimization problem with a fitness function which is used to evaluate the feasibility and quality of a solution. However, sometimes, it is hard to define the fitness function when there are several optimization objectives, especially only one solution can be selected from a population. In this paper, we modified genetic algorithms with a novel-sorting process to solve the above problem. Two algorithms, the classic genetic algorithm and newly proposed recently M-Genetic algorithm, are simulated and altered by embedding the novel-sorting process. Besides, both the algorithms and their alteration versions are applied into wireless sensor network for locating Relay nodes. The sensor node loss and package loss number are reduced in genetic algorithms with our sorting process compared to the original ones. (C) 2020, Springer Nature Singapore Pte Ltd.

Název v anglickém jazyce

Modified Genetic Algorithm with Sorting Process for Wireless Sensor Network

Popis výsledku anglicky

The genetic algorithm is widely used in optimization problems, in which, a population of candidate solutions is mutated and altered toward better solutions. Usually, genetic algorithm works in optimization problem with a fitness function which is used to evaluate the feasibility and quality of a solution. However, sometimes, it is hard to define the fitness function when there are several optimization objectives, especially only one solution can be selected from a population. In this paper, we modified genetic algorithms with a novel-sorting process to solve the above problem. Two algorithms, the classic genetic algorithm and newly proposed recently M-Genetic algorithm, are simulated and altered by embedding the novel-sorting process. Besides, both the algorithms and their alteration versions are applied into wireless sensor network for locating Relay nodes. The sensor node loss and package loss number are reduced in genetic algorithms with our sorting process compared to the original ones. (C) 2020, Springer Nature Singapore Pte Ltd.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_027%2F0008463" target="_blank" >EF16_027/0008463: Věda bez hranic</a><br>

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 1059

ISBN

978-981-15-0323-8

ISSN

2194-5357

e-ISSN

2194-5365

Počet stran výsledku

8

Strana od-do

381-388

Název nakladatele

Springer

Místo vydání

Singapur

Místo konání akce

Ostrava

Datum konání akce

21. 3. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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